How a SaaS Platform Scaled an AI Multi-Tenant Server to 9,800 Concurrent Tenants on Azure

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A company building an AI-first SaaS platform redesigned its multi-tenant MCP server architecture to overcome the hidden costs and failure points of running one LLM worker per tenant. The production system, deployed on Azure Container Apps, used a shared stateless worker pool with per-container memory and CPU limits to balance isolation, performance, and cost. Supporting infrastructure included per-tenant Redis Enterprise for caching and quota management, Azure Cognitive Search for vector similarity, and Azure OpenAI GPT-4 Turbo with keys stored in Key Vault. After 12 months in production, the platform achieved a 95th-percentile request latency of 420 milliseconds, a 32 percent reduction in token costs through caching, and near-zero cross-tenant quota incidents at peak loads of 9,800 concurrent tenants. The case study concludes that baking tenant isolation into the MCP envelope, runtime, and observability stack from the outset is essential for achieving these results at scale.

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