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Complete pricing guide for LiteLLM. Compare all plans, analyze costs, and find the perfect tier for your needs.
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View Full Features →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 a gateway hop between your application and model provider. Actual latency depends on deployment location, logging configuration, routing rules, provider latency, and network conditions, so teams should benchmark it in their own environment before production rollout.
Direct provider SDKs can be simpler for a single provider. LiteLLM is more useful when teams need automatic failover, unified spend tracking, budget enforcement, and the ability to switch or combine providers behind an OpenAI-compatible interface.
LiteLLM can be self-hosted so the gateway runs inside your own infrastructure. However, model requests still go to the configured model providers unless routed to local models, so teams should review both LiteLLM deployment settings and each provider's data handling policies.
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.
Yes. LiteLLM supports routing to local model servers including Ollama, vLLM, and OpenAI-compatible endpoints. This allows teams 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.
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