Vector Database Consulting
We help you choose, architect, and tune the vector database at the heart of your semantic search or RAG system — for the right balance of recall, latency, and cost.
The vector database is the foundation of every semantic search and RAG system, and the wrong choice or configuration shows up as poor recall, slow queries, or a runaway bill. There is no single "best" vector DB — only the right fit for your scale, filtering needs, hosting model, and budget.
Whether you’re starting fresh or scaling an existing system, we help you select the right store (Pinecone, Weaviate, Qdrant, or pgvector), design the schema and indexing strategy, and tune it so retrieval is fast, accurate, and affordable.
What's included
- Vector database selection matched to your scale and constraints
- Embedding strategy, dimensionality, and index design (HNSW/IVF)
- Metadata filtering, hybrid search, and multi-tenancy design
- Quantisation and cost/latency tuning
- Migration between vector stores with minimal downtime
- Benchmarking recall and latency on your real data
How we approach it
- Profile your data volume, query patterns, and filtering needs
- Benchmark candidate stores on your data, not synthetic demos
- Design indexing, sharding, and filtering for the target scale
- Tune, load-test, and document the production configuration
What you get
- A recommended vector database and architecture for your workload
- A tuned, benchmarked production configuration
- A migration plan (if moving off an existing store)
- Recall/latency/cost benchmarks and a scaling roadmap
Technologies we use
Frequently asked questions
Which vector database is best?
The honest answer is "it depends." Pinecone is fully managed and low-ops; Weaviate and Qdrant offer more control and self-hosting; pgvector is great if you already run Postgres and are at moderate scale. We compare Pinecone vs Weaviate vs Qdrant in detail and match the choice to your needs.
Can you migrate us off our current vector store?
Yes. We plan and execute migrations between vector databases — re-embedding, backfilling, and cutting over with minimal downtime — and de-risk lock-in along the way.
Do we even need a dedicated vector database?
Not always. At smaller scale, pgvector inside your existing Postgres may be enough. We’ll tell you honestly when a dedicated store earns its keep and when it doesn’t.
Ready to talk vector database consulting?
Tell us about your project and we'll respond within 24 hours with a clear next step.