The 10 Best AI Tools for Software Developers in 2025

The best AI tools for developers allow the top talent to perfect performance, automate repetitive tasks, and improve software quality while allowing entry-level programmers to refine their skills when developing unfamiliar software. Developer tools can also write documentation and track bugs.

Various AI tools for developers can streamline the software development process, reducing time, saving money, and achieving a faster time to market. Discover the benefits of AI tools for developers, the best AI tools available, and how each one assists programmers in software development.

How AI Tools Are Used in Software Development

Artificial intelligence (AI) is redefining the software development landscape and allows developers to create simple applications without an entire development team. Sre Chakra Yedulla even shares how he built and published a generative AI-powered mobile app using AI developer tools.

Meanwhile, a GitHub survey showed how 92% of developers use publicly available AI tools to code personal projects. Some developers are writing code, using an editor to compile the code, and implementing an AI assistant to autocomplete code. AI tools for developers are changing the game.

Common Use Cases and Benefits of AI-Powered Tools in Software Development

A 2024 McKinsey State of AI survey also found that 67% of companies plan to invest more in AI, making AI tools for developers an invaluable asset. Programmers proficient in the best AI tools will stand out among the developers available to these businesses.

Here are more ways developers use AI tools in the software development lifecycle (SDLC):

• Architecture Design: AI uses recommended designs based on project requirements and software development best practices. Neural networks can analyse vast datasets to recommend the best solution architecture for any software application.
• Automated Testing: AI tools can generate test cases from user stories to optimise testing, covering more scenarios than developers can cover manually. AI-powered tools also optimise tests to save time spent and resources, which ensures a faster time to market.
• Bug Detection and Fixing: Developers use the tools for the faster detection of bugs, to analyse patterns to identify future bugs and defects, and to use autocorrect suggestions based on real-time code data for automated debugging.
• Code Generation: AI tools suggest code completion or generate entire functions based on natural language inputs and prompts. These AI-powered tools have code autocompletion to suggest lines of code while using code synthesis to complete functions based on descriptions.
• DevOps CI/CD Pipelines: AI can enhance tasks like monitoring or scaling CI/CD pipelines, which permits DevOps engineers to improve build efficiency and speed. AI features like intelligent monitoring detects real-time issues and automation handles load balancing and scaling.
• Documentation: Generative AI tools use natural language processing to generate or maintain documentation across the lifecycle, whether developers must document APIs, security protocols, or even errors and vulnerabilities. The features include auto-translation for multiple languages.
• Optimally Designed UX: AI-powered tools automate user interface designs for enhanced user experiences based on user data and behavioural patterns. The tools can tailor the user experience to individual needs and use an A/B test to determine which design attracts more users.
• Project Management: The tools can automate resource management and scheduling to create smooth deadlines for software development projects. The tools handle routine project management tasks like resource allocation and use auto timeline estimation features.
• Refactoring/Optimisation: AI can suggest improvements based on computer science best practices to improve performance and functionality, reducing the need for extensive maintenance. The tool conducts code analysis to improve code efficiency.
• Security Enhancements: AI-powered tools can quickly identify security vulnerabilities using threat detection automation, audit code for secure upgrades, and monitor code to detect new threats using structured threat query languages (SQLi) or cross-site scripting (XSS).

How We Chose the Best AI Tools for AI Development Projects

The key to any team getting the most out of an AI assistant is ensuring it can save time, automate tasks, generate high-quality code, use various programming languages, and access machine learning algorithms. The best AI tools for developers improve productivity and efficiency.

A team must be able to generate code faster to spare time, initially gain access to a free version (albeit some models don’t allow this), or help and support the software development workflow. The best tools also create a learning opportunity for new developers but offer a variety.

The 10 Best AI Tools for Software Developers in 2025

The leading AI tools for developers include a range, ensuring that engineers have access to every different type that can enhance the code quality with a coding assistant, make code suggestions based on code analysis, and automate various other reviewing, debugging, and coding tasks.

