Development Digital Insights

In web development, selecting the right platform is key. Webflow and WordPress each offer unique advantages tailored to different needs. This article compares these platforms to help you make an informed decision for your project.

Webflow

Webflow is an innovative platform that allows designers and developers to create impressive and responsive websites without the need for extensive coding knowledge. Its intuitive drag-and-drop interface offers a refreshing approach to web design, allowing users to visually craft layouts and interactions.

Advantages and Key Features of Webflow

Webflow is designed to empower designers and developers with its comprehensive suite of tools. From crafting visually stunning layouts to managing content and e-commerce effortlessly, Webflow simplifies every aspect of web creation:

• Visual Design Capabilities: Offers a user-friendly interface for creating pixel-perfect layouts and custom styling without extensive coding.
• Responsive and Interactive Design: Creates designs that automatically adjust to different devices and allow adding customized interactive elements and animations.
• Integrated CMS and E-commerce: Offers comprehensive tools for managing content and building online stores, supporting dynamic, content-rich websites.
• Clean Code Generation: Produces clean, efficient HTML, CSS, and JavaScript, enhancing site performance and SEO.
• Simplified Hosting and Publishing: Makes website deployment quick and efficient, directly from the platform.

Drawbacks of Webflow

While Webflow is a powerful tool for website design, it comes with certain limitations that might affect its suitability for some projects:

• Pricing: Webflow may be costlier than alternatives like WordPress, especially for hosting and advanced features, which could strain the budgets of smaller projects or independent creators.
• Learning Curve: Despite its user-friendly design interface, Webflow can be challenging to master for those new to design principles or who need to use its more advanced functionalities.
• Limited Extensions: Webflow has fewer third-party integrations and plugins compared to WordPress, potentially restricting its ability to meet specific customization needs or add unique functionalities.
• Scalability: While suitable for medium-sized projects, Webflow may not offer the same level of flexibility for hosting or scaling up large websites as some other platforms.

Webflow Websites Examples

Webflow's powerful design tools are used by a diverse range of businesses to improve their online presence and make digital interactions more efficient. Here are some examples of big companies from different areas using the power of Webflow:

• Dell Design System - An online resource for designers and developers, providing Dell-branded design assets, patterns, and guidelines to ensure consistent and high-quality user experiences across Dell's products.
• DropboxSign - An electronic signature platform that offers intuitive signing, sending, and managing documents online. Integrates with tools like Google Drive and Salesforce, maintaining high security and compliance standards.
• Michael Kors - A luxury fashion brand utilizing Webflow for the presentation of their seasonal collection. Features include Runway videos, a detailed Lookbook, and a direct link to their e-commerce site.

WordPress

WordPress is a long-standing leader in the web development landscape. It offers a dynamic ecosystem of themes, plugins, and customization options, addressing a wide range of website requirements, spanning from blogs to corporate portals.

Advantages and Key Features of WordPress:

WordPress stands out for its flexibility and wide range of functionalities that cater to diverse website requirements. Here we highlight the platform’s main features that enable users to effectively tailor, manage, and enhance their websites:

• Flexibility: WordPress's extensive library of themes and plugins provides incredible flexibility, enabling users to tailor their websites to specific needs.
• Content Management: WordPress excels as a content management system, making it ideal for bloggers and content creators.
• E-commerce: With the WooCommerce plugin, WordPress can handle e-commerce needs effectively, and its vast plugin directory allows for various functionalities.
• SEO Optimization: WordPress offers plugins and features that facilitate search engine optimization, helping websites rank better in search results.
• Large Community: WordPress boasts a massive community of developers, designers, and users, ensuring ongoing support, updates, and resources.

Drawbacks of WordPress

While WordPress is a flexible and powerful platform, managing it effectively comes with certain challenges. These drawbacks can affect the efficiency and security of websites, especially if not properly addressed:

• Security Issues: WordPress sites can be vulnerable to security threats if not properly maintained, especially due to outdated plugins or themes.
• Performance: Without proper optimization, WordPress sites can suffer from slow loading times, particularly as the number of plugins and customizations increases.
• Maintenance: Keeping a WordPress site updated with all the latest plugin and theme updates can be time-consuming and requires regular attention.
• Plugin Overload: While the vast number of available plugins allows for extensive customization, relying too heavily on them can lead to compatibility issues, potential security vulnerabilities, and a decrease in site performance.

WordPress Websites Examples

WordPress empowers a wide variety of businesses and organizations to manage their online content effectively. Here are some standout examples of how leading organizations use WordPress to manage and enhance their digital presence:

• TechCrunch is a leading technology news site that uses WordPress to manage and publish extensive content efficiently.
• Sony Music represents how major corporations use WordPress for content-heavy websites with a need for robust content management systems.
• The TED Blog uses WordPress to manage its rich content, including articles and TED Talk summaries, effectively integrating multimedia elements to engage a global audience.

Summary

In conclusion, when considering Webflow or WordPress, your choice depends on your specific needs. If you're a designer seeking a seamless blend of creativity and functionality, Webflow stands out. Its responsive capabilities, dynamic interactions, clean code generation, and user-friendly hosting make it a prime choice for creating websites that captivate and engage visitors. While WordPress's content management system versatility and vast ecosystem are valuable, Webflow's focus on elevating the design process and simplifying development workflow make it the preferred platform for those aiming to craft visually stunning and interactive websites without diving deeply into coding intricacies. Whether you're an experienced designer or a novice, Webflow empowers you to bring your web design visions to life.

As the practice shows, no idea, even the most brilliant one, when embodied in a digital product or service, will become successful without quality assurance. It, in turn, always includes two non-intersecting procedures that occur before software is launched: the first of them is aimed at obtaining a clear understanding of whether development is moving in the right direction, and the second of them clarifies whether the solution being created meets end user needs. So, how are they called and performed? Let’s find it out right now.

What Is Verification Testing?

In a nutshell, it is a procedure aimed at checking whether the solution being created meets its predefined specifications and generally accepted design and development standards. Software verification testing is performed throughout the entire creation process and involves the use of various automation testing tools and verification methods, such as modular, integrational, and system testing.

What Is Validation Testing?

Unlike the previous type of testing, this one involves planning real-world scenarios that allow the development team to make sure that the solution being created meets its audience’s needs (for this, you can apply, for example, the alpha- and beta-testing validation methods). After that, the solution has to be compared with its specifications. If any discrepancies are found, they need to be eliminated before this solution is released to the public. Actually, that’s why validation testing is repeated until the team achieves full compliance.

Purpose of Verification and Validation

The main goals of verification and validation testing are to fully satisfy the needs of the software solution owner and the audience as well. As soon as these procedures are successfully completed, the project can be launched.

Difference Between Verification vs Validation Testing

So, what about the verification and validation difference? Generally speaking, while verification is aimed at checking the compliance of a product or service with its list of requirements agreed upon between the development team and the owner, validation allows teams to make sure that this service or product will be positively accepted by its end users. 

In this regard, the first procedure starts from the very beginning of the development process and lasts until the final testing stage and the second – only after the product/service is completed. This also imposes certain differences on the test data used: during verification in software testing, QA engineers use sample data (in this regard, automation methods are often applied), and during the software validation – real data (usually, manual methods do a great job here).

How Are Verification and Validation Testing Performed?

Now, let's look at the four most popular approaches used for validation and verification in software testing:

• Peer reviews, which involve providing a software solution to focus group representatives for a certain period of time in order to obtain feedback on their experience of interacting with it;
• Assessments, which are left by focus group representatives after checking the solution for functionality and identifying errors and inconsistencies in it;
• Walkthrough, which implies a preliminary explanation to focus group representatives of how exactly to interact with the software solution, after which the latter leave their feedback on how well everything was clear and whether they think some areas need to be optimized;
• Desktop-checking, which includes reviewing the software code by specialists (in particular, software engineers).

Tips for Successful Verification and Validation

To ensure these two procedures go smoothly, follow these guidelines:

1. Define the tasks and goals you are pursuing through these procedures and what the results should be upon their completion;
2. Choose the specific tools and approaches you are going to use – they should cover all possible scenarios of user interaction with your software solution, as well as generally accepted software development standards;
3. Create a testing plan, where you will specify deadlines, available resources, and evaluation criteria (along with the acceptable minimum compliance);
4. Proceed directly to testing in order to identify bugs and inconsistencies that will need to be fixed before the project is released;
5. Involve representatives of the potential target audience and stakeholders in the testing procedure to get the most objective assessment of the quality of your team's work;
6. Don't forget to document and monitor the testing results from one bug fixing to another to understand the dynamics of improvements.

Popular Test Automation Tools For Verification and Validation

Although automated testing is considered a standard approach for verification testing only, sometimes, it also brings benefits when implementing validation testing. In this regard, we have compiled a small selection of popular tools that you can use for quality control in software:

• Selenium, an open-source web solution that supports a lot of programming languages, such as Java, Python, C#, and so on, as well as integrates with different frameworks;
• Appium, an open-source service for automated testing of mobile apps on iOS, Android, and Windows (both native, cross-platform, and hybrid ones);
• Robot Framework, an automation framework aiming at implementing acceptance testing and acceptance test-driven development;
• Jenkins, an automation server used for continuous integration and continuous delivery that streamlines the build, test, and deployment stages;
• TestNG, a popular testing framework that is actively used for testing Java-based software solutions;
• Cucumber, an environment for running automated acceptance tests written in Java, Ruby, etc.;
• JMeter, a perfect solution for performance testing of web apps.

Conclusion

We hope that we have helped you understand the difference between validation and verification testing, and now, you can also assess their importance in creating high-quality and competitive software solutions. You can also check out our blog to learn more about software development and design.

Nowadays, AI technology is significantly changing the different industries and the healthcare industry isn’t an exception. One of the additions – chatbots – has the potential to save time and money.

But should the healthcare industry use a bot service, it is a question many medical workers ask. However, we can define healthcare as a service that happens when patients visit the doctors, we will strictly limit our potential productivity gains. Chatbots can help healthcare providers with customer service and interact with healthcare consumers through text conversationally. So it is one of the most effective solutions to increase patient satisfaction.

According to surveys and researches published by IBM, up to 80% of routine questions could be answered by a chatbot. And it isn’t a surprise that the big part of these questions are asked by the patients and a simple chatbot can help with that.

For being in a win-win situation in healthcare, the providers should think about how to give patients, prospective patients, and other site visitors a way to answer questions, find the important information, and even book appointments as soon as possible without calling in. It’s easier and more convenient for people. It’s more efficient and successful for providers. And it can save a tremendous amount of money and time. That’s the dream, right? In the healthcare sector, the patient’s experience and satisfaction continue to be the top priority.

Chatbots in healthcare can offer the best health care service.

So the real question for clinics, hospitals and other private practices looking to improve the patient experience: Is it worth it?

Will chatbots really help and make a difference for your staff and patients?

