Qdrant vs turbopuffer
Two sides of the vector database decision: open-source vector store and managed 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 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.
Choose turbopuffer if- You want a hosted index with nothing to operate
Use it whenYou have many separate namespaces, such as one index per customer.
Trade-offNo free tier (paid plans have a monthly minimum), and it's cloud-only.
At a glance
| Qdrant | turbopuffer | |
|---|---|---|
| Used by | 18 makers' products · 68 open-source projects | 8 makers' products · 7 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) | $21/mo Launch |
| Downloads | 696.8k/wk−19% vs npm | 1.1M/wk3.9× vs npm |
| Pricing | Free and open source to self-host; Qdrant Cloud has a free tier plus usage-based paid plans. · paid from Usage-based, no minimum | Usage-based tiers with a monthly minimum; higher tiers add SSO, audit logs, and enterprise/BYOC deployments. · paid from $16/mo minimum usage |
| Free tier | Yes | No |
| Open source | Yes · self-hostable | No |
| Incidents, 90 daysfrom its status page | no public status feed | 4 (4 major) |
Cost as you grow
At 100k vectors Qdrant costs less ($0 vs $17); from about 500k vectors turbopuffer does ($19 vs $68). They're different kinds of tool — open-source vector store and managed vector store — so the prices don't buy the same thing.
The numbers, plan by plan
| Vectors stored (1,536 dimensions, about 6 GB per million) | Qdrant | turbopuffer |
|---|---|---|
| 0.1 | $0 Free | $17 Launch |
| 0.5 | $68 Standard (1 node, 1 vCPU / 8 GiB) | $19 Launch |
| 1 | $103 Standard (3 nodes, 0.5 vCPU / 4 GiB each) | $21 Launch |
| 5 | $410 Standard (3 nodes, 2 vCPU / 16 GiB each) | $179 Launch |
| 10 | $820 Standard (3 nodes, 4 vCPU / 32 GiB each) | $430 Launch |
| 50 | $4,374 Standard (2 nodes, 32 vCPU / 256 GiB each) | $3,173 Launch |
| 100 | $8,747 Standard (4 nodes, 32 vCPU / 256 GiB each) | $7,576 Scale |
From each vendor's pricing page: Qdrant, turbopuffer.
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 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.
On turbopufferNo maker quote about turbopuffer for vector database yet.
Loved and watch-outs
Themes that recur in makers' words and Hacker News comments, each linked to what it summarises.
Qdrantturbopuffer- Storing vectors on object storage with a cache in front keeps large-scale search cheap. HNHN 2HN 3turbopuffer.com
- Handles hundreds of millions to billions of documents, a common next step when pgvector runs out. turbopuffer.comturbopuffer.com 2turbopuffer.com 3HN
- Operationally simple, with BM25, attribute filtering, recall tuning and consistency options built in. HNHN 2HN 3HN 4
Who uses each
AICamp
April
Omniflow
Solver
Superflex
Trag
falWhat makers pair each with
With Qdrant
WiseWorld
Conva.AI
cognee
Lyzr
Trieve
Truelink
Byterover
VoltAgent
ZamsWith turbopuffer
Dub
Mastrapuppyone-cloudDub
LinearMastrapuppyone-cloudVision-AgentsMastraVision-AgentsMastrapuppyone-cloudMastrapuppyone-cloudNeither 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 →vs turbopuffer →
PineconeManaged vector storeA fully hosted index with nothing to operate, sized by usage.vs Qdrant →vs turbopuffer →
WeaviateOpen-source vector storeHybrid search that mixes keyword and vector results, with built-in modules that can create embeddings for you.vs Qdrant →