---
title: "Matching storage to your workload"
description: "Not every workload needs a dedicated vector database. Postgres with pgvector covers most simple use cases; purpose-built engines (Milvus, Qdrant, Pinecone)"
canonical_url: "https://fanout.sh/ai-engineering/lessons/choosing-the-right-db"
md_url: "https://fanout.sh/ai-engineering/lessons/choosing-the-right-db.md"
access: "public"
---

# Matching storage to your workload

Not every workload needs a dedicated vector database. Postgres with pgvector covers most simple use cases; purpose-built engines (Milvus, Qdrant, Pinecone)

## Public overview

Not every workload needs a dedicated vector database. Postgres with pgvector covers most simple use cases; purpose-built engines (Milvus, Qdrant, Pinecone) earn their complexity at scale, and text-search engines like OpenSearch solve a different problem (keywords, not meaning). The decision is really about scale, filters, latency budget, and what your team already operates.

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