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Kiro AI
✓ verifiedFreemium
Kiro spec-driven AI IDE from prototype to production; notable AWS-backed dev product.
👁 3.8M/mo
Pricing
No public pricing
No public pricing
KIRO FREE: $0 /mo. per user
KIRO PRO: $19 /mo. per user
KIRO PRO+: $39 /mo. per user
No public pricing
Core features
- ✦AI-powered code autocompletion
- ✦Context-aware code referencing and chat
- ✦Natural language code editing
- ✦Customizable AI code assistants
- ✦Fast AI inference
- ✦LPU™ Inference Engine
- ✦Cloud and on-prem solutions
- ✦OpenAI endpoint compatibility
- ✦AI IDE for prototype to production
- ✦Spec-driven development
- ✦Agent hooks for task automation (e.g., generating documentation, unit tests, code optimization)
- ✦Multimodal chat
- ✦Model Context Protocol (MCP) integration for connecting to docs, databases, APIs
- ✦Autopilot mode for autonomous execution of large tasks
- ✦Configurable agent interaction via steering files
- ✦Support for state-of-the-art AI models (Claude Sonnet 3.7, Sonnet 4)
- ✦VS Code compatibility (Open VSX plugins, themes, settings)
- ✦Image input for UI design or architecture guidance
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
Use cases
- →Accelerate development with AI-powered autocompletion.
- →Improve code understanding with context-aware chat.
- →Refactor code using natural language instructions.
- →Running openly-available AI models like Llama, DeepSeek, Mixtral, Qwen, and Whisper with instant speed.
- →Building secure file sharing applications from scratch quickly.
- →Creating games without extensive manual coding.
- →Accelerating development from concept to working prototype in a short timeframe (e.g., a weekend).
- →Generating detailed user stories and capturing requirements like a product manager.
- →Automating routine development tasks such as documentation generation, unit testing, and code performance optimization.
- →Implementing complex features on larger codebases with fewer prompts and less repetition.
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
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