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Rank #3 of 7 in Vector Databases & Memory Stores

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Qdrant

Open Source

Qdrant Solutions GmbH

qdrant.tech ↗Try it →
PA Score32/100AGENT-READY57/100BUILT-IN AI9/100API5/100
MCP✓CLI—API✓
★ 34.5k▲ 5.5k/yrnpm 586.8k/wk★ +137npm/wk -191.2kpypi/wk +101.4k
ArenasVector Databases & Memory Stores#3/7

Access

Open app ↗API docs ↗MCP docs ↗

Install

dockerdocker run -p 6333:6333 qdrant/qdrant
pippip install qdrant-client

Compare head-to-head

Pinecone logovs Pineconevs WeaviateChroma logovs ChromaMilvus logovs MilvusHelixDB logovs HelixDBLanceDB logovs LanceDB

Alternatives to Qdrant →

Showcase

Qdrant homepage screenshot
homepage · captured Sep 2026 · view live ↗
Qdrant docs screenshot
docs · captured Sep 2026 · view live ↗

Try itExperimental

See what an agent can do with Qdrant before you ever sign up. Pick a story: recorded sessions replay real probe-harness transcripts; sandboxed self-drive sessions are designed and gated (docs/TRY-IT.md).

$docker run -d --name pa-qdrant-probe -p 16333:6333 qdrant/qdrant && curl -X PUT localhost:16333/collections/pa_probe -d '{"vectors":{"size":8,"distance":"Cosine"}}' && curl localhost:16333/collectionsrecorded session — replayed, not live
recorded 2026-09-04 · exit 0 · captured verbatim by our probe harness, secrets redacted

Verified integrations

No integration evidence found in our corpus for this product yet — that means none was found, never that it doesn’t integrate.

By theme — the product's score on each story themeBy theme

Agenticness — how well agents can access and operate the productAgenticnessevidence →

How well agents can access and operate the product

35.3/100

Automation depth — how much of the product can run unattendedAutomation depthevidence →

How much of the product can run unattended

6.0/100

Data lifecycle — stories about data lifecycle in this arenaData lifecycleevidence →

Stories about data lifecycle in this arena

44.7/100

Deployment modes — stories about deployment modes in this arenaDeployment modesevidence →

Stories about deployment modes in this arena

46.4/100

Embeddings pipeline — stories about embeddings pipeline in this arenaEmbeddings pipelineevidence →

Stories about embeddings pipeline in this arena

12.0/100

Filtering metadata — stories about filtering metadata in this arenaFiltering metadataevidence →

Stories about filtering metadata in this arena

33.6/100

Multi tenancy scale — stories about multi tenancy scale in this arenaMulti tenancy scaleevidence →

Stories about multi tenancy scale in this arena

66.9/100

Openness — open source, data portability, and self-hosting storiesOpennessevidence →

Open source, data portability, and self-hosting stories

59.2/100

Performance latency — stories about performance latency in this arenaPerformance latencyevidence →

Stories about performance latency in this arena

12.0/100

Pricing plans — plan structure and value — what each tier costs and what it unlocksPricing plansevidence →

Plan structure and value — what each tier costs and what it unlocks

26.7/100

Privacy posture — data-handling and privacy storiesPrivacy postureevidence →

Data-handling and privacy stories

14.7/100

Sdk integrations — stories about sdk integrations in this arenaSdk integrationsevidence →

Stories about sdk integrations in this arena

40.0/100

Search quality hybrid — stories about search quality hybrid in this arenaSearch quality hybridevidence →

Stories about search quality hybrid in this arena

58.2/100

Story verdicts — every judged story with its evidenceStory verdicts

What’s free: 6 free · 0 paid · 0 enterprise · 28 not stated in evidence

?