1. Amazon CodeWhisperer (All-in-One Coding Assistant and Editor)

Amazon CodeWhisperer is the ideal tool developers can use to automate tasks like documentation, reviews, reports, and analysis. It generates real-time code in the integrated development environment based on comments and existing code entered into the system.

Amazon CodeWhisperer also lets developers scan for security vulnerabilities and choose from many programming languages, including C#, Java, TypeScript, Python, PHP, Go, and Rust. Amazon has another IDE tool for developers called Amazon Q Developer, which is similar to CodeWhisperer.

Amazon’s CodeWhisperer Pros and Cons

Here are the top three features of Amazon CodeWhisperer’s coding assistant:

• The system anticipates how developers will finish a line of code based on specific comments.
• Leverage a powerful and advanced security ability to scan code for bugs quickly and efficiently.
• It has a built-in reference tracker to detect output that matches certain open-source trained data.

Here are the disadvantages of using CodeWhisperer tools:

• The built-in suggestions aren’t of high quality for a new developer to try to validate.
• It has limited tools and search features from repositories for the commands or comments.
• The platform lacks a chat window and the ability to access specific code explanations.

CodeWhisperer Pricing

Free for individual developers with premium monthly plans starting at $19 per month per user.

2. Cursor IDE (AI-Powered Code Editor)

The Cursor platform is an agentic AI assistant that works seamlessly with many other integrated development environments (IDEs) with a large selection of tools, including an intelligent code assistant, a powerful code editor, AI code completion, reports, and natural language processing.

Cursor can even combine effortlessly with the Visual Studio Code framework to integrate advanced AI capabilities like smart code optimisation or reports, terminal integration, and a real-time approach to the prevention of errors. The multi-language ecosystem uses Python, JavaScript, and TypeScript.

Cursor IDE Pros and Cons

Here are the top three benefits of using the popular Cursor tools for developers:

• The powerful code editor has the ability to scan and anticipate errors in multiple files and lines.
• The code generation capabilities are designed for general-purpose large language models (LLMs).
• Leverage the platform as a fork of VS code and integrate it with other IDEs like ChatGPT 4.

Here are possible setbacks when using Cursor IDEs:

• Cursor has the potential to lock search or technical issues in multi-user environments.
• The machine learning model isn’t well-known for good technical performance and reports.
• The search suggestions can be unstructured to lack understanding for validation.

Cursor Pricing

A free limited plan is available with premium plans starting at $20 per user per month.

3. Google Gemini Code Assist (Advanced AI Coding Assistant)

Google Gemini Code Assist can help developers generate high-quality code while integrating seamlessly with multiple well-known IDEs to ensure seamless collaboration. The Google Cloud Platform, which has AI coding tools, also lets developers use code completion tools.

The AI coding plugin can generate code, conduct code analysis, and even review boilerplate code. The web or desktop plugin will recommend improvements and code completion in real-time and uses multiple languages like Python, JavaScript, Java, C, C++, PHP, Go, and SQL.

Google Gemini Code Assist Pros and Cons

The top three benefits of using the cloud-based AI approach include:

• Seamlessly integrating the platform with IDEs like PyCharm and VS Code.
• Access to code refactoring suggestions to improve efficiency, minimising maintenance needs.
• Providing clear code explanations of complex lines and segments in the whole codebase.

Some disadvantages of Google’s cloud-based coding include:

• The code quality may not always meet the highest standards, even providing sub-par reports.
• The tool trained on large datasets could lack context-aware code and may need more functions.
• The AI models have required initial experience and understanding from developers.

Google Gemini Code Assist Pricing

An individual developer can work with a free plan with pro plans starting at $19 per client per month.

4. GitHub Copilot (Custom AI Code Editor)

GitHub Copilot is a comprehensive and customisable code editor that integrates seamlessly with VS Code, Jetbrains, and Neovim IDEs. For example, the AI coding tool lets software engineers to automatically complete coding tasks like advanced code completion and intelligent code generation.

GitHub Copilot makes context-aware recommendations, suggests entire code blocks, and stands above the rest with an interactive chat interface with natural language processing and built-in test case generation. The platform uses relevant languages like Python, Ruby, C++, and Rust.