At the end of the article you will find the answers to all your questions, so let’s have a look at how healthcare chatbots actually work, their benefits, and potential use cases.

We can divide chatbots into three categories:

Rules-based: Rule-based chatbots can also be named decision-tree bots. This type of chatbot uses a series of defined rules. These rules are the basis for the types of problems the chatbot is familiar with and can deliver solutions for. Usually, there are very simple or complicated rules. They can’t, however, answer any questions outside of the defined rules. These chatbots do not learn through interactions. They only perform and work with the scenarios you train them for. They follow pre-defined, if-this-then-that “playbooks” that automatically respond to a list of pre-populated queries and questions. These chatbots might answer common questions with simple and canned responses. They might also lead people to dead-ends like “I’m having trouble understanding” due to their limited technical capacity.

Adaptive: Using machine learning (ML), adaptive chatbots can learn patterns the more requests they receive.

AI-powered: An AI-powered chatbot is a smarter version that uses natural language processing (NLP) and ML to better understand the intent of the human and provide a more natural, near-human-level communication. An AI chatbot can quickly determine intent and context, move through more complex and nuanced conversations, and deliver the look and feel of chatting with an actual human being.

Chatbots are created to mimic human conversation. Using chatbots, patients can ask health-related questions, check on their appointments, or explore treatment options without calling in.

Why chatbots are perfectly suitable for healthcare

The chatbot is useful for any type of business. But for the healthcare field, they are especially valuable, because chatbots can provide the required information and actions to patients.

24/7 Service

Anytime chatbots can provide patients service and support. According to this, patients can get immediate engagement and answers.

Follow-up medical care

Quality and results are the most important goals for the patient experience. In many cases, it is difficult for doctors to provide quality care once a patient leaves the hospital. Chatbots could be a good solution for this challenge, and they can give follow-up care by prompting patients to record their symptoms or track their progress.

At-home care

Chatbots can be used anywhere, especially at home. The patient can use the range of actions that include not only looking up symptoms and scheduling appointments, it could also use chatbots for the patients after they are discharged from the hospital. The chatbot could help patients take their medications in time and connect with their doctors.

Keep patient update

This is a new way to address any patient. This will keep the patient and family to remain updated till the last moment. Do you need to go to the appointment? The chatbot will remind you via text message or email. Will the surgery for which you need to be prepared? A chatbot can help patients prepare for any size of surgery or operation beforehand.

Better Patient Experiences

In the end, chatbots no doubt can enhance patient satisfaction. They help improve patient satisfaction scores by providing the convenience of care and reducing wait time. The providers have the opportunity to offer services to the patients and make it convenient for patients to receive medical attention. Chatbots can also facilitate surveys and other patient feedback mechanisms to record and track sentiment.

Use cases in the healthcare industry

• Let’s imagine the patient needs urgent help and information on what to do? The chatbot could quickly quiz users on the health problem they’re facing and recommend a sequence of actions until the ambulance arrives.

• It can be used as a mass email by local or national health organizations. Nowadays, it is especially relevant to people to get to know important details about vaccinations or epidemics.

• It can help patients track their moods or feelings that can be discussed with the psychiatrist or therapist at the next appointment. It could offer tips, notifications and advice between therapy sessions. Furthermore, it even can act more like a personal assistant. It can remind patients to take their medication, help them stay motivated to stick to their schedules and provide other relevant medical information.

• The hospitals and clinics can use the chatbot to allow patients to learn more about their health by asking questions, check symptoms or conditions, and find relevant specialists if they need them.

• The E-commerce industry that sells medical equipment or supplies could build a bot to recommend products to undecided customers. It is one of the ways to increase the visitors on the websites, directing the users to buy the products from it.

• A doctor could set up a chatbot to let users schedule their next appointment easily.

• The bot can be built to help users find in-network providers near them based on city or zip code.

• A medical spa can increase lead generation and user satisfaction using the bot. It is a great idea to ask what users expressly request. Based on the feedback from the users, and add only those services and products which your users have expressly requested. Finally, it could help the best matches schedule a consultation.

Final Thoughts

The chatbots can significantly replace the great part of live chatting with medical personnel. The main advantage that chatbot can provide is improvement in the patient experience. These chatbots are created to be conversational, so they are more responsive and personal than search engines. Chatbots can pull more specific and relevant information for patients rather than search engines. But they can not completely replace a human medical professional’s expertise, but they can reduce the time and money and make it easier for hospital staff to perform their jobs.

Developing a web or mobile app gives a business great opportunities and provides benefits in the form of additional dividends and audience loyalty. Having a clear understanding of the end goals, it is essential to define the main functionality of the future application to achieve the business goals and effectively perform setting tasks.

How to identify core functionalities for your web/mobile application?

Before developing an application, businesses must understand what features should be included in the finished product. The efficiency of the application itself and the speed of achieving the set KPIs depend on this. What may work for one company does not resonate with the business goals of other companies. In this list, we cover what you should consider when compiling a feature list:

1. Define your business goals 

What goals do you want to achieve with the app? Make a clear list of the problems your app will solve, what target audience will use it, and what preliminary results you expect.

2. Research the market and competitors

Analyze the market in your niche to understand what functionality your competitors already offer. Research and analyze their applications to understand what advantages they offer and disadvantages their products have. That will help you determine relevant features for your app to stand out in the market.

3. Make a preliminary list of features

Before development, list those features that will help you achieve your goals. Divide them into core and optional features. In the core features, list the ones without which your app won't be able to function efficiently and solve the users' problem. In the optional ones, list the ones which are not essential to the basic functionality but can add value and competitive advantage.

4. Prioritize features

Determine which features in the application are most important to achieve your initial goal. Prioritize and determine which features can be added later in later app versions. That will help you focus on developing the more essential components. 

5. Conduct user research

The research will help you understand the needs and preferences of your target audience. Conduct surveys, interviews, focus groups, or analysis of user behavior in your niche. Use this data to refine and tailor your feature list to your users' needs.

6. Create user scenarios

Develop different user scenarios to better understand how users will interact with your app and what features they need to achieve their goals. For example, define how users will sign in, log in, perform specific tasks, make purchases, or interact with other users.

Top-9 Core functionalities for every web/mobile application

Multi-Device Synchronization

It is a process in which different devices work together and exchange information so that the data and settings on all devices are the same or consistent.

When devices synchronize, they usually use the Internet to exchange data. For example, you might sync your smartphone with your laptop to update contacts, calendars, and photos on both devices. When you make changes on one device, those changes are automatically transferred to other devices so that all appliances have up-to-date information. 

This makes working on multiple devices as easy as possible, gives you a significant advantage in the app market, and guarantees more users.‍

A User-Friendly and Responsive Interface

In addition to a functional set, it is vital to think about a pleasant design and clear navigation for the user's enjoyable and convenient use of the application. In this way, interaction with the application is easy and effortless. 

The user-friendly interface has clear and easily accessible buttons and menus, well-organized information, and clear instructions. This allows you to perform the necessary tasks without unnecessary complications.

The responsive interface ensures that the system responds quickly to user actions. When you press a button or perform an action, the responsive interface responds instantly and without delay. Or when you scroll through a web page or type on the keyboard, the responsive interface displays changes instantly and smoothly, with no visible delays.

The success of an application is directly related to its loading speed and the attractiveness of the interface. This is what users look for when choosing a user-friendly app.

A Search Option

This feature allows you to find the information you want in the app or on a website. When you enter keywords or phrases related to what the user is looking for, the app scans its database or content to find relevant results.

The search option saves time and effort by helping to search for specific items or information the user is looking for instead of having to browse everything manually. This option is handy on significant marketplaces to search for a particular item.

Push notifications

Push notifications typically appear on the screen of any user's device in the form of small messages or icons, including text, images, or sounds. They are designed to get your attention and notify you of updates or social media activity.

They are convenient and essential because they help you stay informed and ensure you get all the important information. However, if the user receives too many notifications, they can become intrusive or distracting. Therefore, it is crucial to provide the ability to customize their settings, which the user will set independently according to their preferences.

Offline Capabilities

With offline access, users can browse the app's content even when the device is not connected to Wi-Fi or a mobile network. For example, you can view and edit documents, listen to music, or watch videos without the Internet.

It is often the case that you cannot connect to the Internet or have no network, for example, when traveling in regions with poor coverage or when saving data traffic. Therefore, this feature will be a great advantage for those users who want to use the application offline.

Analytics Tools

Analytics tools in a web or mobile app can track various user actions, such as app launches, page views, button clicks, interaction with interface elements, and other events.

Using this data, you can make informed decisions, make changes, optimize your app, and improve its functionality to meet user needs and increase the effectiveness of your app.

Security

Security refers to the measures and mechanisms built into the application to protect data and keep users safe. This process includes data encryption, protection against unauthorized access, and user information storage in secure locations.

This is a basic but important feature that will not only keep each user's data secure and protected but will also increase trust and loyalty to your app.

Online Payments

Online payments allow you to purchase in online stores, pay bills for services, send money to others, and much more. It is convenient because the user can make payments at any time and from any place with Internet access. To do this, it is necessary to connect an electronic payment system through which the payment will take place. 

Due to the growing number of online stores, stores, and food delivery companies - the need to implement this function is increasing.

Integrations

Integration refers to the creation of links and interactions between different software components. This allows them to exchange data, transfer information, and use each other's functionality. 

For example, suppose you have a hotel booking site. In that case, you can integrate it with maps and geolocation systems to offer users the nearest available hotels and show them on a map. Integrating various features and services into web and mobile apps makes the user experience easier and better. 

Integrations help create more useful and powerful apps, extend functionality, and enable easy data exchange with other systems or services.

Summary

Mobile and web apps allow users to access information, interact with others, shop, automate workflows, simplify daily activities, and more through a user-friendly interface and unique features. 

Which features you choose depends on your business, its needs, and the app's purpose. Everything is tailored individually. Combining all of the above features can be the perfect modern app to meet the needs of your customers. But if, for some reason, one of the items doesn't work for you, use something that will definitely increase the efficiency of your product.

The modern financial world dictates its own rules. By including the critical success factors in this area, one can distinguish effective management of relations with the client. Reliable CRM solutions guarantee this goal because they contribute to optimizing many processes, thereby increasing productivity and taking customer interaction to a new level. In this article, we would like to share with you the best CRM systems that have been created, taking into account the characteristics and needs of modern financial companies.

What Is CRM for Financial Services?

CRM for financial services is software designed to manage customer contacts and improve comprehensive business performance. The main goals of financial CRM software are to automate processes, provide detailed analysis of client data to understand customer requirements better, reduce financial costs, and increase the effectiveness of strategic planning. Thus, all of the above in the complex helps to boost profits and strengthen the company's and brand's positions in the market.

How to Choose a CRM for Financial Services

Let's not argue that choosing CRM software for banking industry is difficult. However, we are ready to help you with this task by providing detailed tips.

