What are common mistakes when buying a vector database?

Buying a vector database is easy. Choosing one that performs well in production, scales economically, and doesn't create expensive migration problems is much harder. The biggest mistake is choosing a database based on headline performance or popularity rather than your actual workload. Here are 10 common mistakes to avoid when evaluating vendors in 2026. 10 common mistakes when buying a vector database Choosing based on benchmark leaderboards (Very common) Vendors advertise impressive search…

1. Pinecone
2. Qdrant
3. Weaviate
4. PostgreSQL

Common mistakes when buying a vector database include: Choosing from public benchmarks alone. Benchmark results may use different datasets, dimensions, filters, recall targets, and hardware than your workload. Test candidates on your own corpus and representative queries. Ignoring filtered-search performance. A system may perform well on unfiltered nearest-neighbor search but lose recall when applying restrictive metadata filters. Test filtering early, not just in a demo. Buying for unrealistic…

1. PostgreSQL — PostgreSQL with pgvector or another current platform may be sufficient, avoiding a second system, synchronization work, and additional operational complexity.

When selecting and architecting a vector database (or adopting vector search infrastructure), engineering teams often focus too heavily on raw vendor benchmarks rather than real-world operational constraints.[](https://www.splunk.com/enus/blog/artificial-intelligence/vector-database-architectures.html) The most common mistakes span architectural planning, cost control, and operational readiness: Architecture & Sizing Mistakes

1. Faiss — Confusing an In-Memory Library with a Production Database: Adopting a barebones, in-memory vector library (like a local FAISS index) for a multi-tenant production app instead of a full-featured vector database.