A caching proxy in front of a language model can cut the token bill significantly, but it introduces subtle risks if the design is not careful. Which cache types work in production, where the usual traps sit, and how to add them without degrading the experience.
The LiteLLM proxy sits between your applications and your model providers, exposing a single endpoint in the OpenAI format that routes each call to OpenAI, Anthropic, Cohere, or a self-hosted model. It earns its place at the second provider, where clean code breaks down: every SDK brings its own client, message format, streaming semantics, errors, and function calling.
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