JetBrains AI
Deeply integrated, context aware AI assistant for coding, right in your IDE
JetBrains AI is (obviously) an AI assistant that integrates directly into JetBrains’ range of Integrated Development Environments (IDEs), such as IntelliJ IDEA, PyCharm, WebStorm, and others. As a result of this integration that brings AI right within a coding workflow, JetBrains promises enhanced productivity and efficiency of developers.
The JetBrains AI Assistant supports various programming languages, relying on OpenAI’s tech to do its magic. As a result, it offers functionalities such as code documentation, refactoring suggestions, code generation, unit test generation, problem identification, and even converting files between programming languages.
Because the AI Assistant is deeply integrated into the JetBrains IDEs, it is able to offer context-aware suggestions and solutions based on the current project’s scope, dependencies, and coding conventions. This further makes it a more intuitive tool for developers accustomed to JetBrains’ ecosystem.
As that’s typically the case with these sorts of solutions, the AI Assistant is also available through a subscription — with rates set for individual users and organizations.
Down the road, JetBrains plans to incorporate LLM from other providers and possibly even offer options for on-premises model deployment for enterprise customers. That is something big organizations would definitely love to see, whereas we guess smaller teams don’t mind a cloud-based solution — as long as it helps them get the job done faster.
Homepage Screenshot 📸
Video Overview 🎬
What are the key features? ✨
- AI chat: It acts as a smart coding buddy right inside your IDE (Integrated Development Environment). You can ask it questions related to your code and get instant answers.
- Refactoring suggestions: Refactoring means changing the structure of your code without altering its functionality, making it cleaner and easier to understand. JetBrains AI can suggest to you how to do that.
- Code completion: As you type, the AI can predict what you're trying to write and finish the code for you. It's a cool productivity-boosting feature.
- Documentation writing: JetBrains AI can automate this tedious task and do it all for you, delivering nicely formatted documentation for your functions or classes.
- Generating commit messages: When you save changes to your code, you usually write a commit message to explain what you've done. The AI can automatically create these messages on your behalf.
Who is it for? 🤔
JetBrains AI is designed for software developers and teams looking to enhance productivity and efficiency in their coding processes. It is primarily made for those within the JetBrains ecosystem, utilizing their range of IDEs like IntelliJ IDEA, PyCharm, and WebStorm, among others. The AI's features cater to a range of programming languages and is aimed at both individual developers and organizations looking to streamline their development workflows and improve code quality. Its offering is particularly attractive to those already familiar with AI code generation tools and are seeking to integrate such functionalities more deeply into their coding activities.
Examples of what you can use it for 💡
- Debug and problem-solve your coding issues with JetBrain's AI chat
- Improve your code's structure and readability with JetBrain's refactoring suggestions
- Speed up development with code completion
- Generate detailed documentation without a hassle
- Craft meaningful commit messages using JetBrain AI
Pros & Cons ⚖️
- Automatic documentation alone is worth the subscription
- Code completion can 10x your productivity
- AI chat helps you find solutions to problems and make your code bug-free
- Some argue GitHub Copilot is better
FAQs 💬
What is JetBrains AI Assistant, and how does it fit into my coding workflow?
JetBrains AI Assistant is an AI-powered plugin that integrates directly into JetBrains IDEs like IntelliJ IDEA, PyCharm, and WebStorm to help with code generation, explanations, refactoring, and more. It boosts productivity by suggesting completions inline, handling multi-file edits, and offering a chat interface for queries, all while understanding your project's full context. I think it's especially useful for developers who spend most of their time in these IDEs, as it feels like a natural extension rather than a separate tool.
Which IDEs support JetBrains AI Assistant, and what languages does it handle?
It works with major JetBrains IDEs including IntelliJ IDEA, PyCharm, WebStorm, CLion, Rider, and DataGrip, starting from version 2023.3 or later. Supported languages cover Java, Kotlin, Python, JavaScript, TypeScript, C#, C++, Go, PHP, SQL, and over 20 others, thanks to the IDE's parsing. Recent updates in 2025.2 expanded local model support for offline use across these.
How much does JetBrains AI Assistant cost in 2025, and what are the plan options?
Pricing starts with a free tier for unlimited local completions and limited cloud features. The AI Pro plan runs about $10-15 per month for individuals, unlocking higher quotas and advanced tools like multi-file edits. AI Ultimate bundles it with full IDE access for around $25/month, while Enterprise offers custom pricing with on-prem options. Quotas reset monthly, and you can buy extra credits that roll over up to 12 months.
Is JetBrains AI Assistant secure for enterprise use, especially with sensitive code?
Yes, it emphasizes privacy with no code retention for training models, and you can use local models or .aiignore files to exclude sensitive paths. Enterprise plans support self-hosted LLMs via Amazon Bedrock or similar, keeping data in-house. JetBrains complies with SOC 2 and offers SSO, though some users note occasional cloud routing for snippets. Always check your org's policies.
How does JetBrains AI Assistant compare to GitHub Copilot?
JetBrains shines in deep IDE integration for large projects and monorepos, with better context from semantic indexing and features like commit message generation. Copilot edges out on broad compatibility (VS Code, Visual Studio) and faster inline completions, but it might feel less native in JetBrains tools. In 2025 reviews, JetBrains scores high for refactoring, while Copilot wins for quick prototyping.
What are the key new features in JetBrains AI Assistant for 2025?
Major 2025 updates include multi-file Edit mode for bulk changes with diffs, Junie agent for autonomous tasks like testing and deployment, GPT-5 support, and expanded model choices (OpenAI, Anthropic, Google). The 2025.2 release added image attachments in chat, better offline flexibility with tools like Ollama, and a simpler quota system. These make it stronger for complex workflows.
Can I use my own AI models or API keys with JetBrains AI Assistant?
Absolutely, especially with the upcoming BYOK (Bring Your Own Key) feature rolling out in late 2025, letting you connect personal accounts from OpenAI, Anthropic, or Gemini directly in the IDE. Local models via Ollama or OpenAI-compatible servers work now for completions and chat, ideal for privacy-focused setups.
What are common pros and cons of JetBrains AI Assistant based on user feedback?
Pros include seamless IDE integration, accurate context-aware suggestions, and strong privacy controls, with many praising its refactoring and doc generation. Cons often mention quota limits hitting fast for heavy use, occasional latency or inaccurate outputs in complex scenarios, and higher cost compared to free alternatives. Reddit users note it's improved a lot in 2025 but still trails Copilot in speed for some.
How do I get started with JetBrains AI Assistant if I'm new to it?
Install the plugin from the JetBrains Marketplace in your IDE (version 2025.1+ recommended), sign in with a JetBrains account, and choose your model in settings. Start with simple prompts in the chat for code explanations, then try inline completions. For best results, provide clear context and review diffs. Free tier lets you test local features right away.
Does JetBrains AI Assistant help with testing and debugging code?
It does, generating unit tests, suggesting fixes for errors, and explaining bugs with context from your codebase. Junie takes it further by running tests autonomously and iterating on failures. Users report it saves 30-40 minutes on short tasks, though you should always verify outputs, as it might miss edge cases in intricate setups.
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