First, you should clearly define the specific goals and the needs you intend to achieve/satisfy using CRM in financial services. Do you need only a system to manage customer relationships, optimize marketing campaigns, or increase sales? Or do you want it all together? Once you decide on your own goals, you can automatically narrow down the range of potential options. 

Regarding security, you have to be sure that the CRM system fully complies with modern data protection standards and is designed considering all reliable encryption mechanisms. This not only reduces the risk of data duplication but also maximizes the efficiency of your work processes.

It is also worth checking the CRM provider's support and training. Reliable support and the opportunity to receive professional training guarantee rapid business growth without downtimes and human-made errors. That’s why you should research reviews about your chosen service provider to see how comprehensive their support is.

In the end, the cost of the CRM system should not be devalued. Conduct a comparative analysis of various CRM systems, taking into account both initial costs and, accordingly, future costs, including further maintenance and updating.

Therefore, the main recommendation is to choose a system that offers the best balance between its cost and the quality of possible results for your company in finance.

Top 5 CRM solutions for Financial Services Businesses

It's time to share the top-5 CRM solutions for financial services known for their versatility, reliability, and power to manage customer relationships. However, note that each CRM for fintech from our list has its own features, which you should pay attention to while choosing the best option for your business.

Salesforce CRM

Salesforce is one of the leading CRM solutions, and its development team is confident in bringing its expertise to the financial services industry. This system includes tools that have been designed to meet the needs of many private equity firms, as well as the needs of companies working in related fields (banking, mortgage insurance). Within its core is a vast database to better understand customers' needs, habits, and behavior patterns. Other distinctive feature of this software solution is the ability to create custom modules adapted to the individual tasks and goals of a specific business (however, it is important to understand that the cost of deploying and supporting such a customized system will increase significantly). Moreover, it is equipped with AI-based analytics, helping consultants obtain even more valuable customer insights in no time. Among its other benefits are the ability to consolidate disparate data and optimize lead management, as well as almost unlimited scalability and flexibility.

Its disadvantages include that its work fully depends on the stability of the Internet connection and that its price may be too high for growing businesses. In addition, this CRM has a lot of modules, and the initial setup of the system often requires the participation of a narrow-focused specialist.

Envestnet Tamarac CRM

This fintech CRM focuses on helping independent financial advisers provide more personalized investment expertise to individual clients. Envestnet Tamarac CRM offers intelligent real-time reporting to help track customer progress toward their financial goals. The presence of optimized business adaptation processes for new customers is worth noting, as it allows you to quickly and easily create new accounts through Salesforce. This system is built entirely on the Microsoft Dynamics platform with a familiar Microsoft Outlook interface. Also, in the mobile version, consultants can enter the system «as a client», which enables them to see the dashboard exactly as their client would see it.

The cons of this solution include poor collaboration functionality because, as we mentioned above, it was designed initially for individual consultants.

Wealthbox CRM

This platform is flexible enough to be suitable for a wide range of users – from financial consultants to custodians and large firms combining the functions of broker and dealer. CRM data management, multi-channel client communication, and integration with social networking platforms like Twitter. With it, you can customize «under yourself» business processes or rely on business process templates using conditional logic to automate routine tasks. Also, it provides the ability to use Kanban dashboards to track potential financial transactions and keep abreast of how to work with potential clients. Wealthbox CRM is popular because of its low entry threshold.

However, along with all these benefits, this solution has several drawbacks, too. For instance, some users are dissatisfied with the poor householding functionality. In addition, Wealthbox may not be suitable for people who need to sync with Outlook calendar.

Zoho CRM

Zoho financial CRM software helps collect and organize all customer data, including basic CRM-related information, financial documents, financial policy information, and insurance records. This fintech CRM software offers tools for working with so-called «assets under management» to track all your transactions with financial institutions and other companies (here, we mean AUM tools where AUM is Assets Under Management). With built-in support modules (case modules), you can match your customers' needs with more financial data to provide efficient support. It is worth mentioning «Zia» – the Zoho assistant based on artificial intelligence, which adds a thoughtful layer of forecasts and suggestions for customers and helps you know when and how best to contact them.

At the same time, some Zoho users complain about limited customization features, poor reporting capabilities, and inconvenient dashboards.

NexJ CRM

NexJ is an intelligent financial services CRM with highly specialized modules for financial and banking consultants (specifically, they can use the private equity management module). This CRM system focuses on the specifics of the regional market with experience in more than 60 countries, and all this is thanks to the active use of artificial intelligence, machine learning, and data analytics. These technologies, properly implemented in this solution, allow consultants to concretize their financial recommendations. 

In general, NexJ looks and feels like a highly professional system, but some users note that they feel confused when they have to leave their requests on its pricing. 

Conclusion

The range of financial services available today requires the integration of automated software solutions that are practical, flexible, and more affordable than traditional consultants, whose services were only affordable for some.

CRM for financial services, private capital management, debt resolution, and pension planning are sophisticated tools that go beyond conventional databases. They are designed to foster trust and build long-term relationships with your clients. As all financial services are future-oriented, financial CRM systems help you provide brighter and more profitable prospects for your customers, your company, and, of course, your personal and professional success as a consultant. Thus, the financial CRM system allows you to achieve the best financial results for your customers and provide them with the best service.

IT outsourcing has established itself as a usual part of operation for many companies worldwide. It delivers multiple benefits. These are optimization, cost-effectiveness, scalability, and, of course, access to special-purpose knowledge and skills. At the same time, choosing a service provider is far from easy, even if you resort to the help of outsourcing management software. That’s why we decided to shortlist the best IT outsourcing countries and cover the possible pros and cons of each option.

Key Industries That Outsource IT Processes in 2024

Nowadays, a business that drives innovations or simply wants to grow faster opts for external IT services. They help to reach the goals set faster. In this context, outsourcing software development for startups is worthy to highlight since proficient IT services facilitate bringing innovative ideas into reality. Overall, in 2024, IT services were widely requested by the entities operating in the following sectors:

• Fintech – fintech software outsourcing helps to elaborate on effective solutions for users’ payments and transaction monitoring. Professional IT services enable many existing entities and startups to be well-informed about the latest developments. 
• Healthcare – bolstering the remote interaction between hospitals and patients benefits the industry a lot. For example, healthcare software outsourcing can contribute to remote patient monitoring, predictive analysis, diagnostics, nanomedicine, and so on.
• Telecommunications – the primary point for applying IT advancements is to boost the speed of connection, ensure the network's resilience, facilitate data processing and storage, as well as implement effective billing.
• Cybersecurity – third-party experts can respond to emerging threats and help to smoothly address the existing ones. Advanced IT algorithms they implement help to detect fraudulent transactions and prevent customers from losing their money.
• Education – advanced IT algorithms are now widespread across many universities worldwide. The latter can arrange steady and effective remote interaction with their students. 

Checklist on How to Choose a Country to Outsource Software Development 

Having plenty of IT outsourcing services offers to choose from is surely good. Where competition is intense, the quality is always good. At the same, the prices are balanced under such conditions. On the other side, choosing among multiple offers is a bit time-consuming. The below criteria will help you to sort all the available options out faster:

• Quality is a primary aspect to take into account. Cheaper never means better. In fact, it may turn out that low-quality products may take even more funds, especially, when it comes to corrections. However, the complexity of the task should be kept in focus as well. For more complicated assignments, the quality of outputs has to be superb.
• Tech education – the specialist you would like to assign should have sufficient tech background and skills. Emphasize whether a provider draws attention to the ongoing training. In addition, true IT experts should be ready to address the specifics of your projects effectively as well. Therefore, take time to discuss this aspect in advance.
• Cultural aspects – this nuance is often underestimated, however, it plays a great role. There are jurisdictions that are closer to the Western standards. However, some states are more keen on preserving their local specifics. Make sure you understand it well while being on the shore.
• Timezone – the perfect option in this case is nearshore software outsourcing. When your state is adjacent to the neighboring one where you decide to hire developers, it is always possible to arrange a closer collaboration. If you consider developers from the other part of the globe, make sure you have at least 3-4 hours of overlap.
• English proficiency – price and quality can mean nothing if there is a significant language barrier. While choosing your service provider, ensure developers have sufficient English level. Upper-Intermediate will work well for projects that do not require your constant supervision. However, more complicated ventures, especially innovative ones, will definitely require a more advanced level. 

10 Best Countries to Outsource Software Development

There is a list of 10 top technology countries where you will be able to find the best IT experts:

1. Ukraine

This country is a worthy place to hire developers. The reason is simple. It ensures price-quality balance without sacrificing the latter by any means. The state has a truly impressive pool of skillful workforce. Even more, lots of local talents are spread across the globe. 

The rates are much lower than in the vast majority of the European jurisdictions. Such prices are determined by the affordable costs of living in Ukraine. At the same time, the local talents improve their level of education regularly. Also, they fully immerse themselves in the Western approaches. That's why the quality of their products is bolstered steadily. These endeavors make Ukraine probably the best option for software development outsourcing in Eastern Europe. 

Although there is a huge time gap with the USA and Canada, local developers are also flexible at this point. Many of them are often ready to tighten their work to another time zone. The level of English is well here, both written and spoken.

If you need to hire someone who can elaborate on innovative tech solutions, you can find the right professional here as well – Ukrainians are capable of thinking outside the box. 

Ukraine fosters advancements and investments in this industry. There is even a separate Ministry of Digital Transformation in charge of ensuring a favorable landscape. Ukrainian specialists are also aware of and apply Western and European standards, including GDPR.

Hourly rates: $20-55

2. Poland

This is the EU location that ensures optimal price-quality balance. The rates are slightly higher compared to Ukraine since living in Poland is much more expensive. 

In this country, the greater emphasis has been placed not on state regulation but on market self-regulation. Here, you can find probably the largest software outsourcing companies in Europe. The scope of available services is comprehensive. English proficiency is very decent here, both written and spoken. This country also facilitates tech education. 

Poland is a definitely great option for bringing large IT projects to life, where a level of complexity is above the average. On the contrary, this country is not too keen on promoting innovations. It adopts those that come from other European or North American states. However, the culture and approaches to doing business in Poland are Western overall.

Hourly rates: $35-55

3. Bulgaria

This is another European country that is a good destination for IT outsourcing. Bulgaria has gained a reputation as a reliable IT hub with a highly skilled workforce. In this jurisdiction, a special emphasis is placed on robust IT education and ongoing training. 

The local professionals provide a comprehensive set of IT services. Bulgarian IT professionals can ensure the quality of the products at a good level. The prices of their services are even a bit higher than in Poland. 

The level of English is sufficient for communicating with foreign partners. Bulgarian IT specialists have a mindset focused on Western standards and problem-solving. The local timezone greatly overlaps with the USA and Canada, for instance.

