Choosing a Vector Database
When PostgreSQL with pgvector is enough, when to use a search engine and when a dedicated vector database is worth it.
Who it's for
Engineers building retrieval systems.
Contents
- 01What vector search needs
- 02pgvector
- 03Search engines with vectors
- 04Dedicated vector databases
- 05Filtering and permissions
- 06Operational considerations
Key takeaways
- Selection criteria
- Scale thresholds
- Filtering trade-offs
- Operational costs
Technologies covered
Services
Put it into practice
RAG Development
Retrieval-augmented generation systems that ground LLM answers in your documents with permissions, citations and measurable accuracy.
Learn moreAI Data Solutions
Data engineering for AI: pipelines, warehouses, feature stores, vector indexes and governance that make AI systems accurate.
Learn moreMore resources
Keep reading
Enterprise AI Architecture Guide
A practical blueprint for the shared AI platform — model gateway, retrieval, evaluation, governance — that lets enterprises deliver AI use cases repeatedly.
Learn moreAI Agent Implementation Guide
How to take AI agents from demo to dependable production system: tool design, permissions, approval gates, shadow mode and reliability metrics.
Learn moreAI Transformation Guide
A leadership guide to moving an organisation from scattered AI experiments to a governed portfolio of production AI capabilities.
Learn moreDiscuss your launch.
Tell us what you're launching. A solution architect will reply with an approach, an implementation timeline and next steps.
