Shared infrastructure, isolated tenants: Pool model multi-tenancy with Amazon Bedrock AgentCore

2026-06-23 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, long

Summary

This article details architectural patterns for building production-ready multi-tenant AI applications using Amazon Bedrock AgentCore, focusing on complete tenant isolation, service tier differentiation, granular cost tracking, and scalable architectures. It demonstrates these concepts through a healthcare AI agent example, implementing Basic and Premium service tiers. The Basic Tier utilizes Mistral Ministral 3 8B Instruct for simple document search, while the Premium Tier employs OpenAI GPT OSS 120B for complex clinical analysis, including web search. The solution integrates AWS services such as Amazon Cognito for authentication, Amazon API Gateway for routing and rate limiting, AWS Lambda for context extraction, Amazon S3 for tier-separated document storage, Amazon Bedrock Knowledge Bases for semantic search, and Amazon Bedrock Projects for per-tier cost attribution. Key AgentCore capabilities like Runtime, Identity, Memory, Gateway, Policy, and Observability are leveraged for robust multi-tenancy.

Key takeaway

For AI Architects or MLOps Engineers designing multi-tenant AI applications on AWS, this architecture demonstrates how Amazon Bedrock AgentCore, combined with native AWS services, simplifies complex isolation and cost attribution challenges. You should utilize AgentCore's built-in capabilities for identity, memory, and policy enforcement, alongside S3 for data isolation and Bedrock Projects for granular cost tracking. This approach minimizes custom code, allowing you to build secure, scalable, and cost-effective solutions with differentiated service tiers efficiently.

Key insights

Amazon Bedrock AgentCore enables robust multi-tenancy in AI applications through native AWS services for isolation, differentiation, and cost attribution.

Principles

• Enforce isolation at Tier → Tenant → User hierarchy.
• Combine tiering with pool isolation for efficiency.
• Use declarative policies for access control.

Method

The solution uses Amazon Cognito for authentication, API Gateway for routing/rate limiting, AWS Lambda for context, and AgentCore components (Runtime, Identity, Memory, Gateway, Policy, Observability) for AI orchestration, with S3 and Knowledge Bases for data.

In practice

• Use S3 buckets with hierarchical prefixes for data isolation.
• Implement Bedrock Projects for per-tier cost attribution.
• Apply API Gateway usage plans for tier-specific rate limiting.

Topics

• Amazon Bedrock AgentCore
• Multi-tenancy
• AI Agents
• AWS Architecture
• Data Isolation
• Cost Attribution
• Healthcare AI

Code references

• aws-samples/sample-agentcore-and-multitenancy-blog

Best for: AI Engineer, AI Architect, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.