JetBrains AI Guide: Features, Pricing, Models & How to Use It (SEO optimized, 2026)

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In the rapidly evolving landscape of software development, JetBrains AI has established itself as a cornerstone for developer productivity. By 2026, the tool has matured from a simple chat assistant into a deeply integrated, context-aware coding partner that leverages the power of the JetBrains PSI (Program Structure Interface) to understand code far better than generic text-based LLMs.

This comprehensive guide covers everything you need to know about JetBrains AI in 2026, from its “Agentic” capabilities to enterprise-grade privacy controls.

Tool Overview

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JetBrains AI is not a single model; it is a sophisticated AI service orchestrator embedded directly into the IDE ecosystem (IntelliJ IDEA, PyCharm, WebStorm, Rider, etc.). Unlike external tools that require copy-pasting code, JetBrains AI resides within the editor, possessing read/write access to your project structure, version control history, and runtime execution logs.

Key Features (2026 Update)

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1. Context-Aware Chat: It doesn’t just read the open file; it understands the entire project dependency graph.
2. AI Agents (New in 2025/2026): Autonomous background workers that can perform multi-step refactoring, write comprehensive test suites, and fix linting errors without blocking the user.
3. Predictive Code Generation: Beyond line completion, it suggests entire logic blocks based on your coding style and existing architectural patterns.
4. Terminal Intelligence: Converts natural language commands into complex shell scripts or Gradle/Maven commands directly in the embedded terminal.
5. Smart Commit & PR Summaries: Analyzes diffs to generate semantic commit messages and pull request descriptions.
6. Local Model Support: Enterprise users can now connect local LLMs (Llama 4, Mistral Enterprise) for air-gapped security.

Technical Architecture

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JetBrains AI operates on a hybrid router architecture. It does not rely on a single provider. Instead, the “AI Service” acts as a proxy, routing requests to the best model for the task—whether that’s OpenAI’s GPT-5 for complex reasoning, Google’s Gemini 2.0 for large context windows, or JetBrains’ proprietary small language models (SLMs) for low-latency completion.

Internal Model Workflow

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1. User Trigger: Developer invokes a command (e.g., “Refactor this class”).
2. Context Collection: The IDE gathers AST (Abstract Syntax Tree) data, recent file usage, and language settings.
3. Anonymization: PII and secrets are scrubbed locally.
4. Routing: The JetBrains AI Service determines the optimal model provider.
5. Response Handling: The response is parsed back into IDE actions (diff view, code insertion, or chat response).

graph TD
    User[Developer] -->|Prompt/Action| IDE[JetBrains IDE Plugin]
    IDE -->|Extract AST & Context| ContextEngine[Context Engine]
    ContextEngine -->|Sanitized Payload| JB_Gateway[JetBrains AI Gateway]
    
    subgraph "Model Providers"
        JB_Gateway -->|Complex Logic| GPT[OpenAI GPT-5]
        JB_Gateway -->|Large Context| Gemini[Gemini 2.0 Ultra]
        JB_Gateway -->|Privacy/Speed| Local[Local/Private LLM]
        JB_Gateway -->|Code Specific| JB_LLM[JetBrains Custom Model]
    end
    
    GPT -->|Response| JB_Gateway
    JB_Gateway -->|Formatted Action| IDE
    IDE -->|Apply Diff| Project[Codebase]

Pros & Limitations

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Installation & Setup

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Setting up JetBrains AI in 2026 is streamlined, often requiring no external plugins as it is bundled with the core IDE distribution.

Account Setup (Free / Pro / Enterprise)

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1. Update IDE: Ensure you are running version 2025.3 or later.
2. Activation:
   • Open your IDE settings (Ctrl+Alt+S or Cmd+,).
   • Navigate to Tools > AI Assistant.
   • Click Activate Subscription. You will be redirected to the JetBrains Account portal.
3. Licensing:
   • Free Trial: 7-day full access.
   • AI Pro: Linked to your personal JetBrains account.
   • Enterprise: Managed via the License Server (Floating licenses).

SDK / API Installation

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In 2026, JetBrains introduced the JetBrains AI CLI and SDK, allowing DevOps engineers to use the AI capabilities in CI/CD pipelines (e.g., for automated code reviews).