GitHub Copilot Pros and Cons

GitHub Copilot has numerous advantages with the top three features being:

• Add an easy work request or natural English coding questions in the user-friendly interface.
• Leverage AI-driven code reviews to identify less visible errors, enabling effective test cases.
• Seamless creation of docs and integration with repositories or Visual Code Stution and JetBrains.

Despite the popular GitHub platform advantages, here are some functionality disadvantages:

• The full GitHub platform is usable as a website to work on numerous files on a desktop only.
• The GitHub technology requires specific and clear codebase requests within the command line.
• Manual validation is required to ensure accurate code completion and contextual data.

GitHub Copilot Pricing

Individuals can work with it for free while a detailed professional plan starts at $19 per user per month.

5. GitLab Duo (AI-Driven DevSecOps Platform)

Gitlab Duo is an AI coding assistant and web plugin that partners with Q Developer for AI-powered DevSecOps. Both programs are required, but the combination permits developers to approach a unique selection of development tools to enhance security while using continuous improvement.

The system on its own automatically helps engineers with the integration of detailed CI/CD pipelines, source code generation and management, and detailed compliance and governance solutions. The extensive tools use programming languages like C, C#, C++, Java, Python, Go, and Kotlin.

Gitlab Duo Pros and Cons

It has the following benefits for software engineers within the application realm:

• A comprehensive DevSecOps platform with automation tools and features like merge requests.
• Smart code suggestions with natural language explanations and automatic test generation tools.
• Streamline code reviews and track development metrics with an easy user interface dashboard.

Despite the great help and features, some existing disadvantages include:

• The large language model (LLM) may generate nonsense code that causes more problems.
• Required strict adherence to designs for code completion and reviews.
• The chat interface sometimes has unresponsive problems and leaves a team on read.

Gitlab Duo Pricing

Individual developer plans are free while premium plans are much more at $29 per user per month.

6. OpenAI Chat GPT 4 (Natural Language Processing – NLP)

OpenAI Chat GPT 4 is one of the best-known generative AI tools that replaced GPT 3.5, and the GPT functionality isn’t only available to streamline the workflow for writers. The open-source OpenAI Chat GPT 4 tools for engineers can generate code using natural language processing.

The trained OpenAI Chat GPT 4 AI coding assistant can help with AI-powered recommendations dependent on the user-friendly interface and image creation options to optimise the development of an enterprise application. ChatGPT is one of the best tools that can write code in HTML and Python.

ChatPGT Pros and Cons

Here are some advantages of using the OpenAI ChatGPT-trained machine learning model:

• Enhance developer productivity with templates and code generation for code snippets.
• Create tailored test cases with effective and custom prompts and comments to reduce problems.
• Prompts can also assist in creating code documentation and software reviews.

Here are potential setbacks of ChatGPT’s AI coding assistant:

• The improper validation of code could directly lead to product bugs and problems.
• The text-formatting capabilities are lacking across tasks with a communication focus.
• The information is often outdated or inaccurate as even a writer notices with ChatGPT.

ChatGPT Pricing

Chat GPT 3.5 is free while Chat GPT 4 with more features is much more at $20 per month per user.

7. OpenAI Codex (Natural Language Translation and API Integration)

OpenAI Codex is an open-source platform that provides AI coding assistance with new capabilities like seamless and secure API integration and natural language processing to recommend code snippets dependent on text. GitHub Copilot engineers can also integrate the environment into OpenAI Codex.

Team collaboration and auto tasks are simple enough with the GPT 3 descendant with the ability to process language into text from numerous repositories. The platform has a strong focus on Python but also uses programming languages like JavaScript, Pearl, PHP, and Go to help a team develop an app.

OpenAI Codex Pros and Cons

Here are the key advantages of the right team directly using OpenAI Codex technology:

• It can help developers write code for an app using context and simple natural language questions.
• Codex can support compliance and has a range of tools to identify product security vulnerabilities.
• The AI assistant lets engineers automatically adjust any workflow to enhance productivity.

