AI BriefingBuilding an In-House LLM Gateway on Amazon Bedrock: Authentication, Cost Management, and Governance for Claude Code and Codex
Key point
In preparation for the spread of AI tools like Claude Code, this article proposes a way to build an LLM Gateway centered on authentication, cost control, and governance using Amazon Bedrock.
Details
When deploying generative AI coding tools like Claude Code to developers, organizations face the challenges of tracking usage, controlling model access, and preventing budget overruns. In particular, Claude Code can immediately operate on top of Amazon Bedrock with just environment variable configuration, which makes it difficult to manage.
To address this, an LLM Gateway should be built to implement the following core functions.
- Authentication and permission management: Control access so that each user can only reach the models they are allowed to use
- Cost monitoring: Track API call costs by individual user and team, and enforce budget limits
- Governance and visibility: Provide centralized logs and statistics on which models are being used and how
Through this architecture, organizations can minimize the risks that come with adopting AI tools while maximizing development productivity.
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