Sorted by importance (agentic first) (high → low) · 53/53 stories · click a row’s chevron for the rationale and evidence

Connect an agent via an official MCP server G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness3full8/10T

Drive the product through a documented public API G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness3full8/10T

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

ai-native userAgenticness — how well agents can access and operate the productAgenticness3none0/10

Plug MCP servers into this product so it can use their tools G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness3n/auntestednone yet

Build against official SDKs G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2full8/10X

Issue scoped/least-privilege API credentials for an agent G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2full8/10C

Point an agent at llms.txt or agent-oriented docs G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2full8/10T

Run the product headlessly / in CI for automation G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2partial6/10X

Operate the product with natural-language commands G

Agentic features

ai-native userAgenticness — how well agents can access and operate the productAgenticness2partial5/10T

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

ai-native userAgenticness — how well agents can access and operate the productAgenticness2none0/10

Explore an interactive API reference with runnable examples G

Api quality

ai-native userAgenticness — how well agents can access and operate the productAgenticness2none0/10

Rely on versioned APIs with a documented deprecation policy G

Api quality

ai-native userAgenticness — how well agents can access and operate the productAgenticness2none0/10

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

ai-native userAgenticness — how well agents can access and operate the productAgenticness2noneuntestednone yet

Set up automations that run autonomously in the background G

Agentic features

ai-native userAgenticness — how well agents can access and operate the productAgenticness2n/auntestednone yet

Subscribe to events via webhooks G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2noneuntestednone yet

Use an official CLI G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2noneuntestednone yet

Test against a sandbox environment without touching production data G

Api quality

ai-native userAgenticness — how well agents can access and operate the productAgenticness1partialfree6/10X

Run approximate nearest-neighbor similarity search over embeddings with configurable distance metrics C

Core search

developerSearch quality hybrid — stories about search quality hybrid in this arenaSearch quality hybrid3full9/10X

Self-host the core product G

ai-native userOpenness — open source, data portability, and self-hosting storiesOpenness3fullfree9/10X

Combine dense vector search with keyword or sparse (BM25-style) signals in one hybrid query with fusion ranking C

Hybrid

developerSearch quality hybrid — stories about search quality hybrid in this arenaSearch quality hybrid3full8/10C

Isolate many tenants cheaply using namespaces, partitions, or per-tenant collections with documented limits C

Tenancy

platform-engineerMulti tenancy scale — stories about multi tenancy scale in this arenaMulti tenancy scale3full7/10C

Filter vector search by structured metadata conditions without wrecking recall or latency C

Filtering

developerFiltering metadata — stories about filtering metadata in this arenaFiltering metadata3partial6/10X

Export all of my data in open formats and leave G

ai-native userOpenness — open source, data portability, and self-hosting storiesOpenness3partialfree5/10X

Have the database generate embeddings at ingest and query time using built-in or configured model providers, instead of running a separate embedding pipeline C

Embeddings

ml-engineerEmbeddings pipeline — stories about embeddings pipeline in this arenaEmbeddings pipeline3disputed4/10D

Define rules that trigger actions automatically on events G

ai-native userAutomation depth — how much of the product can run unattendedAutomation depth3noneuntestednone yet

Prevent my data from being used to train AI models G

ai-native userPrivacy posture — data-handling and privacy storiesPrivacy posture3noneuntestednone yet

Back up collections with snapshots and restore them C

Backup

platform-engineerData lifecycle — stories about data lifecycle in this arenaData lifecycle2full8/10C

Build against official SDKs in the major languages (Python, TypeScript, Go, Java) G

Sdks

developerSdk integrations — stories about sdk integrations in this arenaSdk integrations2full8/10X

Enforce granular access control (API keys, roles, per-collection permissions) on database operations C

Tenancy

platform-engineerMulti tenancy scale — stories about multi tenancy scale in this arenaMulti tenancy scale2full8/10C

Read the product's source under an open license G

ai-native userOpenness — open source, data portability, and self-hosting storiesOpenness2fullfree8/10X

Run the database embedded in-process or as a lightweight local instance for development and small workloads C

Local dev

developerDeployment modes — stories about deployment modes in this arenaDeployment modes2full8/10X

Scale beyond one node with sharding or distributed deployment C

Scaling

platform-engineerMulti tenancy scale — stories about multi tenancy scale in this arenaMulti tenancy scale2full8/10X

Choose where my data is stored (region/residency) G

ai-native userPrivacy posture — data-handling and privacy storiesPrivacy posture2partial6/10C