Prerequisites:

• Node.js 22+ or Python 3.12+
• Active JetBrains AI Subscription

Sample Code Snippets

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Python SDK Example (Automated Code Review)

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# JetBrains AI SDK v2.1
from jetbrains_ai import AIClient, ProjectContext

client = AIClient(api_key="jb_ai_...")

# Load project context
context = ProjectContext.from_path("./my_project")

# Analyze a specific file for security vulnerabilities
response = client.review_code(
    file_path="src/auth_handler.py",
    focus=["security", "performance"],
    model="gpt-4o-secure"
)

print(f"AI Suggestions: {response.suggestions}")
# Output: "Line 45: Vulnerable to SQL Injection. Use parameterized queries."

Node.js Example (Generate Documentation)

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import { AI } from '@jetbrains/ai-sdk';

const ai = new AI({ token: process.env.JB_AI_TOKEN });

async function documentFile() {
  const code = fs.readFileSync('./utils.ts', 'utf-8');
  
  const docs = await ai.generateDocs({
    code: code,
    style: 'JSDoc',
    verbosity: 'detailed'
  });

  console.log(docs);
}

Common Issues & Solutions

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1. “Context Window Exceeded”:
   • Cause: Trying to feed too many files into the chat.
   • Solution: Use @file notation to select only relevant interfaces, not implementation details.
2. Plugin Conflict:
   • Cause: Concurrent use of Copilot and JetBrains AI.
   • Solution: Disable conflicting keymaps or deactivate one tool for specific file types.
3. Authentication Loop:
   • Solution: Log out of the JetBrains Toolbox App and log back in to refresh the Oauth token.

API Call Flow Diagram

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sequenceDiagram
    participant Dev as Developer/CI
    participant CLI as JetBrains AI CLI
    participant Auth as Auth Server
    participant Engine as Inference Engine

    Dev->>CLI: Run "jb-ai review --branch feature/login"
    CLI->>Auth: Validate License Token
    Auth-->>CLI: Token Valid (Scope: Write)
    CLI->>CLI: Compress Git Diff
    CLI->>Engine: Send Diff + Linting Rules
    Engine-->>CLI: Return Code Comments JSON
    CLI->>Dev: Display Report in Console

Practical Use Cases

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JetBrains AI excels when the task requires understanding the relationship between code components.

Education

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• Concept Explanation: Highlight a complex Stream.reduce operation in Java and ask: “Explain this flow step-by-step with data visualization.”
• Language Migration: Students learning Rust coming from C++ can paste C++ snippets and ask for the Rust equivalent with explanations on memory safety ownership.

Enterprise

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• Legacy Refactoring: An enterprise team moving from Java 11 to Java 25.
  • Workflow: Select a module -> “Identify deprecated APIs and suggest modern replacements using Records and Pattern Matching.”
• Test Generation: “Generate Spock tests for this Service class, covering edge cases for null inputs and database timeouts.”

Finance

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• Algorithm Optimization: Financial quants use the AI to optimize Python execution loops for lower latency.
• Example Input: “Analyze this NumPy calculation. Can we vectorize it further to reduce execution time by 20%?”

Healthcare

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• Compliance Checking: Using a local-hosted model configuration to scan code for potential HIPAA violations (e.g., hardcoded patient data logging) without data leaving the firewall.

Workflow Automation Example

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Scenario: A developer needs to create a REST endpoint, a database entity, and a DTO.

graph LR
    A[Raw Idea] -->|Prompt| B(Entity Generation)
    B -->|Context| C(Repository & Service)
    C -->|Context| D(Controller & DTOs)
    D -->|Agent Check| E{Missing Parts?}
    E -->|Yes: SQL Migration| F[Generate SQL]
    E -->|No| G[Ready to Deploy]

Prompt Library

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The quality of output depends heavily on the “Prompt Context” feature introduced in late 2024.

Text Prompts

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Code Prompts

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Multimodal Prompts (2026 Feature)

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JetBrains AI now supports pasting images directly into the chat inside the IDE.

• Input: Screenshot of a Figma design for a login modal.
• Prompt: “Generate the Jetpack Compose code to replicate this UI. Use Material 3 design tokens.”
• Output: Complete Kotlin UI code matching the visual style.

Prompt Optimization Tips

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1. Use @ Symbols: Explicitly reference files or symbols (e.g., “Explain how @UserService interacts with @UserEntity”).
2. Specify Frameworks: Always mention the version (e.g., “Use Next.js 15 app router syntax”).
3. Chain of Thought: Ask the AI to “Outline the plan before writing code.”

