LanceDB vs Qdrant
Two sides of the vector database decision: embedded / local-first and open-source vector store. When each fits, what it costs, who moves from one to the other, and what makers who chose it say.
Which fits you
Choose LanceDB if- An embedded vector database that stores vectors and data as files on local disk or object storage, with no server to run.
Use it whenYou want vectors in your app process or a serverless function, backed by S3-style storage.
Trade-offMany concurrent writers need care, and the managed cloud is a separate paid product.
Choose Qdrant if- You need an open-source store with heavy filtering or hybrid search, self-hosted or managed
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.
At a glance
| LanceDB | Qdrant | |
|---|---|---|
| Used by | 3 makers' products · 39 open-source projects | 18 makers' products · 68 open-source projects |
| Cost at default usagevectors stored 1 million vectors, queries 1 million queries, vectors written or updated 500k writes | — | $103/mo Standard (3 nodes, 0.5 vCPU / 4 GiB each) |
| Downloads | 1.5M/wk4.7× vs npm | 696.8k/wk−19% vs npm |
| Pricing | Free (open source); LanceDB Cloud billed by usage, Enterprise by contract. | Free and open source to self-host; Qdrant Cloud has a free tier plus usage-based paid plans. · paid from Usage-based, no minimum |
| Free tier | Yes | Yes |
| Open source | Yes · self-hostable | Yes · self-hostable |
What makers say
Makers on using it for vector database, from Product Hunt and Starter Story interviews, each linked to the source. Products with a page of their own and fuller notes first.
On LanceDBNo maker quote about LanceDB for vector database yet.
On QdrantAfter 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
Thanks to Qdrant, we utilize it as a vector database to store our knowledge base and uploaded file data. Our RAG would not be possible without it.
We evaluated a bunch of vector DBs—and Qdrant stood out for its blazing speed, filtering, and hybrid search. It's the unsung hero that lets our AI agents recall and reason across docs, CRMs, and conversations in milliseconds.
Loved and watch-outs
Themes that recur in makers' words and Hacker News comments, each linked to what it summarises.
LanceDBNothing that recurs in what we collected yet.QdrantWho uses each
LanceDB3 makers' products
CodeRabbit
AICamp
April
Omniflow
Solver
Superflex
TragWhat makers pair each with
With LanceDB
Mastra
cogneemonitor3rd-devsAdalFlowAgent365-Samplesai-agents-frameworksaider-deskAingDeskapi-for-open-llmapp-controlleratomAutoChainand 17 more
CrewAIGraphRAG-Local-UIJamAIBase
LlamaIndexm_flowand 5 moreWith Qdrant
WiseWorld
Conva.AIcognee
Lyzr
Trieve
Truelink
Byterover
VoltAgent
ZamsNeither quite right?
See the whole vector database decision →
pgvectorInside your databaseApps already on Postgres that want vector search in the same database, joined with normal tables.
ChromaEmbedded / local-firstPrototyping RAG on your laptop with a pip or npm install and no server to run.vs Qdrant →
PineconeManaged vector storeA fully hosted index with nothing to operate, sized by usage.vs Qdrant →
WeaviateOpen-source vector storeHybrid search that mixes keyword and vector results, with built-in modules that can create embeddings for you.vs Qdrant →
turbopufferManaged vector storeVery large or many-tenant indexes where storing everything on object storage keeps cost down.vs LanceDB →vs Qdrant →