Here are some key disadvantages of using the Codex platform to suggest application information:

• Programmers find that the website isn’t transparent about the decision-making process.
• It has a steep learning curve for new software engineers, significantly causing technical debt.
• It can’t quickly identify issues in a large or complex type of codebase to support effortless reviews.

OpenAI Codex Pricing

A small team can use limited features for free but standard pricing plans start at $10 per month.

8. Qodo.ai (Codium AI – Test-Writing Assistant)

Qodo.ai (formerly Codium) is ideal for the integration of VS code to improve the software development process. The tool for engineers conducts analysis on code, docstrings, and comments as programmers code to choose the most relevant test suggestions. It also offers suggestions on code changes.

The Agentic AI tool also allows developers to generate code snippets using different programming languages like CSS, Python, Java, JavaScript, Ruby, Go, PHP, C#, and Swift. Codium is a coding assistant that welcomes seamless code documentation, code reviews, and code generation.

Qodo.ai Pros and Cons

Here are the top three advantages of using Codium in an enterprise or community development project:

• The platform has impressive features, such as code review, analysis, and test automation.
• The Codium tool also works as a popular IDE that integrates with JetBrains and IntelliJ IDEA.
• Streamline pull requests, API integration, and context-aware code to reduce technical debt.

Here are some disadvantages of using Codium:

• Suggests irrelevant code that doesn’t fit the context occasionally and has some unusual responses.
• Programmers find that it sometimes generates overly complex code that isn’t always accurate.
• It only supports some Git platforms and IDEs and significantly reduced community opportunities.

Qodo.ai Pricing

An individual plan is available, but a professional plan starts at $19 per user per month.

9. StepSize AI (Streamlined Code Debugging)

StepSize AI is a code debugging AI-powered tool that allows developers to retrieve AI-generated suggestions through static analysis tasks and a comprehensive review process. StepSize AI is efficient and faster than most AI models to identify bugs and reduce errors in source code.

StepSize AI coding features and capabilities automatically include project management, time-saving workflow organisation, team collaboration channels, code documentation, and support services. The platform conducts code analysis and reviews for Python, TypeScript, Go, Java, and JavaScript.

StepSize AI Pros and Cons

Here are the main advantages of using StepSize AI-powered code debugging:

• StepSize AI is ideal for writing sprint reports for scrum masters implementing DevOps.
• The platform ensures excellent security with a new model designed for context-aware code.
• The collaboration potential is seamless as the AI model integrates with Jira and Linear.

Here are some downsides of the StepSize AI platform’s capabilities and accuracy:

• The development team will find a steep learning curve with complex responses and feedback.
• The integration of StepSize AI tech into an existing system or website is still challenging.
• Technological debt is one of the issues when buying smaller packages for larger tech codebases.

StepSize AI Pricing

It has no free plan but starts at $29 per month per Jira board or Linear team.

10. Tabnine (Custom Code Completion and Prediction)

Tabnine is an AI-powered model that can help developers complete and predict complex code using custom prompts and comments in the command line. Tabnine uses a machine learning algorithm to learn from a developer’s previously written code to make future predictions, even contextual code.

Tabnine has a range of AI-powered code completion and prediction features but also allows a team to commit to improving productivity and particularly streamlining a new app development process. The Tabnine platform works with JavaScript, Python, HTML, C#, C++, C, Go, TypeScript, and Java.

Tabnine Pros and Cons

Three key advantages of a team using the web-dependent Tabnine platform for developed apps include:

• The Tabnine platform can use machine learning to suggest code snippets or complex classes.
• The platform also has code reviews and code refactoring for efficient and readable code quality.
• It can support code privacy or integrate with Visual Studio Code, IntelliJ IDEA, and Sublime Text.

Some key disadvantages of a team using Tabnine for mobile or web development include:

• The web platform has high memory usage and requires many resources, even if it’s easy to use.
• Performance issues may still arise on larger codebases, particularly in complex code classes.
• Tabnine uses a proprietary LLM, unlike the Copilot type, with higher accuracy.

Tabnine Pricing

Tabnine has a free plan with professional plans that start at $12 per user per month.

