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Building a self-service LLM gateway with Amazon Bedrock and LiteLLM

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Building a self-service LLM gateway with Amazon Bedrock and LiteLLM

Developers want AI coding assistants with minimal setup. With a large language model (LLM) gateway and LiteLLM, they can quickly access foundation models (FMs) available on Amazon Bedrock while you gain visibility and usage control. Platform teams need to monitor latency and errors, finance teams need to attribute inference costs to teams and projects, and security teams need consistent access controls. An LLM gateway can help address these needs by providing a centrally managed access point to the models that power coding assistants.

In this post, we show how to build a self-service LLM gateway using Amazon Bedrock Application Inference Profiles with LiteLLM running on AWS Fargate to abstract Amazon Bedrock behind an OpenAI-compatible API. With this pattern, developers can access foundation models through a single API with a dedicated key while you gain per-user cost controls and model performance observability. By the end, you will understand the pattern well enough to build your own version from the illustrative snippets and AWS Cloud Development Kit (AWS CDK) constructs. This post uses Claude Sonnet and Claude Haiku models for example purposes. For a list of models and availability by AWS Region, see Supported models by AWS Region in Amazon Bedrock.

This LLM gateway pattern follows a straightforward request flow from the developer, through the LiteLLM layer, to Amazon Bedrock and the underlying FM. The following diagram illustrates the production topology behind that flow. For simplicity, we don’t cover the addition of Amazon Bedrock Guardrails, such as content filtering and grounding, but you would normally include appropriate responsible AI controls before deploying to production.


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