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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

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✓ verifiedFreemium

Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.

775K visits/mo
liteLLM logo
liteLLM
✓ verifiedFreemium

Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.

703K visits/mo

Thin 'Lingbot-map' agent listing on github.com with zero traffic; too thin to tell.

5.2K saves
Apify logo
Apify
✓ verifiedFreemium

Full-stack platform for web scraping, data extraction, and automation; category leader.

4.4M visits/mo2.0K saves
Pricing

No public pricing

Open Source: $0 (self-hosted, 100+ providers)

Free trial available

No public pricing

Free: $0/mo ($5 included usage)
Starter: $29/mo ($26/mo billed annually)
Scale: $199/mo ($179/mo billed annually)
Business: $999/mo ($899/mo billed annually)

Free trial available

Core features
  • Open-source AI code assistant
  • Customizable autocomplete
  • In-editor AI chat
  • Community-built coding agent
  • Unified access to 100+ LLMs in OpenAI format
  • Cost/spend tracking per key, user and team
  • Budgets and rate limiting
  • Automatic provider fallbacks and retries
  • Virtual keys and team management
  • Logging and observability integrations
  • 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
  • Data extraction
  • Browser automation
  • AI agents
  • Anti-blocking
  • Proxy rotation
  • Open-source tools (Crawlee)
  • Ready-made tools and code templates
Use cases
  • Get AI code completions while coding
  • Ask questions about code in the editor
  • Build on an open-source coding-agent foundation
  • Giving developers governed access to many LLMs
  • Attributing and controlling LLM spend
  • Keeping apps running during provider outages
  • Creating and manipulating datasets
  • Training small machine learning models
  • Implementing advanced training and inference logic
  • Building applications that require tensor computations
  • Data for generative AI
  • Lead generation
  • Market research
  • Sentiment analysis
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