JetBrains AI

JetBrains AI Review: Features, Pricing, Use Cases, Pros and Cons

JetBrains AI is an AI-powered software development ecosystem built around JetBrains IDEs and related tools. Rather than offering only a standalone chatbot, it connects AI assistance, coding agents, model choices, and team controls with developers’ existing workflows. Developers can use it for tasks such as understanding code, generating or editing it, and working with AI agents from within a familiar development environment. The broader ecosystem also includes Junie, JetBrains’ coding agent, third-party agents, and enterprise-focused capabilities.

This review explains what JetBrains AI does, how to get started, its current subscription prices, where it may be useful, and how it compares with alternatives. Whether you are an individual developer or evaluating AI tools for a software team, the main question is how well it fits the way you already build software.

Essential tools for software developers and teams. JetBrains AI is best classified as an AI coding assistant because it brings AI-powered help and coding agents to JetBrains IDEs and supports software development workflows.

What is JetBrains AI?

JetBrains AI is an AI ecosystem for professional software development. Its capabilities are connected to JetBrains products, including IDEs, and extend to agent-based coding workflows and business-oriented governance. The platform is designed to let developers work with AI while retaining control over code, decisions, and the models or agents they use.

The ecosystem supports several ways to access AI. Users can work with JetBrains AI, connect their own model providers or API keys where supported, use local models, or bring in compatible third-party agents. JetBrains describes support for agents, including Junie, OpenAI Codex, Claude Agent, and Gemini CLI, as well as additional agents through the Agent Client Protocol, or ACP.

That flexibility is an important part of the product’s positioning. JetBrains AI is not simply a single model or chatbot. It is a collection of AI features and integrations intended to work within software development tools.

Main functions and features

1. AI assistance inside IDEs. JetBrains AI brings AI-powered capabilities into supported JetBrains development environments. Keeping assistance close to the code can make it easier to ask for help while working, rather than repeatedly switching between an editor and a separate chat application.
2. Code generation and editing. AI coding tools can help developers draft code, modify existing code, or explore possible implementations. The generated output still needs human review: developers remain responsible for correctness, security, compatibility, and maintainability.
3. Code explanation and comprehension. Developers can use AI to help them understand unfamiliar code or clarify what a section of a project does. This may be particularly useful when joining a new codebase, navigating older software, or investigating an unfamiliar function.
4. Agent-driven workflows. JetBrains AI supports workflows that involve coding agents, including Junie and integrated third-party agents. Agents can assist with broader development tasks than a single question-and-answer exchange, depending on the agent and its available permissions. JetBrains presents agentic workflows as one part of its wider AI ecosystem.
5. Choice of models and providers. JetBrains says its ecosystem works with models from providers including OpenAI, Google, Anthropic, and xAI. It also identifies JetBrains models, such as Mellum, for specific IDE capabilities and describes options for user-provided or local models via supported connections.
6. Third-party agent integrations. Support for ACP-compatible agents allows users to connect additional tools to their IDE. The available options and authentication requirements vary by integration, so users should check the product documentation for the specific agent they intend to use.
7. Team and enterprise controls. JetBrains describes business-oriented capabilities for managing AI use across teams, including visibility, governance, security controls, and deployment flexibility. These features may matter to organisations that need more than individual coding assistance and want to manage adoption across a workforce.

How to use JetBrains AI

Getting started typically involves choosing a supported JetBrains product and deciding how you want to access AI. JetBrains advertises a free 30-day trial of its AI Pro plan across supported products, and its IDE page also describes connecting personal model providers or agents without a JetBrains AI subscription, where supported.

1. Choose a compatible development environment. Start with the JetBrains IDE or supported product that matches your work, such as an environment for a language or development task you use regularly. Confirm that the AI features and plan you want are available in that product.
2. Choose an access method. You can explore a JetBrains AI plan, use a trial if eligible, or connect your own provider credentials or compatible agent when supported. The right choice depends on your preferred models, expected usage, and whether you want a subscription or a bring-your-own-provider setup.
3. Enable the relevant AI feature or agent. Follow the setup flow for the chosen tool. For a third-party agent, check its integration instructions and authentication requirements rather than assuming that every agent uses the same account or billing arrangement.
4. Use AI on a focused development task. Ask for help explaining a function, drafting a small piece of code, or exploring an error. Focused requests with relevant context are usually easier to review than requests that ask an AI system to make broad changes without constraints.
5. Review the result. Inspect generated code, run the relevant tests, and check for errors, security issues, and project-specific conventions. AI can accelerate parts of the development process, but it does not take responsibility for the software you ship.
6. Monitor usage and adjust. Paid plans include a monthly AI Credit allowance, and JetBrains lists options for top-ups. Developers using agent-heavy workflows should consider their likely usage before choosing a plan.

For teams, it is also sensible to decide which providers and agents are acceptable, what data may be shared with them, and how developers should review AI-generated changes. JetBrains positions its business offering around centralised visibility and control, but organisations should evaluate the details against their own policies and requirements.

JetBrains AI pricing

The pricing page currently lists separate annual prices for AI Pro and AI Ultimate, along with a listed AI Enterprise price. JetBrains says AI Pro includes 10 AI Credits per 30 days and costs USD 100 per user per year. AI Ultimate includes 35 AI Credits per 30 days and costs USD 300 per user per year. Both plans list anytime top-ups.