Hourly rates: $40-90

4. Switzerland 

This country is famous for its professionalism. Local specialists excel in most spheres, including IT. Due the standards of living are more than high here, this determines the high costs of outsourcing software development, including high salaries for developers.

Switzerland has an extremely favorable legal and regulatory landscape. It greatly fosters innovations and high-performance standards.  Switzerland may also be characterized as a calm place with political stability and democracy.

The English level is extremely high, both written and spoken. Ensuring worldwide collaboration is easy here since the time zones with the USA and Canada greatly overlap.

Hourly rates: $100+

5. China 

Dealing with Chinese developers is both an opportunity and a challenge. On the one hand, this country ensures a high level of tech education and training. At some points, this education is considered to be among the best ones in the world. Following the Confucian lifestyle, local people are very hardworking and dedicated. Hiring Chinese developers is a good option from the point of pricing. 

On the other side, there are too severe cultural differences. This makes it difficult to deal with the Chinese developers at many points. Another drawback is the time difference. Arranging real-time collaboration with local specialists can be challenging. Finally, the level of English is a bit below the average level. This is probably the main obstacle on the way to the sufficient integration of Western standards in the Chinese IT industry.

Hourly rates: $35-75 

6. Philippines

This is probably one of the fastest-growing IT outsourcing markets. The IT sector is one of the most developed in this country. However, the rates for the IT services are reasonable. That is mostly because of the affordable living costs that characterize this jurisdiction. The scope of services is diverse here.

Speaking about the collaboration, it is generally easy to implement. English is the official language in the Philippines. Local IT specialists have excellent knowledge of IT terminology common to the Western world. This country also had a long history of dealing with the USA. This enabled it to absorb American business standards immersively. Finally, time zone differences are slight. 

Hourly rates: $20-60

7. Colombia

If you are interested in outsourcing to low cost countries, Columbia may be the right choice. In general, this is a growing market. The rates are below the average ones in the market. But it happens that the quality of services can be decent. 

At many points, it implements and shares the standards common for the North American states. It is also convenient to work with locals since the time zones of the USA and Canada are very close. Still, the level of English should be better for more effective communication.

Hourly rates: $30-50

8. Mexico 

Mexico is not a widely known option, but it is still a good destination for IT outsourcing. First of all, it has lots of skilled professionals who are dedicated and flexible. The level of IT education in this country is high. Even more, close proximity to the USA makes it possible to share common business standards. Mexicans are keen on agile software development. The level of English among the Mexican developers is Upper Intermediate and higher. 

The prices for IT outsourcing services vary depending on the complexity of the project. However, the local rates are a bit below the average in the global market. 

In terms of cooperation, there are some cultural aspects to consider. Mexicans prefer close face-to-face communication. Given that this country is in the same time zone approximately as the USA and Canada, establishing communication has no difficulties.

Hourly rates: $30-100

9.  India

This is one of the top tech countries which remains a global leader in this context. It has a vast pool of skillful tech professionals. Moreover, this state ensures balanced, even say, low prices for the IT assignments done. The level of English is high. In fact, India is among the states with the highest ratio of people speaking English. 

Having a population of around 1.5 billion people, it is possible to bring to life large projects here (even more, this means that in India, you can find the cheapest developers in the world). As for the prospects, this country invests a lot in tech education. Overall, the Indian business framework is mature given its historical bounds with the West. 

Speaking about the drawbacks, a variable quality should be mentioned in this first turn. Cost optimization in this case can be made by sacrificing the quality of the final product. Another downside is the time difference. It may greatly impact real-time interaction in certain cases. Unfortunately, there may be only several hours of overlap with the USA and Canada.

Hourly rates: $15-50.

10. USA

This is probably one of the best countries for software engineers. It is open and encourages innovations and advancements in all terms. The level of technical expertise among developers here is very well. 

The USA not only follows the strictest standards for software development. It also upgrades them and introduces new ones. The USA gave to this world plenty of advanced technologies, AI is the latest one. Also, it is worth highlighting UX design outsourcing, as the USA has the biggest talent pool of designers in the world. Therefore, this country can be considered the right place to implement complex ideas.

Still, the prices for the services are pretty high. However, depending on the complexity of the tasks you want to assign, it may be worthwhile to outsource them exactly in the USA.

Hourly rates: $40-80

Final Words

Summing up the above, the complexity of the project determines the choice of the service provider. Of course, for the high profitability of a project, it is reasonable to save as much as possible. Still, this should be made without sacrificing the quality by any means. Attempting to save a lot may have a reverse effect – you may have to pay more than intended initially. Correcting a poor-quality product is always a far more expensive endeavor. 

If you want to find the perfect ratio between the quality of services provided and their cost, you can consider our company. We will implement your ideas in the most cost-efficient way without compromising its viability and competitiveness, just contact us.

The year 2023 is when technology continues to overturn the existing order of things in the digital environment. Developers look for new ways to push development boundaries every year. From web development to machine learning, the languages we'll discuss today offer a wide range of possibilities and allow developers to turn their creative ideas into reality.

The most popular programming languages in 2023

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Languages, once considered groundbreaking, are becoming the standard, while new versions are constantly emerging to meet the needs of the most demanding projects of all sizes. The rapid development of the digital world in all areas of business and industries leads to a constant demand for powerful programming languages that can be used to create innovative products that simplify the work processes of any business. And also increasing the level of efficiency through the automation of work processes.

In this dynamic programming landscape, some languages are rapidly gaining popularity because of their strengths and ability to meet modern developers' needs. At the same time, some traditional languages remain relevant and continue to play an essential role in the programming world. It is important not to lose the balance between "classics" and new languages that complement the development process and make it more straightforward.

We analyze the top programming languages shaping the technology landscape of 2023 and enabling developers to build high-performance applications, create intelligent algorithms and machine learning, develop web applications, and process massive amounts of data.

Whether you're a seasoned programmer or just starting out in the development world, these popular languages offer you the opportunity to explore new horizons in technology. Let's take a look at what programming languages are worth learning and using in 2023 to stay at the forefront of innovation and successfully implement your projects.

• Python continues to hold its position as one of the most popular programming languages. With its simple syntax, Python is ideal for developing web applications, data analysis, machine learning, and artificial intelligence. With an extensive ecosystem of libraries such as NumPy, Pandas, TensorFlow, and PyTorch, it provides powerful tools to solve diverse problems.

• JavaScript remains an integral part of web development and continues to hold an important place in the programming world. Because of its versatility and widespread use in frontend and backend development, JavaScript is a significant component for creating interactive user interfaces and web applications. Frameworks and libraries such as React, Angular, and Vue.js make developing JavaScript web applications effortless.

• Java is one of the most popular programming languages, especially in enterprise environments. Because of its reliability, portability, and extensive support from the developer community, Java is used to develop scalable applications, web services, and mobile applications on the Android platform. Java also offers a rich selection of frameworks, such as Spring, that simplify application development and management.

• TypeScript, a programming language built on top of JavaScript, will continue to gain popularity in 2023. It adds static typing to JavaScript, which helps developers identify errors at compile time and create more robust code. TypeScript is widely used to develop large projects and applications. Its support in popular integrated development environments (IDEs) such as Visual Studio Code makes it an attractive choice for many developers.

• Go (or Golang) is a relatively new programming language gaining popularity in 2023. Developed at Google, Go provides simplicity and efficiency, enabling the creation of high-performance applications and services. Because of its competitive performance and ability to scale quickly, Go is often used to create web servers, micro-services architecture, and big data processing.
  

Summary

These five programming languages are leading the development world this year. They provide developers with powerful tools for creating various applications and solutions, including web apps, mobile apps, and large-scale digital projects. The choice of programming language depends on the specific needs of the project and the developer's preferences. Still, these five leaders provide a solid foundation for success in today's technology environment.

The technological landscape is constantly changing, and programming languages are also evolving. New languages emerge, and existing ones are updated and adapted to changing needs. Therefore, developers need to remain flexible, adapt to the rapidly changing technological environment, keep abreast of the latest trends, and learn new technologies and programming languages to successfully implement projects of any complexity.

Statistics claim there are 8,945 programming languages, yet we bet neither developers nor businesses need all of them to get market traction. As we enter Q1 of 2025, the programming world hasn't changed much; almost every programming languages rating is still shaped by a mix of established languages and modernized classics like Java and Python. However, it has shifted to include innovative tools like AI, blockchain, data science, and IoT. 

To cut through the clutter, we've analyzed the latest trends and compiled a list of the top 10 programming languages for 2025, an overview of different rankings, and the best ideas on choosing a programming language for a project. Read on for success.

Programming Languages Ratings

As of early 2025, some programming language rankings are still based on 2024 data. However, developers, businesses, and educators can leverage PYPL, TIOBE, and GitHub insights to guide learning efforts, hiring strategies, and project development. 

Read all the ranking lists and find the most desired programming language in 2025.

PYPL Ranking 2025

The PopularitY of Programming Language (PYPL) index is a Google-powered snapshot of programming language popularity. It tracks tutorial searches, giving a clear view of which skills are in demand—a crucial tool for tech trend analysis.

As per the PYPL report, 5 five most popular programming languages are: 

The TIOBE Ranking 2025

The TIOBE Index provides a monthly snapshot of programming language popularity. It ranks languages by global developer presence, educational material availability, and industry adoption based on search trends from platforms like Google, YouTube, and Wikipedia. Amazon and Baidu.

According to TIOBE, the five most popular programming languages are:

The GitHub Ranking 2025

GitHub is a global code hub shaped by Fortune 50 companies. While GitHub doesn't provide rankings, tools like GitRanking organize repositories, and DevRank measures developer influence. Here are the dynamics of popularity from the latest GitHub report:

IEEE Spectrum Ranking 2025

From GitHub commits to Stack Overflow threads, this annual roundup analysis cuts through the noise of top programming languages with three crystal-clear metrics: established giants, breakthrough performers, and market demand. Here is the latest technical ranking IEEE report:

RedMonk Ranking 2025

RedMonk ranks programming languages twice a year, blending GitHub code usage with Stack Overflow engagement. They track adoption trends and developer preferences by combining code usage with developer discussions. Over 45% of software companies use these quarterly rankings to guide their technology decisions.

Here are the latest insights from their programming language RedMonk rankings:

Stack Overflow Developer Survey 2025

The Stack Overflow Developer Survey collects data from over 30,000 developers to explore their opinions and habits. It highlights trends in technology usage and preferences, illustrating how new tools and frameworks rise in popularity each year while others fade away.

According to this survey, the five most popular programming languages here are:

What Affects Programming Language Rankings?

Language rankings depend on GitHub stats, Stack Overflow activity, and job demand. Tech giants can elevate a language overnight. Community support ensures good documentation, while technical strengths drive relevance in AI, cloud, and mobile. Overall, enterprise adoption hinges on commercial investment in tools and frameworks.

Why Do Developers Switch Languages?

