Best AI Tools for Developers
Developers were among the first ones to adopt AI tools, helping them code faster. In fact, some of the AI-powered development tools can generate multiple lines of code and entire functions — all you have to do is ask the AI in plain English.
These tools can work as autocorrect for code writing, completing the code as you type, or you can use them for code review and quality analysis.
Also, they can help with bug detection and fixing, as well as testing.
Finally, it’s worth adding that most of these tools enable multiple developers to work on the same project, bringing automation across teams. And that could lead to drastic increases in efficiency. Here are some of the best AI tools for developers:
GitHub Copilot is an AI-powered coding assistant that integrates into IDEs and GitHub, offering code completions, chat support, and agent-driven automation to streamline coding. Built by GitHub with OpenAI and Microsoft, it’s trained on public code and text, delivering context-aware suggestions that (dramatically) boost productivity. For instance, developers at companies like Shopify and Mercado Libre use it to cut through repetitive tasks, with studies showing up to 55% faster coding without quality loss. It supports Visual Studio Code, JetBrains, and GitHub’s own platform (obviously), making it versatile for solo coders and teams alike.
The tool’s strength lies in its features. Next Edit Suggestions tracks how code changes affect your project, ensuring consistency across files. The agent mode handles issues by planning, coding, and testing, producing pull requests via GitHub Actions. Copilot Spaces organizes project context, like docs and code, for tailored suggestions. The chat interface, powered by models like GPT-5 and Claude Opus 4.1, answers coding questions or explains code in real time. Code review catches bugs and vulnerabilities before submission, a feature that rivals like JetBrains’ AI Assistant don’t match in scope.
Compared to competitors, Copilot shines for GitHub users. Tabnine offers similar completions but lacks the agent mode and deep platform integration. Copilot’s free tier includes 2,000 completions and 50 chats monthly, while Pro and Pro+ plans unlock unlimited use and premium models. Pricing feels competitive, especially for teams already on GitHub, though enterprise plans add customization for larger organizations.
The tool isn’t perfect. Suggestions for niche languages like Rust can be less accurate due to limited training data, as noted in recent Reddit threads. The free tier’s limits may frustrate heavy users, and the chat interface requires some practice to use effectively. Some developers on X report occasional irrelevant suggestions, which can disrupt flow. Still, the code referencing filter helps mitigate risks of matching public code, a concern GitHub pegs at under 1% of suggestions.
A standout feature is Autofix, part of GitHub Advanced Security, which flags vulnerabilities like SQL injections with fix suggestions. This adds real value for security-conscious teams. Copilot’s ability to switch between models for speed or depth is another plus, though non-English prompts may yield weaker results due to English-heavy training data.
To get started, try the free tier to test compatibility with your workflow. Enable the code referencing filter for peace of mind, and use Copilot Spaces to boost suggestion accuracy. Experiment with the chat feature for quick code explanations, but always review suggestions manually. For teams, explore the Enterprise plan for codebase indexing. Copilot is a powerful ally, just keep your coding instincts sharp.
Replit AI by Replit, Inc.
👍 Pros
👎 Cons
- Code completion alone can save a ton of time
- And the same goes for code generation, which could be very handy for many standard functions
- Chat and code explain features are extremely handy for beginners
- Not the only game in town, with GitHub users mostly sticking to GitHub Copilot
Replit is a cloud-based IDE that integrates AI agents to build, test, and deploy apps from natural language prompts. It supports over 50 programming languages and emphasizes real-time collaboration. The platform handles environment setup automatically, allowing users to focus on ideas rather than infrastructure.
Its key feature comes in the “form” of Agent 3, which can process prompts, search the web, and iterate on code using a reflection loop for self-testing. This is meant to reduce manual debugging by generating reports on issues and applying fixes. There is also the Visual Editor, which is able to import Figma designs for direct UI adjustments to streamline frontend work.
Built-in Database and Auth provide secure backend services, integrating with tools like Stripe and OpenAI without exposing keys. Agent Generation creates custom automations, such as Slack bots, from descriptions. Security options like SSO, SOC 2 compliance, and private deployments suit enterprise needs.
In comparison, Replit offers more end-to-end functionality than Cursor, which excels in local editing but requires separate deployment. Versus GitHub Copilot, Replit provides a full browser environment, while Copilot focuses on IDE plugins. Windsurf suits quick prototypes, but Replit adds robust collaboration. Lovable emphasizes no-code, differing from Replits hybrid approach.
Users appreciate the instant deployment and multiplayer editing, which speed up team workflows. However, the AI can alter code unexpectedly, leading to bugs in complex projects (this is true for all AI coding tools).
The platform’s testing via browser simulations catches UI errors early, a practical edge over manual checks in rivals. Recent updates enhance plan mode with auto-saved checkpoints and build score feedback for AI improvement.
For best results, start with small prompts, review the generated code step-by-step, and utilize collaboration for refinements. This approach maximizes efficiency while minimizing surprises.
Amazon Q Developer
👍 Pros
👎 Cons
- Supports all the popular programming languages and IDEs
- Can scan your code for security vulnerabilities
- Generous free individual tier (free) with unlimited code suggestions, reference tracking, and 50 security scans per month
- There are other great AI coding assistants out there - is Amazon Q Developer the best for you?
Formerly called Amazon CodeWhisperer, Amazon Q Developer is a generative AI assistant from AWS that enhances software development through code generation, task automation, and AWS expertise.
It provides real-time code suggestions in IDEs like VS Code and JetBrains. Suggestions range from cover snippets to full functions, based on comments and code context. Inline chat allows direct questions in the editor.
Agentic capabilities enable autonomous execution of complex tasks. These include feature implementation, unit testing, documentation, code reviews, refactoring, and upgrades. Agents read and write files, generate diffs, run commands, and incorporate user feedback.