Partner With Local Expert AI Engineers for Seamless Coding

Ultimately, partnering with artificial intelligence consultants with the right expertise may solve the conundrum of which tool to implement. Our development teams implement integrate AI and automation and machine learning app development algorithms to create a seamless process and save time.

Any of our best tools for software developers would create easy and seamless code suggestions and implement various programming languages including Python and JavaScript, enabling the tool to help developers deliver AI products faster and more efficiently.

For example, using AI and machine learning models will streamline the entire software development process, even helping programmers use the latest coding assistant to ensure the ultimate software development strategy. Hire AI developers from our teams to use any popular toolkit or model.

Hire dedicated software development teams to focus on your development projects until complete, or use our outsourcing software development services for code changes, automatic coding tasks, or high-quality code optimisation. But first, contact us to discuss your AI projects today.

Best AI Tools for Software Developers Conclusion

The best features, assistance, and automation capabilities lie within the top tools for programmers who wish to reduce development time, save costs, and provide high-quality code to clients. AI-generated code may have issues but often requires menial validation and reviews.

Opt to download a free model that lets you test the platform, familiarise yourself with the features, and retrieve test suggestions with static analysis. Choose the best AI features or integration capabilities, and generate excellence. But first, contact us today to discuss AI capabilities and projects.

AI Tools for Software Development FAQs

What are some challenges of using AI and ML tools?

Some AI tools are easy to use for the first developed application. However, other tools add a touch of technicalities that entry-level developers may not grasp. The perfect AI development tool should be easy to use, providing integration with other IDEs with similar features.

Yet, even the guide for some AI tools already has extensive writing instructions and comment aspects that challenge a developer when they try to optimise code or add the latest tech files. Additionally, some tools are only available on desktops and provide no free model to try before buying them.

Writing code is already challenging for new software developers, and a single feature can make it much more difficult. Focus on redefining the way programmers write code with insights from machine learning algorithms that require less expertise, especially for faster development with less experience.

How do I choose the right AI tools for software development?

Understanding the different aspects, features, and capabilities of each AI model will help engineers choose the right tool for development projects. A 10 min-read explaining the pros and cons with the pricing plans ensures programmers know which tool to download and integrate with existing systems. Consider the pricing and features included in the plan before buying specific solutions.

Review the best development tools above to comprehend which features will best serve your development needs, whether programmers need access to multiple programming languages, code analysis, autocompletion, or pull requests. Alternatively, speak to expert artificial intelligence consultants to determine which tool for developers will better serve your needs without issues.

What other AI tools for developers exist without issues?

Various AI models and tools exist to assist programmers in creating web, desktop, or mobile applications, especially with a growing need for improved productivity and automation without hassles. Here are more AI development tools with a brief explanation of the type:

• Bugasura offers code debugging and editing tools that create seamless reports with a list of docs.
• Claude AI is ideal for documentation, reviews, and coding using machine learning assistance.
• Cody AI provides access to a list of AI writing tools, vast assistance features, and plugin options.
• Figstack is another documentation tool to get with various web or website development features.
• Grit.io offers an AI-powered code migration tool with a free version that individual users can get.
• Hugging Face is an open-source artificial intelligence tool used for natural language processing.
• Mintlify is a code documentation and generation assistant to try with complex code insights.
• Mutable AI is another one of the brilliant AI coding tools with a free version and various features.
• Pieces for Developers is a free-to-get tool with code generation and review capabilities.
• Polycoder AI offers a free version to try with the ability to generate code conduct reviews.
• Replit Ghostwriter is an online IDE enabling engineers to create an app quickly from an idea.
• Snyk offers a more expensive cloud platform designed for larger development teams.
• Sourcery offers an AI coding assistant with a strong focus on developing clean code efficiently.
• What the Diff is another AI-powered editing platform with a deep understanding of English text.

Which AI tools for developers aren’t good for writing code or optimising files?

Some AI tools for developers don’t have positive feedback yet, including Supermaven, Claude AI, Devin AI, GPT Pilot by Pythagora, MetaGPT, Gorilla, AI Code Review Action, and Korbit. Developer feedback typically shares negative insights from these close or open-source artificial intelligence tools.