Market demand and salary potential often trigger language switches. New projects at work may require learning unfamiliar stacks. Some developers migrate when they hit technical walls with their current language. Others switch purely out of curiosity to explore fresh programming concepts. Better developer experience—from cleaner syntax to powerful debugging tools — can seal the deal.

How Do Trends Impact Programming Language Rating?

Tech trends reshape the programming landscape constantly. Cloud computing favors languages that handle concurrency well. Mobile development boosts cross-platform-friendly languages. AI and ML create demand for specialized numerical computing capabilities. Modern software architecture—like microservices and serverless — influences language choice based on performance needs. Open source momentum can make or break a language's trajectory. Universities follow these shifts, molding new developers' skills accordingly.

Why Do Some Languages Rise or Fall in Popularity?

Languages surge when they solve pressing problems—like Rust addressing memory safety. Corporate champions (Google with Go, Microsoft with TypeScript) accelerate adoption through resources and integration. Growing ecosystems create network effects, attracting more developers and tools. Languages decline when they stagnate or lose their edge to newer alternatives. Yet some persist through enterprise inertia, particularly in legacy systems. Success hinges on balancing innovation with stability while nurturing a vibrant developer community.

Top 10 Most Popular Programming Languages in 2025 

From crunching data and financial management apps to renewable energy systems, optimizing solar panel efficiency, and reducing carbon footprints—programming spans different fields of humankind. 

But here's the million-dollar question: with so many programming languages out there, which ones are worth learning? We turn to the database of the most desired and loved programming languages from Stack Over Flow. Here is the list of the top 10 below:

1. Python

With clean syntax and broad applications, Python is a go-to language for web development, AI, and data science. Supports procedural, object-oriented, and functional programming, consistently placing it at the top of programming language rankings.

Pros:

• Easy to Learn: Simple syntax makes it beginner-friendly.
• Extensive Libraries: Large collection of standards and toolkits available.
• Integration: Works well with languages like C and C++.
• High Demand: Especially among startups.

‍Cons:

• Slower Speed: Slower compared to compiled languages.
• Limited Concurrency: Global Interpreter Lock (GIL) limits true parallelism.
• High Memory Use: Consumes more memory than some alternatives.
• Complex Debugging: Dynamic typing can complicate the debugging process.
• Poor for Certain Applications: Less suitable for mobile, embedded, and real-time systems due to efficiency concerns.

Common Use Cases of Python:

Thanks to TensorFlow, Python excels in data science and web development, particularly in AI and ML. From Instagram to Dropbox, it powers a wide range of applications. We bet that in years ahead, we'll still see Python as the number №1 solution, ranked in most ratings.

2. JavaScript

Among all the most popular programming languages, JavaScript remains the "default" one for web developers. Born for the browser, JavaScript now dominates both frontend and backend, thanks to Node.js and a massive npm ecosystem. In 2025, it will continue to dominate as a leader on GitHub in terms of code volume.

Pros:

• Versatile & Rich Ecosystem: Works for front-end, back-end (Node.js), and mobile, with a vast array of frameworks and libraries (React, Angular, Vue).
• Client-Side Responsiveness: Executes directly in the browser, reducing server load and improving user experience.
• Interactive Web Pages: Enables dynamic features, animations, and real-time updates.
• Large, Active, and Supportive Community: Ensures ample resources and solutions.

Cons:

• Security Vulnerabilities: Client-side code is visible and susceptible to attacks (XSS).
• Inconsistent Browser Behavior: Code may render differently across browsers.
• Performance Limitations: Heavy client-side JavaScript can impact loading times.
• Debugging Complexity: Client-side debugging can be challenging.
• Single-Threaded Constraints: Limits performance for complex, concurrent operations.

Common Use Cases of JavaScript:

JavaScript powers web development with React, Angular, and Vue.js for interactive interfaces and single-page apps. Node.js extends its reach to server-side development, real-time apps, and microservices.

3. TypeScript

TypeScript is a Microsoft-backed superset of JavaScript that adds static typing and class-based object-oriented programming. It compiles to JavaScript and runs anywhere JavaScript does. Strong type safety and advanced tooling make it a top choice for large-scale development. In 2025, it will remain in the first position of top programming languages.

Pros:

• Type Safety: Static typing helps catch errors during development rather than runtime.
• Enhanced IDE Support: Better code completion, refactoring, and error detection in development tools.
• JavaScript Compatibility: Can use all JavaScript libraries while adding type safety.
• Enterprise-Ready: Perfect for large codebases and team collaboration.
• Improved Code Quality: By catching errors early, TypeScript reduces the likelihood of bugs in production.
• Better Collaboration: Type annotations make code more self-documenting, making it easier for teams to understand and work on the same codebase.
• Scalability: TypeScript is particularly well-suited for large and complex applications.

Cons:

• Type Definition Files: Some libraries may lack or have outdated type definitions.
• Complexity: Initial installation and setup might be a bit tricky.
• Development Time: Writing types can slow down initial development speed.
• Compilation Step: TypeScript code needs to be compiled into JavaScript before it can be executed.
• Increased Code Size: Type annotations can increase the size of your code.
• Runtime vs Compile time: while Typescript catches many errors at compile time, it does not guarantee that there will be no errors at runtime.

Common Use Cases of TypeScript:

It's the default for Angular and powers tools like Visual Studio Code and Slack. Its compatibility with Node.js makes it a top choice for full-stack development, prioritizing type safety.

4. Rust 

Rust, developed by Mozilla Research, delivers memory safety, high speed, and a growing ecosystem. Its ownership model eliminates memory vulnerabilities without a garbage collector. Adopted by startups and major tech firms, Rust is a rising star that won't stop raising its popularity for performance-critical development.

Pros:

• Memory Safety: Catches errors before runtime, not during production crashes.
• Performance: Matches C/C++ speeds with higher-level abstractions.
• Cargo: A modern package manager that just works. 
• Cross-Platform: Write once, deploy anywhere approach.
• WebAssembly Ready: First-class support for the web's low-level powerhouse.

Cons:

• Learning Cliff: Ownership and lifetimes concepts demand serious study.
• Unforgiving Compiler: The "fighting the borrow checker" experience is real.
• Build Times: Complex codebases can test your patience during compilation.
• Young Ecosystem: Some specialized tools are missing in the standard of mature languages.

‍Common Use Cases of Rust:

Rust shines in critical infrastructure: Firefox's core engine, Discord's real-time systems, and cutting-edge blockchain platforms. It's the go-to choice for projects where failure isn't an option—embedded systems, operating system kernels, and game engines pushing performance boundaries.

5. Go (Golang)

Go, developed by Google, is a high-performance language known for simplicity, efficiency, and scalability. In 2025, Go continues to rise with native scaling and a lightweight yet productive design, often chosen over Java or C# for backend and DevOps. While Rust challenges C++, Go is a streamlined alternative for modern software teams. Consequently, in recent years, it frequently tops each rating of programming languages.

Pros: 

• Simplicity: Clean and straightforward syntax, easy to learn. 
• Concurrency: Excellent built-in support for goroutines and channels. 
• Performance: Fast compilation and execution speeds, comparable to C++. 
• Garbage Collection: Automatic memory management, reducing the risk of memory leaks. 
• Static Typing: Catches type-related errors at compile time. 
• Standard Library: Comprehensive and well-documented standard library. 
• Cross-Compilation: Easy to compile for different platforms. 

Cons: 

• Lack of Generics: Limited code reuse for different data types (now addressed but still evolving). 
• Verbose Error Handling: Requires explicit error checking, which can lead to verbose code. 
• Limited Expressiveness: Some developers find the language less expressive than others. Dependency Management: Older dependency management was considered cumbersome (now improved with modules). 
• Smaller Ecosystem: While growing, the ecosystem is smaller than more established languages.

‍Common Use Cases of Go:

Go is widely used in cloud-native development, web development (server-side), and network programming. It's popular for building microservices, distributed systems, and command-line tools. Companies like Google, Docker, and Kubernetes utilize Go for their infrastructure and tooling.

6. C#

C# is a modern, object-oriented coding language developed by Microsoft as part of the .NET platform. It blends the robustness of C++ with the simplicity of Visual Basic, featuring strong type safety, versioning, and scalability support. In 2025, C# will remain the strongest language in the Microsoft ecosystem and seems not to lose its popularity.

Pros:

• Type Safety: A strong static typing system prevents common runtime errors and improves code reliability.
• Rich Ecosystem: Extensive standard library and NuGet package ecosystem for nearly any functionality.
• Cross-Platform: .NET Core/.NET 5+ enables true cross-platform development and deployment.
• Performance: High-performance capabilities, especially with recent memory management and compilation improvements.
• Modern Features: Pattern matching, nullable reference types, LINQ, and continuous language evolution.

Cons:

• Platform Dependencies: While cross-platform, some libraries still have Windows dependencies.
• Resource Usage: Higher memory footprint compared to lower-level languages.
• Compilation Required: Must be compiled before execution, adding a build step.
• Learning Curve: Complex features and patterns can be challenging for beginners.
• Verbose Syntax: More ceremonial code compared to dynamic languages.

Common Use Cases of C#:

C# is widely used in enterprise software development, particularly in the Microsoft ecosystem. It powers everything from ASP.NET web applications to Unity game development, Windows desktop applications, and cloud services on Azure. 

7. C/C++

C and C++ pioneers of modern computing remain unmatched in hardware control. C provides procedural efficiency while C++ adds object-oriented features; both deliver raw performance critical for systems development.  In 2025, it remains first among the 10 top programming languages.

Pros:

• Zero-Overhead Abstraction: Efficient execution with direct hardware access.
• Precise Memory Control: Fine-grained resource management without a garbage collector.
• Cross-Platform Compatibility: Runs on diverse architectures and operating systems.
• Extensive Legacy Codebase: Decades of existing libraries and software.
• Deep System Integration: Ideal for OS development, embedded systems, and performance-critical applications.

Cons:

• Manual Memory Management: Prone to leaks and undefined behavior.
• Complex Syntax: Challenging constructs like C++ templates.
• Slow Compilation: Long build times for large projects.
• Security Risks: More exposure to vulnerabilities like buffer overflows.
• Steep Learning Curve: Requires deep understanding of low-level concepts.

Common Use Cases of C/C++:

These languages drive OS development, game engines, embedded systems, real-time applications, and high-performance computing, including Chrome's V8 and major databases.

8. R

R is an open-source language for predictive analytics and data visualization, built with C, C++, Fortran, and R. It features S3 and S4 frameworks for flexible data modeling and method dispatching, which allows for flexible data modeling and method dispatching.

Pros:

• Statistical Analysis: Built-in capabilities.
• Visualizations: Publication-ready.
• Packages: Extensive scientific libraries.
• Math Operations: Advanced support.
• Community Support: Favorite among researchers.