Do everything through the API that I can do in the UI G

ai-native userOpenness — open source, data portability, and self-hosting storiesOpenness2partial6/10T

Replicate data across nodes or zones for high availability with a documented consistency model C

Scaling

platform-engineerMulti tenancy scale — stories about multi tenancy scale in this arenaMulti tenancy scale2partial6/10X

Run keyword/full-text search over documents inside the database without bolting on a separate search engine C

Hybrid

developerSearch quality hybrid — stories about search quality hybrid in this arenaSearch quality hybrid2partial6/10C

Bulk-import and bulk-export vectors plus metadata in documented formats C

Portability

developerData lifecycle — stories about data lifecycle in this arenaData lifecycle2partial5/10C

Control data retention and deletion G

ai-native userPrivacy posture — data-handling and privacy storiesPrivacy posture2partial5/10C

Express rich filter conditions (ranges, geo, nested boolean logic, array membership) in queries C

Filtering

developerFiltering metadata — stories about filtering metadata in this arenaFiltering metadata2partial5/10C

Upsert and delete records continuously and have changes reflected in search results quickly, with documented freshness/consistency behavior C

Freshness

developerData lifecycle — stories about data lifecycle in this arenaData lifecycle2partial4/10X

Use a fully managed cloud version of the database with programmatic provisioning C

Managed cloud

developerDeployment modes — stories about deployment modes in this arenaDeployment modes2partialfree4/10X

Enable vector quantization or compression to cut memory and storage cost with a documented accuracy trade-off C

Index tuning

ml-engineerPerformance latency — stories about performance latency in this arenaPerformance latency2partial3/10C

Perform bulk operations across many items at once G

ai-native userAutomation depth — how much of the product can run unattendedAutomation depth2partial3/10X

Tune index parameters (HNSW graph settings, index types) to trade recall against latency and memory C

Index tuning

ml-engineerPerformance latency — stories about performance latency in this arenaPerformance latency2partial3/10C

Pay serverless usage-based pricing with transparent per-unit costs instead of provisioning fixed clusters G

Pricing

developerPricing plans — plan structure and value — what each tier costs and what it unlocksPricing plans2none0/10

Plug the database into RAG and agent frameworks (LangChain, LlamaIndex, etc.) through maintained first-class integrations C

Integrations

ml-engineerSdk integrations — stories about sdk integrations in this arenaSdk integrations2none0/10

Rerank search results with built-in or first-party-integrated reranking models C

Reranking

ml-engineerSearch quality hybrid — stories about search quality hybrid in this arenaSearch quality hybrid2none0/10

See published benchmarks or measured latency/recall numbers backing the database's performance claims C

Benchmarks

platform-engineerPerformance latency — stories about performance latency in this arenaPerformance latency2none0/10

Opt out of telemetry and usage tracking G

ai-native userPrivacy posture — data-handling and privacy storiesPrivacy posture2noneuntestednone yet

Schedule recurring jobs or workflows G

ai-native userAutomation depth — how much of the product can run unattendedAutomation depth2n/auntestednone yet

Prototype on a meaningful free tier before paying anything G

Pricing

developerPricing plans — plan structure and value — what each tier costs and what it unlocksPricing plans1fullfree8/10X

Deploy to production on Kubernetes with an official Helm chart or operator C

Self managed

platform-engineerDeployment modes — stories about deployment modes in this arenaDeployment modes1partial4/10C

Version, review, and roll back my automations G

ai-native userAutomation depth — how much of the product can run unattendedAutomation depth1noneuntestednone yet

Opportunities — the stories that would move this product's scores, from its own judged verdictsOpportunitiestop 8 of 33 stories with headroom

What would move Qdrant’s scores — derived from its own judged verdicts, biggest headroom first. Each line quotes what the judge found missing; shipping it (or evidencing it publicly) is the fix.

  1. Agenticness — how well agents can access and operate the productDelegate tasks to a built-in AI assistant inside the product

    nonemoves Built-in AIimpact 45

    Evidence shows only external integrations (agent skills for coding assistants, an MCP server for external agents to query Qdrant) but no built-in AI assistant embedded within the Qdrant product itself that a user could delegate tasks to.

