Paolo Di Francesco

Paolo Di Francesco

MLflow contributor · MLOps & agentic AI at AWS

Create a Generative AI Gateway to allow secure and compliant consumption of foundation models

less than 1 minute read

Every team in the enterprise wants to try foundation models. Nobody wants ten teams each wiring up their own model access, cost tracking, and compliance controls — and the security office wants that even less. In this post, we propose a Generative AI Gateway: one governed entry point through which the whole organization consumes foundation models, so teams can move fast without each of them re-solving privacy, cost, and compliance from scratch.

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Architecture: model consumers reach the Gen AI Gateway through an Amplify playground UI or programmatically via Amazon API Gateway with a Lambda authorizer, cache layer, and dispatch Lambdas, with CloudTrail, CloudWatch, usage metrics, and Comprehend for observability and control; a Model Abstraction Layer with Amazon Cognito identity, a model policy store, model engine, and secrets is managed by the model manager and routes to a vendor model registry of SageMaker, Bedrock, and third-party models

What happened since. When we wrote this in 2023, “LLM gateway” wasn’t yet a category — it has since become a standard enterprise pattern, with a whole ecosystem of open-source and commercial gateways implementing the same core ideas: one governed entry point, centralized cost attribution, and compliance controls decoupled from the consuming teams. The architecture reasoning here still holds; the build-vs-buy answer has shifted.

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