Cons:

• Big Data Performance: Struggles with big data performance.
• Memory Usage: Can spiral quickly.
• Syntax Complexity: Unique syntax challenges newcomers.
• Package Quality Variance: Package quality varies wildly.
• Limited Use Cases: Beyond data analysis.

Common Use Cases of R: 

R dominates biotech labs and research institutions. Companies like Pfizer and Merck crunch clinical trial data while hedge funds model risk. Academia relies heavily on R for peer-reviewed research and statistical validation.

9. Java

Java is a high-level, object-oriented language known for its platform independence. With ongoing improvements, including syntactic enhancements and advancements in GraalVM and WebAssembly, Java is expanding beyond traditional backend development to stay competitive.

In 2025, Java will steadily retain its leadership in the top 10 programming languages as a workhorse for enterprises and large high-load systems. If you plan to work with large corporations, the banking sector, or any system that requires stability, Java is an excellent choice.

Pros:

• Platform Independence: Code runs anywhere via the JVM.
• Rich Ecosystem: Extensive libraries and frameworks like Spring and Hibernate.
• Concurrency Support: Built-in multithreading enhances performance.
• Security Features: Strong protections make it ideal for web and enterprise applications.
• Community Support: A large user base ensures abundant resources and tools.

‍Cons:

• Verbose Syntax: Requires more code compared to modern languages like Python.
• Performance Overhead: Slower than lower-level languages due to JVM interpretation.
• Memory Intensive: Consumes more memory than C or C++.

Common Use Cases of Java: 

Java is everywhere—from Google's backend to Fortune 500 banks. It's vital for Android, Spring, and high-load systems, and with cloud innovations, it continues shaping enterprise tech, big data, and finance.

10. PHP

Once a simple scripting language, PHP now drives much of the web, including WordPress and Facebook. Modern frameworks like Laravel have refined it, making PHP more efficient and scalable than ever.

‍Pros:

• Web Integration: Excellent integration with HTML and web servers.
• Easy Deployment: Simple setup and hosting requirements.
• Large Community: Extensive documentation and community support.
• Framework Ecosystem: Modern frameworks like Laravel provide robust development tools.
• Cost-Effective: Wide availability of hosting and developers.

Cons:

• Inconsistent Design: Historical inconsistencies in function naming and parameter ordering.
• Security Concerns: Easier to write insecure code if not following best practices.
• Type System: Weak typing can lead to subtle bugs (though improving with recent versions).
• Performance: Not as performant as compiled languages for complex applications.
• Legacy Code: Many older codebases use outdated practices.

Common Use Cases of PHP: 

PHP continues to dominate CMS, e-commerce, and traditional web development. Ideal for small to mid-sized projects, it remains the backbone of WordPress, Wikipedia, and countless content-driven platforms.

How to Choose a Programming Language for a Project?

While language wars rage on Twitter, programming languages ratings "hustle-and-bustle with numbers," successful tech leads know it's not about which language is "best" – it's about which one serves your specific mission. Let's delve into tips on how to figure out what programming language suits your business best.

Project Type

• Identify the target platform (e.g., web, mobile, desktop, or multi-platform).
• Define the application's purpose and user environment.

Language Features

• Evaluate performance, functionality, and built-in capabilities.
• Assess support for modern coding paradigms (e.g., functional, object-oriented).

Ecosystem

• Explore the availability of tools, plugins, and development environments.
• Investigate the breadth of open-source contributions and ecosystem maturity.

Scalability

• Analyze how well the language supports large-scale applications.
• Consider its ability to handle increased user loads or data growth efficiently.

Learning Curve

• Determine how intuitive the language is for new developers.
• Assess the time required to become proficient compared to alternatives.

Compatibility

• Verify how seamlessly it integrates with existing systems and APIs.
• Check for cross-platform support and interoperability with other technologies.

Industry Demand

• Research hiring trends and industries actively using the language.
• Examine its prevalence in job postings and developer surveys.

Support

• Review the quality and availability of official documentation.
• Look into active communities, forums, and professional support channels.

Future Trends

• Monitor the language's adoption of cutting-edge technologies (e.g., AI, blockchain).
• Analyze its trajectory in developer rankings and long-term viability.

Here is a detailed overview of the area domain and programming language features and connections:

Don't Fall Behind With the Wrong Programming Language

In most cases, when it comes to real development projects—it's not just about code but performance, scalability, and future growth. Checking the most popular programming language ratings ensures a wise, future-proof decision. Choose a language that fits your needs, leverages your team's expertise, and keeps you ahead of the curve. With Darly Solutions, you're always one step ahead.

Backed by deep industry knowledge and hands-on experience, we help businesses navigate the ever-changing tech landscape. Whether launching a startup or scaling an enterprise, don't let the wrong decisions hold you back—let's build for the future together.

In today's world of IT technologies, cooperation with Ukrainian developers is becoming more and more relevant and promising. Ukraine is ranked as one of the world's top IT service providers and continues to hold that position.

Ukrainian IT market overview

Today, the Ukrainian IT market has positive growth dynamics, as Ukraine is a more favorable center of cooperation than other countries. Why is this so? It is due to our specialists' high level of education, scientific potential, the country's proximity to the European market, and favorable tax conditions. The close geographical location to Europe also ensures faster and more efficient communication between clients and developers. All this makes it possible to double Ukraine's export revenues in the next 4 years.

Currently, more than 200,000 Ukrainian developers work in the international market. That demonstrates the high level of qualification and professionalism of IT specialists who successfully work on projects of any scale and complexity.

In 2021-2022, the IT industry grew by 36%, from $5 billion to $6.8 billion in exports. Over the past three years, these figures have almost doubled, and the growth trend is quite stable. Despite the war and issues many IT companies face nowadays, the IT sector is still a driving force in the Ukrainian economy and continues to develop. 

The majority of IT companies today work remotely. But this does not prevent participants in the development process from creating digital products, being in touch with the client, and providing timely updates on the work done. During the full-scale invasion of the aggressor country, all Ukrainian companies have adapted to the new reality and are ready for force-majeure situations at any level. 

Our company did not disrupt any project over the past year and delivered all developed products to each customer. Moreover, our practical work resulted in the support of foreign customers who want to cooperate only with Ukrainian companies on convenient terms.

Development tasks that Ukrainian developers solve

Ukrainian developers today are among the most in-demand in the world. Through continuous development and training, top-notch IT specialists contribute to developing international products and participate in their launch and maintenance. They successfully solve many development tasks and problems, including:

1. Complex technical projects

‍Ukrainian developers have high technical expertise and can implement projects of any complexity. They use the most advanced technologies and programming languages, such as Java, Python, Ruby, JavaScript, etc.

2. Mobile application development

A significant amount of experience is available among Ukrainian developers in developing mobile applications for iOS and Android platforms using React Native, Ionic, and other technologies. They know how to create intuitive, user-friendly interfaces that satisfy users' needs. They also specialize in developing cross-platform applications that run on multiple operating systems, which can significantly speed up the development process and reduce project costs. 

3. E-commerce platform development 

Ukrainian developers also have considerable experience in developing online stores and e-commerce platforms. They are familiar with major CMS systems such as Magento, Shopify, and WooCommerce.

4. Software development 

It is well known that Ukrainian developers create high-quality software. They know how to write code that will work quickly, efficiently, and reliably. They also have experience in various areas, such as web development, mobile development, artificial intelligence, and more. Ukrainian developers often use modern technologies and development methods such as Agile, Scrum, and DevOps to ensure high quality and fast development time.

5. Integration and maintenance

Besides developing new software, Ukrainian developers integrate it with existing systems and maintain them. They are ready to solve problems during the operation and ensure the smooth operation of the applications.

6. Cybersecurity

Ukrainian developers actively work in cybersecurity, providing reliable protection of information and data. They know how to protect applications from threats like hacker attacks, viruses, and other malware. And also use modern technologies and methods such as data protection, encryption, and authentication to keep software secure.

Advantages of cooperation with Ukrainian developers

Cooperation with Ukrainian developers today is one of the most advantageous options for companies looking for reliable and experienced IT specialists. Ukraine is one of the world's leaders in the IT services market, thanks to its talented and highly qualified developers capable of fulfilling tasks of any complexity.

• One of the main advantages of working with Ukrainian developers is the high quality of their work. Ukrainian developers have an extensive experience in various fields of IT. They follow the latest trends and use the most advanced technologies. Due to this, they can realize tasks of any complexity, including creating complex websites, mobile applications, software development, and much more.

• In addition, Ukrainian developers are known for their speed and efficiency. They strive to complete their projects on time without compromising the quality of their work. Ukrainian IT companies are also ready to provide flexible working conditions, including working in different time zones, which makes cooperation with them even more convenient and effective.

• One more advantage of cooperation with Ukrainian developers is their price competitiveness. Ukraine offers more favorable conditions for cooperation than other countries while maintaining a high quality of work and professionalism. That allows companies to save on costs without losing quality.

• ‍Flexible cooperation conditions are another advantage of Ukrainian developers. Many of them work remotely, which makes their services more flexible and accessible to clients from all over the world. That reduces the cost of organizing work with remote teams and the time required to find the right specialists. In addition, even remote communication with Ukrainian developers allows you to respond quickly to changes in the project and solve any problems that may arise. Also, communication with Ukrainian developers is in English, which facilitates interaction with customers from around the world.

Summary

Ukraine is one of Europe's largest exporters of IT services, with an excellent knowledge base, great motivation, and great opportunities. Despite the Russian invasion, Ukrainian IT companies continue to provide quality services, support the economy, and maintain a high rate of development and continuous growth.

Cooperation with the Ukrainian company is not only a guarantee of quality work but also support for Ukrainian businesses and Ukraine as a whole. Numerous IT companies survived difficult times and are gaining momentum in their activities due to the trust of international customers and the continuous work of the development team. 

If you regularly think about how to optimize your business operations by speeding them up, increasing their accuracy, reducing related expenses, and eliminating risks associated with the human factor, perhaps the introduction of artificial intelligence will be the best option. 

Specifically, according to the CompTIA IT Industry Outlook 2024 report, today, about 22% of companies are actively seeking to integrate AI into most of their technological solutions and processes, and 45% of companies are considering its implementation. If you are still in the second category, it's time to figure out which programming language will be able to form the basis of your new highly intelligent software tools.

The term “Artificial Intelligence” describes software algorithms and systems that are aimed at solving problems typical to the human mind—such as learning, as well as pattern generation and recognition. Such algorithms are based on colossal amounts of disparate data collected from various sources. Thus, these algorithms form self-learning software solutions capable of analyzing this data and extracting valuable insights from it.

Top 10 Programming Languages for Artificial Intelligence in 2025 

It is worth noting that AI-powered solutions may not be based on any software language—this can be explained by the high resource consumption of the AI algorithms that form them, as well as the lack of high-level functions. At the same time, there are ten languages that are most often used in AI programming. Which one of them? best programming language for AI?

