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Picking a Vector DB in 2026: A Decision Framework

By OrionAI Build Editorial · Published 2026-05-10 · // guide

I've helped four teams pick a vector DB this year. Each pick was different. The framework that gets to the right answer in 15 minutes.

The four questions

  1. How many vectors, today and at 12-month projection?
  2. What's your latency budget — P95, under your real load?
  3. What's your filtering pattern — heavy metadata filtering, geo filters, or pure-vector?
  4. How much ops are you willing to carry?

The answer matrix

Under 1M vectors, light filtering

pgvector. Already in your Postgres if you have one. Don't add new infrastructure for <1M vectors.

1M to 10M vectors, light filtering

Qdrant or Weaviate self-hosted on a $20-$60/month box. pgvector starts to slow on similarity search around the 5M-vector mark unless you tune carefully.

1M to 10M vectors, heavy metadata filtering

Qdrant. Filterable HNSW handles pre-filter cases pgvector struggles with.

10M+ vectors

Managed Qdrant Cloud, Pinecone, or Weaviate Cloud. The ops burden of self-hosting at scale is real and grows.

Multi-tenant SaaS pattern

Pinecone or Qdrant Cloud, namespaces per tenant. Self-hosting multi-tenant vector search at scale is a full-time job for someone.

Geo-filtered search (location-aware)

pgvector with PostGIS, or Qdrant with geo filters. Not Pinecone — geo support is weaker.

What people optimise for that doesn't matter

What does matter and is under-measured

The 15-minute test

Take 1,000 of your real vectors. Index them in three options. Run 100 of your real queries. If two options give similar quality, pick the one with the smaller ops surface. If one option clearly fails, you've saved yourself months.

Model APIs — vetted picks
GPU & compute — vetted picks
Dev tools — vetted picks