  2. Automation depth — how much of the product can run unattendedDefine rules that trigger actions automatically on events

    nonemoves PA Scoreimpact 30

    No evidence in the pack shows Qdrant supporting rule-based automation or event-triggered actions (e.g., webhooks, alerts, triggers on data changes); documentation covers hybrid search, filtering, sharding, snapshots, and security only.

  3. Privacy posture — data-handling and privacy storiesPrevent my data from being used to train AI models

    nonemoves PA Scoreimpact 30

    Missing: explicit vendor privacy policy or ToS statement on not using customer data for model training, evidence of an opt-out setting, or independent confirmation of this practice.

  4. Agenticness — how well agents can access and operate the productGet AI-generated insights and suggestions from my data inside the product

    nonemoves Built-in AIimpact 30

    Qdrant is positioned as a vector search infrastructure/database with client libraries, deployment, and security features, but the evidence pack contains no mention of any built-in AI-generated insights, analytics, or suggestion features surfaced to users inside the product itself.

  5. Agenticness — how well agents can access and operate the productUse an official CLI

    nonemoves agent-readyimpact 30

    The evidence pack shows client libraries in multiple languages, Docker deployment, and agent skills for IDEs, but no mention of an official Qdrant CLI tool for AI-native workflows.

  6. Agenticness — how well agents can access and operate the productSubscribe to events via webhooks

    nonemoves agent-readyimpact 30

    No evidence anywhere in the pack of webhook subscriptions, event notifications, or pub/sub-style triggers from Qdrant; the product's evidence covers storage, search, deployment, security, and clients but nothing about event-driven webhook subscriptions.

  7. Agenticness — how well agents can access and operate the productExplore an interactive API reference with runnable examples

    nonemoves API qualityimpact 30

    The evidence pack shows no interactive API reference with runnable examples; a probe explicitly found no OpenAPI/Swagger spec at any candidate URL, and no docs mention runnable code snippets or an API playground.

  8. Agenticness — how well agents can access and operate the productDownload a machine-readable API spec (OpenAPI or equivalent)

    nonemoves API qualityimpact 30

    No evidence of a downloadable OpenAPI/machine-readable spec; direct probes for openapi.json/swagger.json paths all returned 404, and no doc page references an API spec download for Qdrant's REST/gRPC API.

Showing the top 8 of 33 — every none/partial verdict in the story verdicts table is headroom.

Think a verdict is wrong? Every verdicts-table row has a Flag link — see the methodology.

Coverage map — which docs area, API section, or community source covers which judged storiesCoverage map5 surfaces · 35 covered stories

Where the cited evidence behind each covered verdict came from — the same citations the verdicts table shows, no extra judging.

Documentation docs30 stories

Hacker News19 stories

GitHub README13 stories

OpenAPI spec2 stories

llms.txt1 story

Probe proofs — replayable recordings from the probe harnessProbe proofs

Replayable recordings from our probe harness — see the Prove-It protocol to submit one.