Python 

At the top of our list of programming languages for artificial intelligence is Python. This programming language appeared long before the popularization of AI development. However, thanks to its low entry threshold, open-source code and extensive compatibility, its community quickly grew, and today, Python is considered one of the three most relevant languages worldwide. Beginners can easily find support and useful resources on platforms such as GitHub and Stack Overflow.

As for the tools for artificial intelligence programming, there are a lot of Python-compatible frameworks, libraries, and APIs, aimed at simplifying and speeding up this process. Among them are TensorFlow, PyTorch, Keras, SciPy, NumPy, Scikit-learn and Matplotlib and many others. Using them, developers can create high-performance, reliable, and scalable solutions that can solve even the most complex research problems. This significantly saves time since, instead of creating everything from scratch, you can simply import ready-made tools.

One of the factors that makes Python one of the most popular AI programming languages is its simple and intuitive syntax, which ensures ease of learning.

Python is also known for its versatility, making it a top programming language for artificial intelligence among data analysis scientists and software developers. For example, it is actively used to create neural networks and machine learning algorithms with frameworks like TensorFlow and PyTorch. Additionally, Python is widely applied in data analysis, helping to identify patterns, trends, and correlations.

Best suited for: AI development, data analysis, and machine learning.

Tip: If your project involves resource-intensive computations and requires a detailed analysis of requirements, it is worth considering other AI programming languages.

R

R, being a statistical programming language, is great for data analysis and visualization. Even though developing artificial intelligence and machine learning solutions is not the primary use case for R, it is still adept at handling very large numbers. So, where popular Python may fall short, particularly in Data Science and Data Analysis solutions, R may be a better alternative.

Additional benefits of this developer tool include support for functional programming, vector computing, and OOP, as well as a number of packages specifically created for AI and ML development (for example, gmodels, RODBC, and OneR).

It includes numerous visualization libraries such as Ggplot2, Lattice, and Shiny, which allow for the quick creation of graphs, charts, and other visual representations of data. Thanks to built-in functions like GLM and LM, specialists can perform statistical modeling.

The Caret library enables building, training, and testing machine learning models, as well as optimizing their performance through hyperparameter tuning.

R also provides tools for performance evaluation, including resampling methods and cross-validation, which help developers improve their AI models. Additionally, R supports integration with other programming languages, such as Java and Python, creating a powerful ecosystem for developing various applications.

Best suited for: Statistical analysis, data processing, and visualization in AI projects.

Tip: R is the best programming language for AI in the early stages of research and experimentation. However, its use for deploying machine learning models in real-world products may be limited, as it lacks built-in compilers and efficient memory management. Moreover, it may be challenging to learn for those who do not specialize in data science.

Java

This portable and scalable programming language is actively used in the development of highly intelligent corporate applications, especially for mobile platforms. Thanks to its platform independence, Java programs can run on any operating system where the Java Virtual Machine (JVM) is installed. In addition to compatibility with popular tools for ML programming and AI programming, such as the Deep Java Library for deep learning, OpenNLP for natural language processing, and Neuroph for building neural networks, Java is easy to write and debug program code.

It is worth noting that this language is object-oriented and scalable. It also simplifies data visualization and supports Swing and SWT (Standard Widget Toolkit), which allow developers to create attractive and complex graphics and interfaces.

Java offers a wide range of built-in libraries and modules, such as JUnit and Apache Commons, which accelerate development and help create high-performance applications. For example, Java is actively used in simulations, robotics, and embedded artificial intelligence systems. Additionally, the RTSJ extension enables the development of real-time systems, particularly for robotic solutions.

Support for multithreading makes Java suitable for resource-intensive tasks that require parallel execution. The language scales well, allowing for the creation of both simple and complex applications capable of adapting to growing demands. A large user community provides access to numerous learning materials and valuable resources.

Optimal choice for: Developing scalable, platform-independent AI applications and embedded systems.

Tip: As an artificial intelligence programming language, Java is an excellent choice for large-scale business AI systems where algorithms need to be transformed into stable and reliable software. It ensures high performance, reliability, and compatibility with various devices, making it an efficient choice for building high-performance solutions.

Weakness: Verbose syntax, not specifically designed for AI.

C++

C++ is considered an extremely powerful language for AI programming and can greatly benefit developers when creating games and embedded systems. Like Python, C++ is a mature language, which does not detract from its advantages, such as flexibility and high performance. C++ has several libraries for machine learning and neural networks that help complex algorithms run faster (including MapReduce, mlpack, and MongoDB). In general, many software engineers prefer this language for building projects that require high speed, as it interacts with training modules in a production environment.

Actually, that’s why this low-level language used for AI is primarily a tool for creating complex banking software, cloud systems, and intelligent speech recognition solutions.

C++ plays a key role in developing complex software systems with a high level of abstraction. Its popularity in the field of artificial intelligence is due to its flexibility, performance, and ability to efficiently interact with hardware components.

Although machine learning libraries such as TensorFlow and PyTorch are primarily designed for Python, C++ allows working with them through APIs or bindings. This makes it possible to integrate powerful tools into developed applications without the need to build all algorithms from scratch. Thanks to support for multithreading and parallel computing, C++ is suitable for creating high-performance AI solutions and stands out among other machine learning programming languages.

Developers can manually manage memory allocation, for example, by allocating more resources for computationally intensive tasks, which reduces latency and improves performance. Additionally, using OpenCL and CUDA enables hardware acceleration, enhancing algorithm efficiency by leveraging the computing power of graphics processors.

Best suited for: Developing high-performance AI applications with hardware resource control and memory management.

Weakness: A complex language designed for system programming.

JavaScript

JavaScript is a scripting language used to add interactivity to web pages. Even though it is not as popular as the AI programming languages ​​described above, it can be extremely helpful in implementing solutions for Data Science, one of the most promising areas for using JS.

In addition, JavaScript is the most popular programming language in the world, so you won’t have any problems finding the right AI development tools and frameworks that are compatible with it. The popularity of this coding language for AI can be explained by several factors: reliability, stability, abundance of high-level functions, libraries, and tools, as well as fairly low requirements for other technical skills of developers (it is likely that you will not have any difficulty finding dozens of AI tools that support JS, which will cover the whole scope of work related to your AI project).

Among AI programming languages, JavaScript is particularly useful for developing real-time AI applications, including virtual assistants, chatbots, and recommendation systems. By using APIs, developers can easily integrate artificial intelligence into web applications, expanding their functionality.

Since code execution occurs on the client side, this reduces server load and increases speed, which is especially important for applications requiring instant interaction.

With the emergence of JavaScript frameworks such as TensorFlow.js, Brain.js, and ConvNetJS, it has become possible to directly implement machine learning in web development. These tools allow training and deploying machine learning models directly in the browser, expanding AI capabilities in the web environment.

Best suited for: Developing interactive AI applications in the browser and integrating artificial intelligence into web projects.

Weakness: Not well-suited for mathematical computations and machine learning.

Scala

This machine learning language is concise and expressive, and is often used in big data solutions. Scala is a multi-paradigm language specifically designed to express common programming concepts in a simple, convenient, and type-safe manner.

Scala is commonly used in the development of machine learning algorithms and pattern detection in large data sets, and all this – thanks to the built-in Smile data processing library, extensive compatibility with AI frameworks and libraries (in particular, BigDL, Breeze, DeepLearning.scala, and so on), as well as its object-oriented and scalable nature.

It allows writing clear and efficient code, facilitating the creation of high-performance applications.

One of Scala’s key advantages is its default support for immutability. This means that once declared, objects do not change their state, making them easier to track and manage throughout the entire development lifecycle. Similar to C++, Scala supports multithreading and parallel computing, enabling the creation of AI applications capable of performing multiple tasks simultaneously.

Thanks to its compatibility with Java, developers can leverage tools and libraries built for Java, including Apache Hadoop and Spark, which are essential for processing large volumes of data. This helps analyze information, identify hidden patterns, and make informed decisions.

Scala also features a powerful pattern matching system, making it convenient for natural language processing (NLP). This is particularly important in areas where AI needs to recognize and analyze user input to generate appropriate responses. Scala’s object-oriented approach supports the development of specialized tools, such as for prototyping and research.

Best suited for: Developing scalable AI applications with big data support and real-time information processing.

However, Scala is not the best choice for projects requiring rapid prototyping, such as NLP or machine learning model development in tight timeframes. This is due to a limited selection of AI-focused libraries and frameworks, as well as a longer compilation process, which complicates quick testing and model adjustments.

Julia

Julia is the best programming language for AI powered scientific solutions and technical projects that require statistical data processing. This is a relatively new language with dynamic typing, parallel programming, and JIT compilation that has proven itself well due to its high speed (Julia-based projects are rarely inferior in performance to their C++-based counterparts), flexibility, and a mature ecosystem.

Many of the tools built into this language use polymorphic dispatch, which makes switching between them much faster than in other languages. Julia has gained popularity among AI programming languages for AI developers due to its high performance and rapid prototyping capabilities. It ensures fast data processing, which is critical in the field of artificial intelligence due to the vast amounts of data that need to be analyzed. Moreover, its syntax is clear and similar to Python, making the learning process easier.

Julia supports interaction with other programming languages, such as R, C, and Python. This allows developers to use powerful libraries, including TensorFlow and PyTorch, integrating them into their projects without the need to rewrite code.

With built-in support for parallel computing and distributed computational processes, Julia is capable of effectively handling large datasets and significant computational loads without a noticeable drop in performance.

As an open-source language, Julia provides access to libraries developed by the community, saving time and effort during development. Notable tools include Plots.jl and Gadfly.jl for data visualization, MLJ.jl used for machine and deep learning, as well as Scikitlearn.jl, which is an interface to the Python library scikit-learn.

Best suited for: High-performance AI applications that require fast data processing and easy integration with other languages.

Weakness: Newer language, smaller ecosystem.

Haskell

Haskell is based on the functional programming paradigm and uses key concepts such as pattern matching, higher-order functions, and immutability. This makes it suitable for handling complex data structures in the field of artificial intelligence.

The language has a strong mathematical foundation, which contributes to the development of complex algorithms and even deep learning models. Thanks to lazy evaluation, expressions are only evaluated when needed, allowing for more efficient resource usage.

Haskell's strong type system and type safety control help avoid errors related to incompatible values in variables, improving the reliability and stability of AI applications developed in this language.

Best suited for: Creating AI systems with complex algorithms and a focus on functional programming.

Weakness: Abstract, rarely used in the field of artificial intelligence.

Lisp

Lisp is a high-level functional programming language that is well-suited for artificial intelligence development due to its flexibility and ability to process symbolic expressions. These expressions can contain both simple data types (such as strings or numbers) and complex structures like lists and arrays. This makes Lisp effective in working with large data sets, performing complex computations and manipulations.

