Vector Databases
Here's a secret the vector database vendors won't lead with: for a large share of enterprise workloads, pgvector on the Postgres you already run is the right answer. For the rest — billion-scale corpora, heavy metadata filtering, multi-tenant isolation — the dedicated engines earn their keep. Knowing which situation you're in is worth more than any benchmark blog post.
We choose on workload, not fashion, and then do the part everyone skips: index tuning against your actual query patterns, hybrid dense-plus-keyword search, filter-aware indexing that doesn't fall off a performance cliff, and boring operational discipline — backups, monitoring, capacity planning — because your vector store is production infrastructure the moment an agent depends on it.
Engine selection with receipts
pgvector, Pinecone, Weaviate, or Qdrant — benchmarked on your data and query shape, not vendor marketing.
Index and recall tuning
HNSW parameters, quantization, and hybrid search tuned to your latency and accuracy budget.
Filtered and multi-tenant search
Metadata filtering and tenant isolation that stay fast at scale.
Production operations
Monitoring, backups, re-index strategies, and capacity planning on Kubernetes or managed services.
Migration between engines
Zero-downtime moves when you've outgrown the first choice.
Do we need a dedicated vector database?
Maybe not. Under a few tens of millions of vectors with moderate query load, pgvector keeps your stack simple and your data in one place. Past that — or with heavy filtering and strict latency SLOs — dedicated engines win. We benchmark on your workload and show you the numbers before you commit.
Our vector search returns garbage. Is the database the problem?
Usually not. Bad chunking, a mismatched embedding model, or missing hybrid search cause most quality issues; the index just serves what it's given. We audit the whole retrieval path first — it's cheaper than migrating databases to fix a chunking bug.
Can you run vector search inside our compliance boundary?
Yes. Self-hosted Qdrant or Weaviate on your Kubernetes, or pgvector in your existing Postgres, keeps every embedding inside your perimeter. Data residency requirements usually decide the architecture before performance does.
Put Vector Databases to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
Book a deployment →