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Pricing sourced from LiteLLM · Last verified March 2026
Yes. LiteLLM is available as a Python package (pip install litellm) that you can use as a library in your code or run as a standalone proxy server. Docker is recommended for production deployments but not required.
LiteLLM adds minimal overhead — typically under 10ms per request for local proxy deployments. The proxy handles routing, logging, and spend calculation asynchronously to minimize impact on response times.
Direct provider SDKs lock you into a single provider. LiteLLM gives you automatic failover across providers, unified spend tracking, budget enforcement, and the ability to switch models by changing a parameter — without rewriting application code.
LiteLLM's self-hosted proxy runs entirely on your infrastructure. No data passes through LiteLLM's servers. For the enterprise cloud option, LiteLLM provides security documentation and compliance FAQs at docs.litellm.ai/docs/data_security.
LiteLLM supports 100+ providers including OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, Azure OpenAI, Cohere, Mistral, Together AI, Replicate, Hugging Face, Ollama for local models, and many more. New providers are added regularly.
Yes. LiteLLM supports routing to local model servers including Ollama, vLLM, and any OpenAI-compatible endpoint. This allows you to mix cloud and local models in the same routing configuration with unified logging and spend tracking.
AI builders and operators use LiteLLM to streamline their workflow.
Try LiteLLM Now →AI gateway and observability platform for managing multiple LLM providers with routing, fallbacks, and cost optimization.
Compare Pricing →Open-source LLM observability platform and API gateway that provides cost analytics, request logging, caching, and rate limiting through a simple proxy-based integration requiring only a base URL change.
Compare Pricing →Universal AI model API gateway providing unified access to 300+ models from every major provider through a single OpenAI-compatible interface - eliminating vendor lock-in while reducing costs and complexity.
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