Build a multi-tenant generative AI environment for your enterprise on AWS

This post discusses how to build a multi-tenant generative AI environment for enterprises on AWS. It covers an architecture that enables quick experimentation, unified model access, reusability of common components, centralized governance and controls, and tracking model usage and costs per tenant.

Specifically, the article covers:

• The overall solution architecture, consisting of a generative AI gateway (with shared services) and tenant-specific applications
• Key components of the generative AI gateway like the HTTPS endpoint, core services (model registry, observability etc.), generative AI model components, responsible AI components, and generative AI application components
• Shared services like model onboarding, prompt catalog, prompt chaining, agents, re-rankers, guardrails, red teaming, human-in-the-loop, model evaluation and monitoring
• Tenant integration patterns including data isolation approaches for retrieval augmented generation
• Scaling considerations like separating production/non-prod environments, avoiding tight coupling of data components, and leveraging managed AWS services