When should I pick Pinecone over Qdrant?

Pick Pinecone if: You want a hosted index with nothing to operate.

When should I pick Qdrant over Pinecone?

Pick Qdrant if: You need an open-source store with heavy filtering or hybrid search, self-hosted or managed.

Which is cheaper, Pinecone or Qdrant?

At vectors stored 1 million vectors, queries 1 million queries, vectors written or updated 500k writes: Pinecone $113/mo (Standard), Qdrant $103/mo (Standard (3 nodes, 0.5 vCPU / 4 GiB each)), at list prices.

Which do more makers use for vector database?

Among makers' products we track: Pinecone 55, Qdrant 18.

Are people switching from Pinecone to Qdrant?

Public GitHub pull requests since 2024-10-02: 3 from Pinecone to Qdrant.

Pinecone vs Qdrant for vector database: which fits you

Which fits you

Choose Pinecone if

You want a hosted index with nothing to operate

Use it whenYou want vector search without running any infrastructure.

Trade-offClosed source and cloud-only, so leaving means re-indexing somewhere else.

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

	Pinecone	Qdrant
Used by	55 makers' products · 23 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	$113/mo Standard	$103/mo Standard (3 nodes, 0.5 vCPU / 4 GiB each)
Moved to it on GitHubpull requests since Oct 2024	fewer than 3	3 from Pinecone
Downloads	822.6k/wk−35% vs npm	696.8k/wk−19% vs npm
Pricing	Free Starter tier; Builder $20/month flat, Standard usage-based with a $50/month minimum, plus an Enterprise tier. · paid from $20/mo	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	No	Yes · self-hostable
Incidents, 90 daysfrom its status page	9 (6 major)	no public status feed

Cost as you grow

Both cost $0 up to 100k vectors; from 500k vectors Pinecone costs less ($20 vs $68); from about 1M vectors Qdrant does ($103 vs $113). They're different kinds of tool — managed vector store and open-source vector store — so the prices don't buy the same thing.

Qdrant Pineconex: vectors stored (1,536 dimensions, about 6 gb per million) (million vectors), other usage scaled with it · cheapest usable plan at each point, list prices · try your own numbers

The numbers, plan by plan

Vectors stored (1,536 dimensions, about 6 GB per million)	Pinecone	Qdrant
0.1	$0 Starter	$0 Free
0.5	$20 Builder	$68 Standard (1 node, 1 vCPU / 8 GiB)
1	$113 Standard	$103 Standard (3 nodes, 0.5 vCPU / 4 GiB each)
5	$2,532 Standard	$410 Standard (3 nodes, 2 vCPU / 16 GiB each)
10	$9,987 Standard	$820 Standard (3 nodes, 4 vCPU / 32 GiB each)
50	$246,797 Standard	$4,374 Standard (2 nodes, 32 vCPU / 256 GiB each)
100	$985,753 Standard	$8,747 Standard (4 nodes, 32 vCPU / 256 GiB each)

From each vendor's pricing page: Pinecone, Qdrant.

Who moves from one to the other

Public pull requests on GitHub since Oct 2024 whose title says "Pinecone to Qdrant" or the reverse — real code changes, by developers in general rather than makers only.

Fewer than 3 pull requests move from Qdrant to Pinecone.

Pinecone →

Qdrant3 PRs

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

All matching pull requests on GitHub ↗

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 Pinecone

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.

TwelveLabs, the makerSep 2026 ↗

Though we are using function tools to retrieve information from our integrations in real-time, we also use Pinecone to retrieve relevant long-term information.

Tometo, the makerSep 2026 ↗

We needed a vector database that's fast, reliable, and serverless for our RAG system. Pinecone was the easiest to set up and performs consistently at scale.

Starnus, the makerOct 2026 ↗

35 more on the Pinecone page →

On Qdrant

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

Conva.AI, the makerSep 2026 ↗

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.

AICamp, the makerSep 2026 ↗

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.

Zams, the makerSep 2026 ↗

12 more on the Qdrant page →

Loved and watch-outs

Themes that recur in makers' words and Hacker News comments, each linked to what it summarises.

Pinecone

Most loved

Getting started is quick, with clear documentation and a generous free tier. PH PH 2 PH 3 PH 4
Handles large vector volumes with low latency as usage grows. PH PH 2
The serverless tier keeps costs down while iterating and at scale. PH PH 2 PH 3

Watch-outs

It is one more database to run and sync, when Postgres or plain search often covers the need. HN HN 2 HN 3 HN 4
Its core features have become a commodity, and it was late to integrated embeddings compared with rivals. HN HN 2 HN 3 HN 4
It stores embeddings without the source chunk, unlike most other vector databases. HN

On Product Hunt: 4.9★, 74 reviews · mentioned most: ease of use, scalability, high-performance vector database

Qdrant

Most loved

It is fast and scales with strong price-performance, helped by its Rust core. PH
Payload filtering and hybrid semantic plus boolean search work together. PH
Runs easily self-hosted in Docker, a common pick for local RAG and agent memory. PH HN HN 2 HN 3

Watch-outs

As a separate server it is slower than in-process stores for small local datasets. HN
Setting up and operating a vector database is overkill for teams that just need working search. HN HN 2

On Product Hunt: 5.0★, 23 reviews · mentioned most: fast performance, semantic search, excellent documentation

Who uses each

Pinecone55 makers' products

Human Behavior InboxAgents

Kick

Kipps AI Kroolo

Kuse +43 more

Qdrant18 makers' products

Used by both — often one replacing the other, or each for a different part of the product

VoltAgent

What makers pair each with

With Pinecone

Neither 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 Pinecone →vs Qdrant →

WeaviateOpen-source vector storeHybrid search that mixes keyword and vector results, with built-in modules that can create embeddings for you.vs Pinecone →vs Qdrant →

turbopufferManaged vector storeVery large or many-tenant indexes where storing everything on object storage keeps cost down.vs Pinecone →vs Qdrant →