Vector Database
Store embeddings and find the most similar ones fast, for semantic search, RAG and recommendations.
The real choice
Inside the database you already run, or a separate vector store. pgvector keeps vectors next to your rows, with one backup and one query language, but tuning large indexes is on you. A dedicated vector store handles scale, filtering and hybrid search for you, at the cost of another service to sync data into.
Pick by situation
Tap the ones that are you — the tools that fit light up below.- You already use Postgres and have up to a few million vectors
pgvector - You're prototyping retrieval on your laptop, or want vectors in files with no server
Chroma - You want a hosted index with nothing to operate
Pinecone
turbopuffer - You need an open-source store with heavy filtering or hybrid search, self-hosted or managed
Qdrant
Weaviate
The contenders
Grouped by the side of the choice they answer, not ranked. Open a row for when to use it, the trade-off and what makers say.
pgvectordatabase · open source · free tierApps already on Postgres that want vector search in the same database, joined with normal tables.70 open source—411.7k/wk+21% vs npm
Use it whenYou have up to a few million vectors and want one system to back up.
Trade-offIndex tuning and memory sizing are your job, and very large indexes can crowd out the rest of the database.
llms.txtNo maker's product we track shows it for this yet · in 70 open-source projects.
Chromadatabase · open source · free tierPrototyping RAG on your laptop with a pip or npm install and no server to run.
13+69 open source$51Starter236.5k/wk−12% vs npm
Use it whenYou're still figuring out whether retrieval works for your use case.
Trade-offFor production you either run its server yourself or move to Chroma Cloud.
llms.txtUsed by Clarm, Conduit, Maximem Synap, Reef, Supaboard AI and 8 more · in 69 open-source projects.
Chroma makes it super easy to manage embeddings for AI apps. We love the open-source focus and how quickly it integrates into RAG pipelines.
Pineconedatabase · free tierA fully hosted index with nothing to operate, sized by usage.
55+23 open source$113Standard822.6k/wk−35% vs npm
Use it whenYou want vector search without running any infrastructure.
Trade-offClosed source and cloud-only, so leaving means re-indexing somewhere else.
llms.txtUsed by AI Context Flow, Brev.io, ClueoChat, CustomGPT, Fleak and 50 more · in 23 open-source projects.
TwelveLabs uses Pinecone to efficiently store and search the vector embeddings produced by our embedding model, enabling fast, scalable retrieval across large video and text datasets.
turbopufferdatabaseVery large or many-tenant indexes where storing everything on object storage keeps cost down.8+7 open source$21Launch1.1M/wk3.9× vs npm
Qdrantdatabase · open source · free tierHeavy metadata filtering alongside vector search, self-hosted from one binary or on its managed cloud.
18+68 open source$103Standard (3 nodes, 0.5 vCPU / 4 GiB each)696.8k/wk−19% vs npm
Use it whenYour queries combine similarity with many filters, such as tenant, date and category.
Trade-offOne more service to deploy and keep in sync with your main database.
llms.txtUsed by AICamp, April, Byterover, cognee, Conva.AI and 13 more · in 68 open-source projects.
After evaluating a bunch of Vector DBs to be our internal vector DB, we finally closed on QDrant because it was the one that scaled the best and had the best price performance ratio
Weaviatedatabase · open source · free tierHybrid search that mixes keyword and vector results, with built-in modules that can create embeddings for you.
16+20 open source$253Flex372.6k/wk−65% vs npm
Use it whenUsers search with both exact terms and meaning, and you want both in one query.
Trade-offMore concepts and configuration to learn than simpler stores; some advanced features need a license key.
llms.txtUsed by cognee, Cortex, Depth, Insight7, Lamatic.ai and 11 more · in 20 open-source projects.
To store all our vector embeddings, now a staple for us to build forward. Their automatic 'load balancing' on which vectors are recently used is a game changer for system optimization
Cost: the cheapest plan that fits vectors stored 1 million vectors, queries 1 million queries, vectors written or updated 500k writes, from list prices. Try your own numbers →
Cost as you grow
Each contender's cheapest usable plan as usage rises.
Who switches to what
Public pull requests on GitHub since Oct 2024 whose title says "X to Y" — real code changes, by developers in general rather than makers only. Pick a flow to see its pull requests.
