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✕
The New GitBook
✓ verified
Widely used technical-documentation platform with AI-powered search; very high traffic.
👁 653K/mo♥ 2.9K
✕
Kiro AI
✓ verifiedFreemium
Kiro spec-driven AI IDE from prototype to production; notable AWS-backed dev product.
👁 3.8M/mo
Pricing
No public pricing
DEVELOPER: FREE
STARTER: $119 / month
GROWTH: $599 / month
ENTERPRISE: Starting at $1,800 / month
No public pricing
KIRO FREE: $0 /mo. per user
KIRO PRO: $19 /mo. per user
KIRO PRO+: $39 /mo. per user
Free Plan: $0 one-time
Hobby: $16/month
Standard: $83/month
Growth: $333/month
Auto Recharge Credits: $11/mo for 1000 credits
Credit Pack: $9/mo for 1000 credits
Enterprise Plan: Contact for Pricing
Core features
- ✦AI-powered search and insights
- ✦Git-based workflow
- ✦WYSIWYG editor
- ✦OpenAPI spec file support
- ✦SEO and AI data ingestion optimization
- ✦Adaptive content
- ✦Built-in analytics
- ✦Developer-first platform for AI-powered integrations
- ✦Secure, isolated sandboxes for running JavaScript/Python code
- ✦Automatic management of npm/PyPI dependencies
- ✦Built-in platform plumbing: secrets, webhooks, scheduling, logs, and audit
- ✦Yep Agent (prompt → runnable processes)
- ✦MCP Server/Tools (convert code into AI agent tools)
- ✦Serverless runtime (YepCode Run) and SDK access
- ✦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)
- ✦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
- ✦Web scraping
- ✦Web crawling
- ✦Data extraction in Markdown, JSON, and screenshot formats
- ✦Dynamic content handling
- ✦Rotating proxies
- ✦Rate limits management
- ✦Open-source availability
- ✦Media Parsing
Use cases
- →Build product documentation your users will love
- →Create interactive API docs
- →Localize your docs with AI
- →Centralize product docs, API reference, help center, and changelog
- →Building complex API integrations that require custom code and logic beyond what no-code tools offer.
- →Safely running AI-generated scripts in isolated environments with secrets management.
- →Automating workflows that require large datasets, loops, branching, or custom dependencies.
- →Connecting AI agents to external databases, APIs, and services using MCP tools.
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →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.
- →Powering AI assistants with real-time web content
- →Enhancing sales data with web information
- →Adding scraping capabilities to code editors
- →Enabling customers to build AI apps with web data
- →Extracting comprehensive information for in-depth research
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