The pricing page also lists AI Enterprise at USD 720; the extract does not specify a billing period for that figure, so confirm the current terms on the official pricing page before budgeting. JetBrains notes that taxes depend on the purchaser’s location, tax details, and purchase method. The page says one AI Credit is worth USD 1.00, charged in the customer’s local currency, for top-up purposes.

JetBrains also advertises a free 30-day AI Pro trial for supported products. Availability, eligibility, plan details, and prices may change, so check the official pricing page before purchasing. The amounts listed above are annual plan prices when the page explicitly states an annual billing period; they should not be mistaken for monthly prices.

Application scenarios

1. Every day coding assistance. Individual developers can use AI features while editing code, seeking explanations, or handling routine development tasks. The advantage is having assistance integrated into a familiar IDE.
2. Learning an unfamiliar codebase. A developer joining an established project may need help understanding unfamiliar files or patterns. AI assistance can provide a starting explanation, while the developer verifies it against the code and documentation.
3. Prototyping and exploration. Developers experimenting with an approach can use AI to generate an initial draft or compare possible implementation directions. This is most useful when the generated result is treated as a prototype rather than automatically production-ready code.
4. Agent-assisted tasks. When a task involves multiple steps, an integrated coding agent may help investigate or carry out parts of a development workflow. The value depends on the agent’s capabilities, the quality of the task description, and the developer’s ability to inspect its work.
5. Working across preferred AI providers. Developers who do not want to depend on a single provider may appreciate the ecosystem’s support for multiple models, personal credentials, and compatible agents. Exact availability depends on the chosen integration and setup.
6. Team adoption and governance. Organisations exploring AI across development teams may evaluate JetBrains’ business-oriented controls, deployment options, and centralised management capabilities. Enterprise buyers should check the current service terms, supported deployment configurations, and security documentation for their use case.

Pros and cons

Pros

• Fits established IDE workflows: AI capabilities are integrated into JetBrains development products rather than limited to a separate chat window.
• Multiple ways to access AI: The ecosystem supports JetBrains AI, selected external providers, local-model options, and compatible third-party agents, subject to the relevant setup and availability.
• Agent integrations: Developers can explore coding agents alongside more conventional AI assistance.
• Plans for different usage levels: AI Pro and AI Ultimate provide different monthly credit allowances, giving users a choice based on expected use.
• Team-oriented positioning: JetBrains describes governance and control capabilities aimed at organisations managing AI adoption across teams.
• Trial option: A free 30-day AI Pro trial is available for supported products, allowing eligible users to evaluate the service before subscribing.

Cons

• Credits require attention: Paid plans have specified monthly allowances, and developers who frequently use agents may need to monitor usage or consider top-ups.
• Pricing may be a separate cost: The AI plans are priced separately unless AI access is already included through an applicable JetBrains subscription or arrangement. Check the current terms for the products you own.
• Feature availability can vary: The relevant IDE, agent, model, and authentication method may affect what is available and how it works.
• Generated code needs review: As with other AI coding tools, outputs can be incomplete or incorrect. Developers need to test and assess suggestions before relying on them.
• Choice adds setup complexity: BYOK, local models, and multiple agents can offer flexibility, but they may also require additional configuration and account management.
• Business requirements need verification: Organisations should confirm that available privacy, security, deployment, and governance features meet their specific policies before rollout.

Alternatives and competing AI tools

1. GitHub Copilot is an AI coding assistant commonly considered by developers working in GitHub-centred workflows. It is a direct alternative for users who want AI help while coding, though the IDE integrations, plans, and features differ. Compare current pricing and supported environments before making a choice.
2. Cursor is an AI-focused code editor that emphasises AI integration into the editing workflow. Developers may compare it with JetBrains AI if they are open to changing editors or want a different approach to AI-assisted code changes.
3. Visual Studio IntelliCode offers AI-assisted development features in Microsoft’s development ecosystem. It may be a practical option for teams whose workflows already centre on Microsoft tools and supported editors.
4. Amazon Q Developer provides AI assistance for software development, particularly relevant to teams working with AWS services. Developers should compare their supported environments and cloud-focused capabilities with their own stack.
5. Codeium (Windsurf) is another AI coding product that developers may consider for code completion and broader AI-assisted development. Its fit depends on current integrations, features, and pricing.
6. Continue is an extensible coding-assistant option for developers who want to configure model providers and work across supported editors. It may appeal to users who prefer a more configurable setup, though configuration can require extra effort.

These products are not identical substitutes. Compare the editor or IDE you prefer, supported models and agents, privacy and data handling, usage limits, pricing, and how well the tool works with your team’s development process.

Final verdict

JetBrains AI is a strong option for developers who already work in JetBrains IDEs and want AI assistance and coding agents closer to their everyday tools. Its main differentiators are IDE integration, access to multiple models and agents, and an ecosystem that extends from individual developer workflows to team-oriented controls.

Its flexibility is appealing, but it also makes evaluation important: decide whether the included monthly credits fit your usage, check that your preferred IDE and agents are supported, and verify current pricing and data-handling terms. AI-generated code should be reviewed and tested, regardless of which product creates it.

For individual developers, the advertised trial lets them assess AI Pro before committing to an annual plan. For teams, the main question is whether JetBrains’ governance and deployment options align with internal requirements. Overall, JetBrains AI is worth considering for software developers seeking integrated AI coding tools, particularly those already invested in the JetBrains ecosystem.