$docker run -d --name pa-qdrant-probe -p 16333:6333 qdrant/qdrant && curl -X PUT localhost:16333/collections/pa_probe -d '{"vectors":{"size":8,"distance":"Cosine"}}' && curl localhost:16333/collectionsreproduced
$ docker run -d --name pa-qdrant-probe -p 16333:6333 qdrant/qdrant && curl -X PUT localhost:16333/collections/pa_probe -d '{"vectors":{"size":8,"distance":"Cosine"}}' && curl localhost:16333/collections
f28bd12954cc697b2bfdfdd2562c65e6b8691d55d782948f333d2ca20c6a1b12
{"title":"qdrant - vector search engine","version":"1.19.1","commit":"6ab21cac18ebb6f4ae29102c7f8f5cc11affd5de"}
{"result":true,"status":"ok","time":0.028641295}
{"result":{"collections":[{"name":"pa_probe"}]},"status":"ok","time":3.542e-6}
proves: Self-host the core productproves: Drive the product through a documented public APIrecorded 2026-09-04
$echo '<jsonrpc initialize>' | QDRANT_LOCAL_PATH=/tmp/pa-qdrant-mcp COLLECTION_NAME=pa-probe uvx mcp-server-qdrantreproduced
$ echo '<jsonrpc initialize>' | QDRANT_LOCAL_PATH=/tmp/pa-qdrant-mcp COLLECTION_NAME=pa-probe uvx mcp-server-qdrant
⠋ Resolving dependencies...                                                     
⠙ Resolving dependencies...                                                     
⠋ Resolving dependencies...                                                     
⠙ Resolving dependencies...                                                     
⠙ mcp-server-qdrant==0.8.1                                                      
⠙ fastmcp==2.7.0                                                                
⠙ fastembed==0.8.0                                                              
⠙ pydantic==2.11.10                                                             
⠙ pydantic-core==2.33.2                                                         
⠙ qdrant-client==1.19.0                                                         
⠙ authlib==1.8.0                                                                
⠙ exceptiongroup==1.3.1                                                         
⠙ httpx==0.28.1                                                                 
⠙ httpx==0.28.1                                                                 
⠙ mcp==1.29.1                                                                   
⠙ pydantic==2.11.10                                                             
⠙ openapi-pydantic==0.5.1                                                       
⠙ python-dotenv==1.2.3                                                          
⠙ rich==15.0.0                                                                  
⠙ typer==0.27.2                                                                 
⠙ huggingface-hub==1.30.0                                                       
⠙ loguru==0.7.3                                                                 
/Users/judegomila/.cache/uv/archive-v0/64cPnC3CPPvyL5pG/lib/python3.13/site-packages/fastmcp/server/auth/providers/bearer.py:6: AuthlibDeprecationWarning: authlib.jose module is deprecated, please use joserfc instead.
It will be compatible before version 2.0.0.
  from authlib.jose import JsonWeb[redacted], JsonWeb[redacted]
[09/04/26 15:25:15] INFO     Starting MCP server                   server.py:981
                             'mcp-server-qdrant' with transport                 
                             'stdio'                                            
{"jsonrpc":"2.0","id":1,"result":{"protocolVersion":"2025-06-18","capabilities":{"experimental":{},"prompts":{"listChanged":false},"resources":{"subscribe":false,"listChanged":false},"tools":{"listChanged":true}},"serverInfo":{"name":"mcp-server-qdrant","version":"1.29.1"}}}
proves: Connect an agent via an official MCP serverrecorded 2026-09-04

Claims vs evidence — vendor claims reconciled against independent verdictsClaims vs evidence

8 of 16 testable claims verified · 1 contradicted → integrity 38/100

17 distinct capability claims found in Qdrant’s own claimed-docs/GitHub materials, reconciled against our judge’s independent verdicts.

8

Verified

7

Unverified

1

Contradicted

19

Undersold

Verified (10)
Unverified (8)
Contradicted (1)
Undersold (19)

Pricing signals

pricing unclearThe page describes tiers (Free, Standard, Premium, Hybrid, Private Cloud) and mentions usage-based billing by vCPU/RAM/disk/tokens, but prints no numeric dollar figures or per-unit rates that can be extracted without conversion. page checked ↗

Checked 2026-09-07. We never estimate a price we didn’t extract.

Business model

open-sourcefree-tierusage-basedenterprise-custom

Apache-2.0 open-source vector search engine, free to self-host; Qdrant Cloud offers a free 1GB cluster, resource-based managed pricing, hybrid cloud, and private enterprise deployments.

pricing ↗

Score trend

How this product’s scores have moved as evidence and verdicts are re-derived — a point per change, not per day.

PA Score32 (Sep 4 '26)32 (Sep 15 '26)
Agent-ready57 (Sep 4 '26)57 (Sep 15 '26)

Try Experimental

Run it in the microterminal →

Recorded agent sessions — and a live MCP handshake where the vendor ships one.

Flag

⚑ Flag a verdict

Think a verdict is wrong? Opens a prefilled GitHub issue — or use the ⚑ next to any verdict above.

Badge

Embed this product's score badge →

Hotlinked SVG — always shows the live current score.

For agents

This page as markdown/llms.txt

Data

Evidence (JSON)Verdicts (JSON)

Agent surface uptime llms.txt 100% (30d, checked every 6h since Sep 8 '26)