By Sagar Shankaran, Founder of CallSphere
Sharding patterns that hold up beyond 100M vectors. The 2026 designs for partition keys, replication, and rebalancing.
Key takeaways
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.
flowchart TB
Strategy[Sharding strategy] --> S1[By tenant]
Strategy --> S2[By hash]
Strategy --> S3[By semantic / cluster]
Each tenant's vectors live in their own shard. Natural for multi-tenant SaaS.
Hash the vector ID, shard by hash. Even distribution.
Cluster vectors by topic; each cluster is a shard.
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.
Each shard is replicated for availability:
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The replication strategy affects consistency:
For vector data, asynchronous is common because vector updates are usually appends with eventual consistency tolerance.
When shards become uneven:
Rebalancing is operationally painful. Patterns to minimize:
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.
Top-K across shards: each shard returns its top-K, the router merges, returns the global top-K. The math:
For consistency in score scales, all shards should use the same scoring (same embedding model, same distance metric).
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For very large vector deployments (1B+ vectors):
Vector DB Sharding Strategies for Hundreds of Millions of Vectors forces a tension most teams underestimate: agent handoff state. A single LLM call is easy. A booking agent that hands a confirmed slot to a billing agent that hands a follow-up to an escalation agent — that's where context loss, hallucinated IDs, and double-bookings live. Solving it well means treating the conversation as a stateful workflow, not a chat.
The protocol layer determines what's possible: WebRTC for browser-side widgets, SIP trunks (Twilio, Telnyx) for PSTN voice, WebSockets for the Realtime API streaming session. Each has its own jitter buffer, its own ICE/STUN dance, and its own failure modes when a customer's corporate firewall is hostile.
Front-end is Next.js 15 + React 19 for the marketing surface and the in-app dashboards, with server components used heavily for the SEO-critical pages. Backend splits across FastAPI for the AI worker, NestJS + Prisma for the customer-facing API, and a thin Go gateway that does auth, rate limiting, and routing — letting each service scale on its own characteristics.
Datastores: Postgres as the source of truth (per-vertical schemas like healthcare_voice, realestate_voice), ChromaDB for RAG over support docs, Redis for ephemeral session state. Postgres RLS enforces tenant isolation at the row level so a misconfigured query can't leak across customers.
What's the right way to scope the proof-of-concept?
Real Estate runs as a 6-container pod (frontend, gateway, ai-worker, voice-server, NATS event bus, Redis) backed by Postgres realestate_voice with row-level security so multi-tenant data never crosses tenants. For a topic like "Vector DB Sharding Strategies for Hundreds of Millions of Vectors", that means you're not starting from scratch — you're configuring an agent template that's already been hardened across thousands of conversations.
How do you handle compliance and data isolation? Day one is integration mapping (scheduler, CRM, messaging) and prompt tuning against your top 20 real call transcripts. Day two through five is shadow-mode running, where the agent transcribes and recommends but a human still answers, so you can compare side-by-side. Go-live is the moment your eval pass-rate clears your internal bar.
When does it make sense to switch from a managed model to a self-hosted one? The honest answer: it scales until your tool catalog gets stale. The agent is only as good as the integrations it can actually call, so the operational discipline is keeping schemas, webhooks, and fallback paths green. The platform handles the rest — observability, retries, multi-region routing — without your team owning the GPU layer.
Want to see how this maps to your stack? Book a live walkthrough at calendly.com/sagar-callsphere/new-meeting, or try the vertical-specific demo at salon.callsphere.tech. 14-day trial, no credit card, pilot live in 3–5 business days.
Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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