Show notes
Multitenancy in vector databases involves four distinct isolation trade-offs: storage, compute, enforcement, and operations. When partitioning in Qdrant, the dominant cost is rarely the vector data itself, but the HNSW index overhead required for each collection or shard. This deep dive maps out Qdrant's four isolation models—from separate collections and payload partitioning with the tenant index to custom sharding and tiered promotion—contrasting real-world memory benchmarks against latency trade-offs across different corpus sizes.
Key sources:
• Qdrant, Configure Multitenancy, retrieved 2026-11-10 — https://qdrant.tech/documentation/manage-data/multitenancy/
• Qdrant, Choose a Multitenancy Design, retrieved 2026-11-10 — https://qdrant.tech/documentation/deploy-and-scale/multitenant-search/
• Qdrant, agent-readable mirror of the multitenancy docs (includes Cloud 1,000-collection limit, `is_tenant` v1.11.0 note, per-tenant IDF v1.19.0), retrieved 2026-11-10 — https://skills.qdrant.tech/md/documentation/manage-data/multitenancy/
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