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Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
Google's AI coding assistant for code completion, generation, chat and review across IDEs and GitHub.
Independent benchmarks comparing AI models and API providers on intelligence, speed, and cost across many leaderboards.
Enterprise platform to build, run and govern AI agents and ML models across cloud, on-prem and hybrid environments.
No public pricing
Free trial available
No public pricing
No public pricing
No public pricing
No public pricing
Free trial available
- ✦Publish structured documentation sites
- ✦Git sync for docs-as-code workflows
- ✦AI setup agent to build and import docs
- ✦GitBook MCP server for AI access
- ✦Enterprise controls
- ✦Free tier to start
- ✦AI code completion and suggestions
- ✦Natural-language code generation
- ✦In-IDE chat assistance
- ✦AI code review
- ✦IDE integrations (VS Code, JetBrains, etc.)
- ✦GitHub integration
- ✦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)
- ✦Intelligence Index across many benchmarks
- ✦Model speed and cost comparisons
- ✦Coding, speech, image, and video leaderboards
- ✦Provider performance analysis
- ✦Personalized model recommender
- ✦Premium data and reports
- ✦Agent workforce build/run/govern platform
- ✦Generative and predictive AI
- ✦AI governance and observability
- ✦Deploy on-prem, hybrid or cross-cloud
- ✦Prebuilt agents and blueprints
- ✦NVIDIA and SAP integrations
- →Publish product and API documentation
- →Maintain docs-as-code with Git sync
- →Make docs consumable by AI assistants
- →Import existing docs into a hosted site
- →Speeding up coding with AI completions
- →Generating code from plain-language prompts
- →Getting in-editor help and explanations
- →Reviewing pull requests with AI
- →Understanding unfamiliar codebases
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Choosing an AI model or provider
- →Tracking frontier model progress
- →Comparing price and performance
- →Build enterprise AI agents
- →Deploy and monitor ML models
- →Govern AI across the organization
- →Run AI in regulated/complex environments