JetBrains AI Assistant vs Kiro
Detailed comparison of features, performance, and use cases
90/100
JetBrains AI Assistant
Integrated AI coding assistant built into JetBrains IDEs, offering context-aware suggestions and explanations.
Quick Verdict
JetBrains AI Assistant excels at jetbrains ide users and enterprise development with a score of 90/100. JetBrains AI Assistant leverages the deep IDE integration and powerful static analysis capabilities of JetBrains tools.
85/100
Kiro
AWS's spec-driven agentic IDE that transforms natural language into structured requirements, designs, and tasks—enabling autonomous AI agents to work for hours or days with persistent context.
Quick Verdict
Kiro excels at agentic workflows and spec-driven development with a score of 85/100. Kiro represents AWS's strategic entry into agentic coding, differentiated by its unique spec-driven development approach.
📊 Visual Score Comparison
Side-by-side comparison of key performance metrics across six evaluation criteria
Technical Specifications
| Feature | JetBrains AI Assistant | Kiro |
|---|---|---|
| Core AI Model(s) | JetBrains AI Assistant uses a combination of models. It leverages its own proprietary LLM, Mellum, which is optimized for coding. It also provides access to third-party cloud models from providers like OpenAI, Google (Gemini 2.5 Pro), and Anthropic (Claude 3.7 Sonnet). | Claude Sonnet 4.5 as primary model, with Auto mode that combines frontier models with prompt caching to optimize quality, latency, and cost. |
| Context Window | The assistant is deeply integrated into the IDE and is context-aware, using information from the current project, including language versions, libraries, and related files, to generate more accurate prompts and suggestions. | Large context support through Claude Sonnet 4.5. Persistent context across sessions enables multi-day autonomous work without losing project understanding. |
| Deployment Options | The AI Assistant is available as a plugin within JetBrains' commercial IDEs. For enterprise customers with strict data privacy needs, an on-premises solution is available through IDE Services, which can run in an air-gapped environment using local models like Llama 3.1 via Hugging Face. | Standalone IDE (Code OSS-based) for macOS, Windows, Linux. CLI available for macOS and Linux. No AWS account required—sign in with GitHub, Google, AWS Builder ID, or IAM Identity Center. |
| Offline Mode | Yes, JetBrains AI Assistant supports an offline mode. Users can connect to locally hosted models through tools like Ollama or LM Studio, allowing most AI features to function without an internet connection. However, some advanced features like multi-file edits are not available in offline mode. | Cloud-based, requires internet connection. Core agentic features depend on cloud AI inference. |
Core Features Comparison
JetBrains AI Assistant Features
- Context-aware code completion within JetBrains IDEs
- Code explanation and documentation generation
- Refactoring suggestions based on best practices
- Integration with JetBrains' powerful development tools
Kiro Features
- Spec-driven development with auto-generated requirements.md, design.md, and tasks.md
- Autonomous agent that works for hours/days with persistent context
- Kiro Powers for dynamic context activation (Stripe, Figma, Datadog)
- Property-based testing (PBT) to verify code matches specifications
- Native MCP (Model Context Protocol) integration
- Agent hooks for automated documentation and testing on file events
- Multimodal chat supporting images and UI designs
- Agent steering files for project-specific customization
Pricing & Value Analysis
| Aspect | JetBrains AI Assistant | Kiro |
|---|---|---|
| Pricing URL | View JetBrains AI Assistant Pricing | View Kiro Pricing |
| Overall Score | 90/100 | 85/100 |
| Best For | JetBrains IDE Users, Enterprise Development, Code Refactoring | Agentic Workflows, Spec-Driven Development, Enterprise Development, AWS Integration, Long-Running Tasks |
Best Use Cases
JetBrains AI Assistant Excels At
- Automated commit message generation based on code changes and project context
- Complex refactoring operations with AI understanding of code dependencies and design patterns
- Code explanation and documentation for team knowledge sharing and onboarding new developers
Kiro Excels At
- Converting product requirements into structured specs and implementation plans before writing any code—ensuring alignment between stakeholders and developers
- Running autonomous agents on complex features overnight, returning to completed implementations with full audit trails of decisions made
- Enterprise development where compliance requires traceable specifications that map directly to generated code artifacts
Performance & Integration
| Category | JetBrains AI Assistant | Kiro | Winner |
|---|---|---|---|
| IDE Support | Fully integrated with the suite of JetBrains IDEs, including IntelliJ IDEA, PyCharm, WebStorm, CLion, ReSharper, and others. | Kiro is a standalone IDE based on Code OSS. Supports VS Code settings import, Open VSX extensions, and existing themes. CLI available for terminal workflows. | Tie |
| Community | Active community | Active community | Tie |
| Data Richness | Comprehensive | Comprehensive | Tie |
| Overall Score | 90/100 | 85/100 | JetBrains AI Assistant |
The Bottom Line
Both JetBrains AI Assistant and Kiro are capable AI coding tools, but they serve different needs. JetBrains AI Assistant scores higher (90/100 vs 85/100) and excels in jetbrains ide users and enterprise development. The choice depends on your specific workflow, team size, and technical requirements.
Choose JetBrains AI Assistant if: you prioritize jetbrains ide users and enterprise development and want the higher-rated option (90/100).
Choose Kiro if: you prioritize agentic workflows and spec-driven development and don't mind a slightly lower score for specialized features.