Compare tools
Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
⇄ Comparison dimension — pick the market you're actually shopping in
✕
Mintlify
✓ verifiedFreemium
AI documentation platform with clean design and dev-friendly features.
👁 718K/mo
✕
The New GitBook
✓ verified
Widely used technical-documentation platform with AI-powered search; very high traffic.
👁 653K/mo♥ 2.9K
Pricing
Starter: $0/mo (10,000 AI credits/month)
Free trial available
No public pricing
No public pricing
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 documentation enhancements
- ✦Codebase syncing and web editor
- ✦API playground
- ✦Visitor authentication
- ✦Automatic translations
- ✦Integrations with popular tools
- ✦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
- ✦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)
- ✦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
- →Creating public-facing documentation for APIs and SDKs
- →Building internal knowledge bases for teams
- →Generating documentation that converts users
- →Streamlining documentation workflows for developers and non-developers
- →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
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →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
Visit