Qdrant → pgvector9 PRs- Refactor vector storage from Qdrant to Supabase pgvectorunais-08/docs-query-RAG · 2026-09-23
- Feature/qdrant to pgvectorahmedeldamaty20/mini-rag · 2026-08-23
- migrated from qdrant to pgvectorJrahimin/rag-builder · 2026-07-16
- feat(brain): move the vector store from Qdrant to Supabase pgvectorwildlifeai/ww-website · 2026-07-09
- Change from qdrant vector db to postgres pgvectorKicaRonaldOkello/chat_pdf · 2026-07-07
- Migrate from Qdrant to pgvector: update configurations, remove Qdrant references, and adjust indexing logicnandinigthub/Knowledge-hunt · 2026-07-06
- feat: drop Qdrant — migrate fully to PGVector + PGVector semantic cachesuncirkles/nexus-learner · 2026-04-15
- Migrate from Qdrant to PostgreSQL pgvector for vector storagedx-junkyard/episteme-graph · 2026-03-23
- Migrate vector database from Qdrant to pgvectorLucasgarciamdz/tesis-entrenai · 2025-05-21
Chroma → pgvector7 PRs- LOT 27 P3 — audit Chroma to pgvector data mappingcyranoaladin/RAG · 2026-07-15
- feat(db): migrate Chroma embeddings to pgvectoryaronsha/photo-app · 2026-05-22
- Chroma to pgvector migration scriptNYU-ITS/NAGA-open-webui · 2025-12-05
- Chroma to pgvector migration scriptNYU-ITS/NAGA-open-webui · 2025-12-04
- Revert "Revert "Chroma to pgvector migration script""NYU-ITS/NAGA-open-webui · 2025-12-02
- Revert "Chroma to pgvector migration script"NYU-ITS/NAGA-open-webui · 2025-12-01
- Chroma to pgvector migration scriptNYU-ITS/NAGA-open-webui · 2025-11-24
Chroma → Pinecone6 PRs · Compare →- Migrate vectorstore from Chroma to Pineconeparas-the-coder/Agentic-Hybrid-RAG · 2026-05-31
- switched from chroma to pinecone vector dbrchadha/rag-application · 2026-03-17
- feat(rag): Migrate from Chroma to Pinecone and add CI-based indexingrjacaac211/nutriguide-ai · 2026-03-16
- Chroma to pineconeabhiTagline28/youtube-transcript-rag · 2025-09-08
- Change chroma to pineconedanielkwan2004/TechJam2025 · 2025-08-30
- feat: migrate from Chroma to Pinecone vector databaseArohasina/chat-with-websites · 2025-06-30
Chroma → Qdrant4 PRs · Compare →- feat(rag_core): switch retriever backend from Chroma to Qdrant financ…Xeoyeon/Whyfi-v2 · 2026-08-14
- Migrate RAG vector storage from local Chroma to Qdrantseethygerald/myfinancialapp · 2026-05-24
- feat: migrate chroma to qdrantrobbypambudi/RAGforge · 2025-10-08
- Add Chroma DB support as an alternative to Qdranthadv/wisdomforge · 2025-03-27
pgvector → Pinecone3 PRs- feat: migrate vector search from Supabase pgvector to Pinecone serverlesscmtemkin/needham-navigator · 2026-02-20
- Refactor notebook 05: migrate from pgvector to Pinecone architecturealiomraniH/billing-model · 2025-12-13
- changed pgvector to pinecone v-db & restructured codeharikrishnan51688/NewsNeuron · 2025-08-04
Pinecone → Qdrant3 PRs · Compare →- Migrate from Pinecone to Qdrant; add unit tests and update docsganura-epam/pdfchatbot · 2026-03-21
- Migrate vector database from Pinecone to Qdranthunterbryant/HuntBot · 2026-03-09
- Chucking PineCone for RAG , Switching to QdrantAdityaPrakash781/VytalCare · 2025-12-08
Before you choose
If you already use Postgres, start with pgvector; most solo apps never outgrow it. Store the source text and metadata next to each vector so you can filter and re-embed later. Move to a dedicated store only when you hit a real limit — millions of vectors, slow filtered queries, or hybrid search you can't build yourself.
- Storing only vectors and IDs, then having to re-fetch and re-embed everything when you want to filter by date, user or source.
- Forgetting to delete or update vectors when the source record changes, so search keeps returning content users already removed.
- Adding a separate vector service to sync before trying the vector search in the database you already run.
Other options
Real choices most makers here won't need to weigh.
LanceDBEmbedded / local-firstAn embedded vector database that stores vectors and data as files on local disk or object storage, with no server to run.3 makers' products
MongoDBInside your databaseApps already on MongoDB Atlas that want vector search on the same documents, with no second store to sync.
UpstashManaged vector storeA serverless vector index billed per request, alongside Upstash Redis and QStash, reached over HTTP from edge functions.
MilvusOpen-source vector storeVery large collections — hundreds of millions of vectors and up — on a distributed open-source database, or managed as Zilliz Cloud.
FaissEmbedded / local-firstA fast in-memory vector index library for research, batch jobs and apps that load their index at startup.Decided alongside
What the 104 makers' products here chose for their other decisions.
SendGrid
Resend
Postmark
OpenAI API30
Claude15
Gemini API6
Next.js33
React3
Astro2
Amazon Web Services
Cloudflare
Render
Supabase
Neo4j
Plausible Analytics
Stripe
Orb
Polar
LangChain
OpenAI Agents SDK
LangGraph