Advanced Features / Pro Tips

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Automation & Integration

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By 2026, JetBrains AI integrates with external tools via the “AI Actions” pipeline.

• Jira Integration: “Create a Jira ticket for this TODO comment.”
• Notion: “Export this chat session as a technical specification to the team Notion page.”

Batch Generation & Workflow Pipelines

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You can define .jb-ai/workflows.yaml in your repository to automate tasks.

# .jb-ai/workflows.yaml
workflows:
  on_commit:
    - task: "Generate Commit Message"
      model: "gpt-4o-mini"
    - task: "Check for console.log"
      action: "warn"
      
  on_pr:
    - task: "Summarize Changes"
      output: "PR_DESC.md"

Custom Scripts & Plugins

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Advanced users use the AI Scripting API to create custom intention actions.

• Example: A custom “Convert to Hex” action that uses AI to detect color strings and convert them, bound to Alt+Enter.

Pricing & Subscription

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Pricing has adjusted slightly for inflation and increased capability in 2026.

Comparison Table

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API Usage & Rate Limits

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• Pro: Soft cap at 500 requests/day.
• Enterprise: Volume-based pricing or unlimited floating license pools.

Recommendations

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• Freelancers: The AI Pro plan is a no-brainer. The time saved on boilerplate and debugging pays for the subscription in roughly 2 hours of work.
• Corporations: AI Enterprise is essential for the Local Model support to ensure IP protection and compliance with GDPR/SOC2.

Alternatives & Comparisons

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While JetBrains AI is powerful, the 2026 market is competitive.

Competitor Analysis

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1. GitHub Copilot X (vNext):
   • Pros: Tighter GitHub ecosystem integration (Issues, PRs).
   • Cons: Less context-aware regarding deep Java/Kotlin ASTs compared to JetBrains.
2. Cursor (Independent Editor):
   • Pros: Extremely fast, UI completely built around AI.
   • Cons: Requires switching away from IntelliJ/WebStorm.
3. Tabnine Enterprise:
   • Pros: Best-in-class local model deployment for extreme privacy.
   • Cons: Chat features are less “reasoning” capable than GPT-5 based tools.
4. Codeium:
   • Pros: Excellent free tier.
   • Cons: Enterprise features lag behind JetBrains.

Feature Comparison

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FAQ & User Feedback

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1. Is my code sent to OpenAI/Google?

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• Pro/Enterprise: JetBrains acts as a proxy. Your code is sent to the LLM provider for inference but is not used to train their models (Zero Data Retention).
• Local Models: No code leaves your network.

2. Can I use JetBrains AI offline?

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• Yes, if you have an Enterprise license and have configured a local model (e.g., using Ollama or LM Studio connected to the IDE). The cloud-based models require an internet connection.

3. Does it support C++ and Unreal Engine?

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• Yes, specifically in Rider and CLion, the AI is fine-tuned for C++ memory management and Unreal blueprints logic.

4. How do I clear the chat context?

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• Click the “New Chat” icon (+) in the AI sidebar. It is good practice to start new chats for distinct tasks to prevent hallucination.

5. Why is the AI suggesting deprecated code?

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• Check your project JDK/Language level settings. If the project is set to Java 8, the AI will suggest Java 8 compatible code. Explicitly prompt it to “Update to Java 21 features.”

6. Can I share AI chats with my team?

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• Yes, use the “Export Chat” or “Share to Space” feature to create a permanent link to a solution found via AI.

7. Does it work with Vim mode?

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• Yes, JetBrains AI works independently of the editor input mode.

8. Is there a student discount?

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• JetBrains offers free educational licenses for the IDEs, but the AI service usually requires a separate subscription, though students often get a significant discount (approx. 50%).

9. Can it generate images?

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• No, JetBrains AI focuses on code and text. It does not generate assets, though it can write the code to render SVG or Canvas graphics.

10. How do I enable the “Agent” features?

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• Go to Settings > AI Assistant > Experimental Features and toggle “Enable Autonomous Agents.”

References & Resources

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• Official Documentation: JetBrains AI Help Center
• Blog: JetBrains Blog - AI Category
• Community: IntelliJ IDEA Forum
• Tutorials: JetBrains Academy - AI for Developers

Disclaimer: This article assumes a future state as of January 2026. Features and pricing are projected based on the trajectory of JetBrains AI development.