Two years ago, vector databases were an exotic infrastructure component used primarily by AI researchers. Today, they are the data layer for a generation of enterprise AI products — knowledge bases, semantic search, recommendation systems, and the retrieval component of every serious RAG implementation.
In this webinar, we surveyed the competitive landscape, evaluated selection criteria, and walked through architectural patterns from production systems. Here is a summary of the key takeaways.
The Landscape Has Matured
The vector database market has consolidated around a handful of serious contenders: Pinecone (managed, optimised for developer experience), Weaviate (open-source, strong multi-modality), Qdrant (open-source, excellent performance per dollar), and pgvector (Postgres extension, operational simplicity at moderate scale). All major cloud providers now offer managed vector storage as part of their AI platform offerings.
"Choose your vector database based on your operational constraints, not marketing benchmarks. The performance differences between mature options at moderate scale are smaller than the operational differences between managed and self-hosted."
Selection Criteria That Actually Matter
Scale requirements drive the primary decision: managed versus self-hosted. Under 10M vectors, managed options eliminate operational overhead that is rarely worth internalising. Above 50M vectors, cost-optimised self-hosted deployments start to make financial sense, but only if your team has the infrastructure capability to support them.
Hybrid search — combining vector similarity with traditional keyword filtering — is a hard requirement for most enterprise search use-cases. Evaluate candidates on the quality and flexibility of their hybrid search implementation, not just their ANN performance.
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