Why OpenSearch Multi-Tenant Architectures Fail at Scale and How to Fix Them

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Most search applications are inherently multi-tenant, whether built for SaaS customers, e-commerce sellers, or fintech users, but architectures that work smoothly at small scale often break down at hundreds of thousands of tenants. The standard log-analytics approach of large shards and daily index rollovers optimizes for write throughput but forces every user query to fan out across dozens of shards, overwhelming coordination overhead. The opposite strategy of giving each tenant a dedicated index delivers clean isolation but causes cluster metadata to balloon, eventually choking master nodes as tenant counts reach tens of thousands. Neither conventional playbook was designed for high-volume, per-tenant query workloads that sit at the intersection of logging and search. The article argues that reaching million-tenant scale on Amazon OpenSearch Service requires moving beyond both defaults toward hybrid architectural patterns that balance shard efficiency with tenant isolation.

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