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---
title: pgvector vs Pinecone vs Turbopuffer vs Qdrant (2026)
description: A hands-on comparison of four vector databases — pgvector, Pinecone, Turbopuffer, and Qdrant — tested against a real RAG workload of 2 million vectors at...
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# pgvector vs Pinecone vs Turbopuffer vs Qdrant (2026)

**[Alex CloudStar](https://daily.dev/sources/alexcloudstar)** · [@alexcloudstar](https://daily.dev/alexcloudstar) · 16 min read · 0 upvotes · 0 comments

## Summary

A hands-on comparison of four vector databases — pgvector, Pinecone, Turbopuffer, and Qdrant — tested against a real RAG workload of 2 million vectors at ~1,000 queries/day. Key findings: pgvector wins on cost and simplicity for under 10M vectors if you're already on Postgres (8–25ms p95, ~$30/mo); Qdrant leads on hybrid search and filtering (15–40ms p95, ~$40–80/mo); Turbopuffer offers dramatically lower cost for large corpora but has cold-query latency (300–800ms); Pinecone remains the easiest managed option but costs ~$180/mo at this scale. The author migrated from Pinecone to pgvector for their main workload, kept Qdrant for hybrid search needs, and uses Turbopuffer for a large document archive.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.alexcloudstar.com/blog/vector-database-comparison-2026/>

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---

Tags: [#pgvector](https://daily.dev/tags/pgvector), [#pinecone](https://daily.dev/tags/pinecone), [#qdrant](https://daily.dev/tags/qdrant), [#vector-search](https://daily.dev/tags/vector-search)

[View this post on daily.dev](https://daily.dev/posts/pgvector-vs-pinecone-vs-turbopuffer-vs-qdrant-2026--m1dot7ras)

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