The recursive programming mechanism allows for the creation of powerful algorithms for analyzing and processing significant volumes of information. Additionally, automatic garbage collection enables programmers to delegate memory management to the system, focusing on developing core functionality.

Lisp's dynamic typing allows the interpreter to determine variable types during program execution. This provides high flexibility and makes it easy to work with complex data structures, which is important for artificial intelligence tasks.

Lisp is one of the first AI programming languages to support functional programming. Its simple yet powerful syntax structure encourages experimentation and research in AI, especially in areas that require symbolic expression processing.

Best suited for: Scientific research and experimentation in the field of artificial intelligence, particularly for working with symbolic expressions.

Although Lisp was once an innovative language in artificial intelligence, over time it has given way to statistical machine learning and neural networks, which currently dominate the field. However, it remains indispensable for developing expert systems and logical decision-making models based on symbolic reasoning rather than data analysis.

Prolog

Prolog is known for its capabilities in logical programming and symbolic reasoning, making it an effective tool for creating AI solutions, particularly for knowledge representation and solving complex problems.

Thanks to its declarative approach, developers can describe the logic of computations without focusing on the details of execution flow management. This allows for easy specification of rules, relationships, and facts, which is especially useful when creating knowledge bases in artificial intelligence.

The powerful pattern-matching mechanism enables quick analysis of large amounts of information and finding matches based on logical relationships. This makes Prolog a valuable tool in areas such as natural language processing (NLP), helping systems interpret and respond to queries. Additionally, this feature supports the development of various algorithmic solutions.

Best suited for: Symbolic reasoning, knowledge representation, and solving logical problems in AI.

Weakness: Limited usage and a small number of libraries.

What Is the Most Popular Programming Language Used in AI in 2025?

Considering the top programming languages for artificial intelligence, it is worth noting that in 2025, Python remains the most popular. Its popularity is driven by its easy-to-learn syntax and the wide range of libraries and frameworks, such as TensorFlow, PyTorch, and Keras, which significantly simplify the AI development process.

In addition to Python, other languages are also actively used in AI. These include R, known for its powerful statistical analysis and data visualization tools; Java, which offers high performance and scalability; C++, which provides low-level control over hardware; and Julia, which is quickly gaining popularity for its efficiency in numerical computations.

The choice of programming language for AI depends on the specific requirements of the project, available tools, and the personal preferences of the developer.

The Programming Languages That Are Not Recommended for AI

Along with the extensive list of languages for AI development, there are also those that are better used for solving other problems. These are usually niche and low-level languages, such as PHP and Ruby (scripting languages that do not have the needed flexibility and speed to simplify the development process), C (a low-level language that lacks almost all the functions needed in AI development), and Fortran (despite its use for mostly scientific purposes, it has poor functionality).

Ultimately, a seasoned software engineer will be able to cope with any AI development task using any language; another thing is that, given the expected scope of work, the languages that we described in the previous paragraph are much better suited for these tasks.

If you are looking for an experienced team that will launch the digital transformation of your business processes through custom-made AI and ML solutions, feel free to contact us.

Choosing the Best Programming Language for AI Development

From Python to Scala, different programming languages can be used for AI projects. While they share some similarities, each one has unique characteristics that make it optimal for specific tasks.

Here are a few steps that will help you choose the best programming language for your next AI project:

For Your Project

Start by assessing the requirements and needs of your AI project. Pay attention to the structure of the application and the main features it needs to function properly.

Determine whether you are building a web application, a deep learning model, or a robotics project. Different types of projects may require different programming languages.

For example, JavaScript may be suitable for building web applications, such as those related to trading, as it supports many browser features by default. In contrast, C++ would be the optimal choice for robotics development, as it provides low-level hardware integration.

To Learn

Consider your skill level, as programming languages have different learning curves. For instance, Python has a gentle learning curve thanks to its simple and understandable syntax. On the other hand, C++ has a steeper learning curve, meaning it requires more effort to master.

By taking the time to carefully assess these factors, rather than rushing, you will lay a solid foundation for your AI project. Proper planning at the start will help avoid costly problems in the future. Additionally, it will increase your chances of choosing a programming language that best suits both your current needs and long-term goals.

The mobile apps market is rapidly growing. According to Grand View Research, the global mobile app market was evaluated at approximately USD 253 billion in 2023 and is going to grow with a 14.3% CAGR by 2030. That’s why the need for qualified mobile development specialists is so relevant today. 

At the same time, one of the key features of a competent specialist is knowledge of the software development life cycle (SDLC) and which of the existing models is best to use in a specific project. The following article will overview the most popular SDLC models and explain where to apply them for the best results. 

What is the Software Development Life Cycle (SDLC)?

The software development lifecycle is a methodology that guides the development team during the app creation process. Each of the development life cycle phases has its aims, timeframes, and challenges. Yet, a systemized approach toward software development ensures that the final product is of the highest quality. 

In comparison, there are other approaches to software development. For example, development teams can choose from the following options: 

• Application lifecycle management. It has a broader scope outside of the actual development process. It can include several SLDC development life cycle stages inside. 
• Software testing lifecycle. This method emphasizes debugging and elimination of possible errors in the product. 
• Product development lifecycle. This strategy includes market and product promotion after its development is complete. 

What Is the Purpose of the Software Development Life Cycle (SDLC)?

The SDLC’s clear division into development cycle stages ensures that software is developed in a controlled environment. The systematic approach helps to avoid such challenges as: 

• Budget breaches.
• Project overruns and crunches 
• Failure to meet the requirements 
• Expired deadlines

Creative freedom is important during the software development process. Yet, the SDLC approach ensures that all the creativity and revolutionary solutions are channeled constructively. 

While following the life cycle of software development, each team member knows what task they should do and its deadline. 

What are the 7 Phases of SDLC? 

There are seven common SDLC phases: 

1. Requirements analysis 
2. Planning
3. Design 
4. Development
5. Testing
6. Deployment
7. Maintenance

Let’s consider them in more detail.

Requirements Analysis 

It is the initial stage at which the developer's team evaluates the required features for the app. It defines how long the app will be in development, the budget’s size, and the deadlines. 

This step answers such questions as: 

• What is the objective of the app? 
• Is it an upgrade or a brand-new software? 
• What are the expected results? 

Planning 

The project can be planned after its goals and desired results are established. Planning in SDLC defines the technological stack and project roles. The project roadmap is also developed during the planning phase of SDLC. 

Design 

The software blueprint is created during the design stage. Ideas turn into features, architecture, and UI/UX design. Designing also includes consideration of the software's platforms and security as a part of SDLC processes. 

Design can undergo several iterations before reaching its final look. During this phase, teams usually create a software prototype to see how all the features work together. This phase concludes with design document specification — a text description of the app’s functionality to share with stakeholders and within the team. 

Development 

Development is the long-lasting of the SDLC steps. During this stage, the team implements planned features and functionality into actual software. Tools for progress tracking and version control, such as Git, are common in the software development stage.

Testing 

Testing comes after the implementation phase in SDLC. It is a rigorous process during which all software elements are evaluated. Testing can include: 

• Performance testing
• Security testing
• Integration testing
• Alpha and beta testing, and so on 

The result of the testing and debugging software development cycle phases should be a product meets its specifications and is ready for the commercial environment. 

Deployment 

Usually, the apps undergo testing and development in a secluded, special environment. Deployment implies transferring the software to the commercial space. The deployment phase includes final testing of the working product and gathering feedback from a limited audience. 

The product is ready for full-fledged commercial use when the software development stages are over. 

Maintenance 

Maintenance is the final stage of the SDLC cycle. It includes software updates, security, and performance optimization. An upgrade or upscale of the software may be required in the future. In this case, the SDLC begins from the first stage, and the major update undergoes all the steps mentioned. 

Most Common SDLC Models 

The SDLC standards are common across the industry and development teams. However, there are different SDLC models. Each model serves its own purpose and offers a different order in which the different phases are performed.

SDLC Agile Model 

The Agile SDLC model is the hallmark of modern industry. This model implies dividing the bigger tasks into smaller chunks and working on their implementation. The Agile method emphasizes communication between cross-functional teams and stakeholders. 

Agile also implies frequent feedback gathering and analysis. Rigorous quality control is also an integral part of the Agile model. 

SDLC Big Bang Model

The Big Bang model is an almost informal approach to the SDLC cycle. It ignores any planning or assessment and jumps straight into the coding. Such an approach is more risky than others. However, the model works best if delivery time is critical. 

The Big Bang model can work for you if you wish to experiment or test waters in new markets. 

SDLC DevOps Model

DevOps is a combination of development (Dev) and operations (Ops) teams to streamline the delivery of software products. The combined practices require service providers of highly skilled specialists. The main DevOps principles are continuous integration and delivery at the enterprise software development level. These methods help automate the process and streamline management tasks. 

The DevOps model can strain the budget. However, the result is worth the cost. The DevOps SDLC cycle ensures that the product is in a deployable state and can be swiftly modified. 

SDLC Iterative Model

An Iterative model is another representative SDLC cycle. Unlike the Agile model, iterative SDLC does not require continuous customer involvement. Besides, the iterative model has a more defined structure. 

The Iterative model implies iterating the software and making changes between the cycles. Such an approach works well for expanded teams and allows the desired results to be achieved without failing deadlines or breaching the budget. 

SDLC Lean Model

The Lean model is one of the oldest SDLC cycles that has persisted until today. It aims to achieve maximum efficiency in resource management. Unlike Agile, it focuses on the highest possible quality instead of close and consistent communication with the client. 

It also takes a broader than Iterative model approach and improves the overall development process rather than the product itself. Lean is more philosophy than a precise model. As an SDLC model, Lean will ensure the quality of the product and client satisfaction before everything else. 

SDLC Spiral Model

The Spiral model is a combination of Iterative and Waterfall models (this one will be discussed below), taking the best features from both approaches. The model consists of spirals, and each spiral has the following steps: 

• Planning 
• Risk analysis 
• Development 
• Testing

Such an approach allows risk mitigation at the early stage of the project. While being time-consuming, the Spiral model can be a fail-proof net for your project. 

SDLC Waterfall Model

If you are looking for something straightforward and easy to manage – the Waterfall model is your choice. It takes the SDLC structure at face value and follows each step consecutively. The Waterfall model has its risks. It is rigid, and if changes or feedback appear in later stages, it will be hard to implement them. At the same time, the Waterfall model struggles with scalability. 

On the other hand, it can spare the headache of complex management and task delegation. 

Conclusion 

Software development is a complex process that requires structure and precision. The Software Development Lifecycle provides such a structure. In seven steps, a software development team can clearly see goals and meet the client’s requirements. 

Various SDLC models fit different needs and project scopes, and we know how to work with all of them. If you need specialists for custom software solutions, we are ready to answer your call and create a high-quality product. Contact us to discuss your project in detail.