AWS integration offers guidance in the console, Teams, and Slack. It optimizes costs, troubleshoots incidents, diagnoses networking, and follows well-architected patterns. Data tasks involve natural language queries, pipeline coding, and ML model design with governance.
Security features scan vulnerabilities across languages and suggest fixes. Customization connects to private repositories for relevant recommendations and codebase queries. CLI support includes autocompletions and bash translation.
Competitors include GitHub Copilot for suggestions, Tabnine for privacy focused completion, and Cody for codebase search. Amazon Q leads in agentic tasks and AWS depth. The Free Tier offers 50 chats and 1,000 lines per month, with usage-based pricing thereafter.
Test in a small project to gauge fit before full adoption.
BlackBox AI by Course Connect
👍 Pros
👎 Cons
- Support for 20+ programming languages
- Search across 100M+ open source code repos
- Quickly turn any question into code
- Only the Legendary plan includes web-based IDE
- All plans are yearly (though there is free trial)
BlackBox AI is an AI coding assistant that boosts developer productivity through real-time code suggestions and autonomous agents. It integrates with over 30 IDEs, including VS Code and JetBrains, supporting multiple languages ranging from Python to JavaScript. The tool accesses over 300 AI models, enabling users to select the best fit for tasks such as debugging or app development.
Key features include smart autocompletion that predicts code based on context, reducing typing time by up to 55 percent according to user benchmarks. The autonomous coding agents operate in the cloud, handling full projects asynchronously, running tests, and notifying users upon completion. Voice interaction enables natural language commands for explanations or modifications, making it accessible even for those with basic experience.
In comparison, BlackBox AI offers broader model variety than GitHub Copilot, which relies heavily on OpenAI for inline predictions but limits flexibility in non-Microsoft environments. Against Amazon Q Developer, it provides lighter pricing for individuals while matching enterprise security through encryption and audits. Tabnine stands out for local processing to enhance privacy, yet BlackBox’s cloud agents excel in scalability for team workflows.
Users appreciate the image-to-code converter for turning designs into functional components, though it may require adjustments for intricate layouts. The community snippet library facilitates quick implementations, but occasionally, outdated entries require verification. Free access covers basic needs, with pro tiers unlocking unlimited queries and advanced agents at a cost-effective rate compared to competitors’ subscriptions.
Potential drawbacks involve latency during high loads and overengineering in agent outputs for simple fixes. Despite this, integration with tools like Figma streamlines prototyping. Recent updates in 2025 improved multi-file editing, enhancing reliability for larger codebases.
For practical use, start by installing the IDE extension to test autocompletion on routine tasks. Experiment with voice for debugging sessions, review agent results thoroughly, and combine with version control to maintain code quality. This approach maximizes efficiency while minimizing errors.
Tabnine
👍 Pros
👎 Cons
- Can generate entire functions within your favorite editor
- You can run it on your own machine (privacy friendly)
- Major companies love it: LG, Nike, Amazon, Bloomberg, etc.
- Some developers prefer Copilot
Tabnine is an AI platform for code assistance, offering completions, specialized agents, and deployment options, including air-gapped solutions for enhanced security.
It integrates into IDEs such as VS Code, IntelliJ, and others. Completions use whole-line predictions based on current files, open tabs, and repository context. Agents cover SDLC stages: Code Review Agent checks PRs against standards, Jira agents implement and validate issues, while Testing Agent builds cases from existing tests.
Deployment choices include SaaS, VPC, on-prem, and air-gapped. Enterprise controls enable per-user LLM access, set budget thresholds, and track usage metrics. It integrates with Git, Jira, and Confluence to provide organizational context. Supports multiple models, no lock-in.
Competitors include GitHub Copilot for fast completions in Microsoft tools, Amazon Q Developer with AWS integration and scans, and Cursor for agentic coding across platforms. Tabnine stands out in agents and offline security (though we prefer Cursor). When it comes to pricing, it’s fairly simple – there’s a free individual tier, paid options for teams, and custom enterprise pricing.
Users appreciate adaptive suggestions, agent automation, and auditability. However, there are a few complaints, namely in the areas of resource use during indexing and occasionally inaccurate, complex suggestions.
To get started with Tabnine, test it for free in your IDE on a small project first, then explore agents on real tasks before committing to an enterprise solution.
What can you do with AI tools for developers?
AI tools can help developers increase their productivity and efficiency by a factor of 10. Specifically, they can help with:
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Code generation and completion
Instead of typing all your code, AI tools for developers can do part of it for you. The fact that these services have been trained on billions of lines of code speaks volumes. They can understand your needs and generate entire functions on your behalf. Or complete the code you’ve started writing.
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Code review and analysis
Beyond completing your code, AI tools for developers can also analyze it and provide suggestions. This can further help you optimize your apps and services to run faster across platforms and devices.
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Bug detection
Or you can rely on AI to detect bugs and quickly fix them. There are common bugs that AI algorithms can detect with ease, as well as other coding errors, like missing letters in functions, endless loops and more. AI tools will help you both detect and fix these bugs.
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Automated testing
Before deploying your software, you will want to test it first. Many AI tools for developers let you test your apps internally so that you can move them to a production server with confidence.
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Project management
Even in smaller organizations, developers tend to work in teams. And so AI tools made for them include various project management features, enabling collaboration across the board. This means that multiple developers can share code, use the same code repositories, and so on.
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Design
Beyond coding, there are AI tools for developers that include design-related features. You can use them to generate mockups, user interfaces, and even functional prototypes based on the input provided.
AI tools for developers are here to stay, and many developers can’t even think of returning to the “world before AI.” Simply put – it makes their lives much easier and their work that much better.