Vector DB Sharding Strategies for Hundreds of Millions of Vectors

When Sharding Becomes Necessary

A single vector index works well up to roughly 50-100M vectors with HNSW, depending on dimensions and hardware. Beyond that, query latency and memory pressure force sharding. The sharding choice — by what key, with what topology, with what replication — decides the system's scale ceiling and operational behavior.

This piece walks through the patterns that hold up.

The Three Sharding Strategies

flowchart TB
    Strategy[Sharding strategy] --> S1[By tenant]
    Strategy --> S2[By hash]
    Strategy --> S3[By semantic / cluster]

By Tenant

Each tenant's vectors live in their own shard. Natural for multi-tenant SaaS.

• Pro: per-tenant isolation; predictable; easy to reason about
• Con: hot tenants overload one shard; small tenants waste capacity

By Hash

Hash the vector ID, shard by hash. Even distribution.

• Pro: even load distribution
• Con: queries must fan out to all shards (unless filterable up-front)

By Semantic / Cluster

Cluster vectors by topic; each cluster is a shard.

• Pro: queries hit only relevant shards (lower fan-out)
• Con: complex to maintain; cluster boundaries drift

For most production systems in 2026, by-tenant works for multi-tenant SaaS; by-hash for single-tenant high-volume; by-semantic for specialized very-large corpora.

Replication

Each shard is replicated for availability:

• 2-3 replicas per shard typical
• Reads served from any replica
• Writes go to primary, propagated to replicas

The replication strategy affects consistency:

• Synchronous: all replicas updated before write returns
• Asynchronous: write returns; replicas catch up
• Quorum: majority of replicas updated

For vector data, asynchronous is common because vector updates are usually appends with eventual consistency tolerance.

Rebalancing

When shards become uneven:

• Add a new shard
• Move vectors from overloaded shards to the new one
• Update routing

Rebalancing is operationally painful. Patterns to minimize:

• Predict shard growth and pre-shard
• Use consistent hashing so adds move minimal data
• Schedule rebalancing during low-traffic windows
• Some vector DBs (Milvus) support online rebalancing

Routing

Where do queries go?

flowchart LR
    Q[Query] --> R[Router]
    R -->|filter shards| S1[Shard 1]
    R -->|fan out| S2[Shard 2]
    R -->|by tenant| S3[Shard 3]
    S1 --> Merge[Merge results]
    S2 --> Merge
    S3 --> Merge
    Merge --> Top[Top K]

The router decides which shards to query. Smart routing saves cost and latency.

Cross-Shard Top-K

Top-K across shards: each shard returns its top-K, the router merges, returns the global top-K. The math:

• Each shard returns top-K
• Merge sorts (K * num_shards) candidates
• Returns global top-K

For consistency in score scales, all shards should use the same scoring (same embedding model, same distance metric).

What Goes Wrong

• Hot shard — one tenant or one hash bucket dominates
• Skewed distribution — some shards 90 percent full, others 10 percent
• Query fan-out at every request — saturates the network
• Stale replicas serving wrong data
• Rebalancing during peak — performance degrades

Operational Realities

For very large vector deployments (1B+ vectors):

• Monitor per-shard query rate, latency, memory
• Alert on imbalance
• Have a rebalance runbook
• Test failover regularly

Specific Vendor Patterns

• pgvector: Citus / Postgres sharding extensions
• Qdrant: native sharding with replication
• Milvus: distributed by design; multiple sharding modes
• Weaviate: multi-tenant with shard per tenant
• Pinecone: managed sharding (the user does not see it)

When Not to Shard

• Workloads under 50M vectors fit in one HNSW index — do not over-engineer
• Workloads with very low QPS may be fine on a single node
• Latency-sensitive workloads where fan-out cost is unacceptable

Sources

• Milvus distributed architecture — https://milvus.io/docs
• Weaviate multi-tenancy — https://weaviate.io/developers/weaviate/manage-data/multi-tenancy
• Qdrant clustering — https://qdrant.tech/documentation
• "Sharding strategies for vector DBs" — https://www.pinecone.io/learn
• Citus Postgres extension — https://www.citusdata.com

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