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Rank #5 of 7 in Vector Databases & Memory Stores

Weaviate

Open Source Built-in AI assistant

Weaviate B.V.

weaviate.io ↗Try it →
PA Score29/100AGENT-READY43/100BUILT-IN AI34/100API3/100
MCP✓CLI—API✓
★ 16.8k▲ 1.6k/yrnpm 276.3k/wkpypi 2.2M/wk★ +20npm/wk -122.9kpypi/wk +816.2k
ArenasVector Databases & Memory Stores#5/7

Access

Open app ↗API docs ↗MCP docs ↗

Install

dockerdocker run -p 8080:8080 -p 50051:50051 cr.weaviate.io/semitechnologies/weaviate:latest
pippip install -U weaviate-client

Compare head-to-head

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Alternatives to Weaviate →

Showcase

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

Try itExperimental

See what an agent can do with Weaviate 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-weaviate-probe -p 18080:8080 -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true cr.weaviate.io/semitechnologies/weaviate:latest && curl -X POST localhost:18080/v1/schema -d '{"class":"PaProbe","vectorizer":"none"}' && curl localhost:18080/v1/schemarecorded 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

32.9/100

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

28.8/100

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

Stories about embeddings pipeline in this arena

80.0/100

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

Stories about filtering metadata in this arena

10.8/100

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

Stories about multi tenancy scale in this arena

36.4/100

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

Open source, data portability, and self-hosting stories

50.0/100

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

Stories about performance latency in this arena

0.0/100

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

10.0/100

Privacy posture — data-handling and privacy storiesPrivacy postureevidence →

Data-handling and privacy stories

6.0/100

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

Stories about sdk integrations in this arena

52.0/100

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

Stories about search quality hybrid in this arena

58.0/100

Story verdicts — every judged story with its evidenceStory verdicts

What’s free: 6 free · 0 paid · 0 enterprise · 26 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 productAgenticness3partial5/10C

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 productAgenticness3none0/10

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

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2full9/10T

Build against official SDKs G

Agent access

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

Operate the product with natural-language commands G

Agentic features

ai-native userAgenticness — how well agents can access and operate the productAgenticness2full7/10T

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 productAgenticness2partial6/10C

Run the product headlessly / in CI for automation G

Agent access

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

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

Agent access

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

Set up automations that run autonomously in the background G

Agentic features

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

Use an official CLI G

Agent access

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

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

Api quality

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

Explore an interactive API reference with runnable examples G

Api quality

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

Rely on versioned APIs with a documented deprecation policy G

Api quality

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

Subscribe to events via webhooks 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 productAgenticness1partialfree4/10C

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 hybrid3full9/10X

Self-host the core product G

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

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 pipeline3full8/10C

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 scale3partial6/10C

Export all of my data in open formats and leave G

ai-native userOpenness — open source, data portability, and self-hosting storiesOpenness3partial5/10X

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 hybrid3partialfree5/10T

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

Filtering

developerFiltering metadata — stories about filtering metadata in this arenaFiltering metadata3partial3/10T

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 posture3n/auntestednone 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 integrations2full8/10C

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 hybrid2full8/10C

Scale beyond one node with sharding or distributed deployment C

Scaling

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

Read the product's source under an open license G

ai-native userOpenness — open source, data portability, and self-hosting storiesOpenness2fullfree7/10T

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

Portability

developerData lifecycle — stories about data lifecycle in this arenaData lifecycle2partial6/10C

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 scale2partial5/10C

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 hybrid2partial5/10C

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 modes2partial5/10X

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

Managed cloud

developerDeployment modes — stories about deployment modes in this arenaDeployment modes2partialfree5/10C

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 integrations2partial4/10X

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

ai-native userPrivacy posture — data-handling and privacy storiesPrivacy posture2partial3/10C

Perform bulk operations across many items at once G

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

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 lifecycle2partial3/10C

Control data retention and deletion G

ai-native userPrivacy posture — data-handling and privacy storiesPrivacy posture2none0/10

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

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

ai-native userOpenness — open source, data portability, and self-hosting storiesOpenness2noneuntestednone yet

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 latency2noneuntestednone yet

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 scale2noneuntestednone yet

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

Filtering

developerFiltering metadata — stories about filtering metadata in this arenaFiltering metadata2noneuntestednone yet

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

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 latency2noneuntestednone yet

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 latency2noneuntestednone 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 plans1partialfree5/10C

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 38 stories with headroom

What would move Weaviate’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 productPlug MCP servers into this product so it can use their tools

    nonemoves agent-readyimpact 45

    Evidence only shows Weaviate exposing itself as an MCP server (so external LLMs/IDE assistants can call Weaviate's own tools), not Weaviate acting as an MCP client that plugs in and uses external MCP servers' tools.

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

    nonemoves PA Scoreimpact 30

    Weaviate is a vector database with search, RAG, multi-tenancy, backup and agent-integration features, but there is no evidence of an event-driven rules/triggers system that automatically fires actions based on defined conditions or data events.

  3. Agenticness — how well agents can access and operate the productSet up automations that run autonomously in the background

    nonemoves Built-in AIimpact 30

    Missing: no scheduling/cron mechanism, no event-driven triggers, no documented background automation workflows.

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

    nonemoves agent-readyimpact 30

    Evidence lists official client libraries (Python, JS/TS, Go, Java) and an MCP server, but no mention anywhere of an official CLI tool for interacting with or managing Weaviate.

  5. Agenticness — how well agents can access and operate the productIssue scoped/least-privilege API credentials for an agent

    nonemoves agent-readyimpact 30

    Missing: any mention of API key scoping, role-based permission grants, or credential minting workflow for agents.

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

    nonemoves agent-readyimpact 30

    No evidence in the pack mentions webhooks or event subscription mechanisms; Weaviate's documented features cover search, RAG, multi-tenancy, replication, backups, and MCP integration but nothing about webhook-based event subscriptions.

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

    nonemoves API qualityimpact 30

    Missing: interactive API explorer/playground, runnable code snippets embedded in docs, evidence of live query execution from documentation.

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

    nonemoves API qualityimpact 30

    The evidence pack contains no mention of an OpenAPI spec, Swagger docs, or any machine-readable API specification being available for download; it only covers client libraries, MCP server, and quickstart guides.

Showing the top 8 of 38 — 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 map6 surfaces · 32 covered stories

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

GitHub README23 stories

Weaviate docs22 stories

docs.weaviate.io9 stories

Hacker News7 stories

llms.txt5 stories

Pricing docs3 stories

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-weaviate-probe -p 18080:8080 -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true cr.weaviate.io/semitechnologies/weaviate:latest && curl -X POST localhost:18080/v1/schema -d '{"class":"PaProbe","vectorizer":"none"}' && curl localhost:18080/v1/schemareproduced
$ docker run -d --name pa-weaviate-probe -p 18080:8080 -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true cr.weaviate.io/semitechnologies/weaviate:latest && curl -X POST localhost:18080/v1/schema -d '{"class":"PaProbe","vectorizer":"none"}' && curl localhost:18080/v1/schema
cf5ff864f4ddf48c20192975612d4dee698e5d3fca2b0aebc9b2f79f9132f70c
{"grpcMaxMessageSize":104858000,"hostname":"http://[::]:8080","modules":{"generative-anthropic":{"documentationHref":"https://docs.anthropic.com/en/api/getting-started","name":"Generative Search - Anthropic"},"generative-anyscale":{"documen
{"class":"PaProbe","invertedIndexConfig":{"bm25":{"b":0.75,"k1":1.2},"cleanupIntervalSeconds":60,"stopwords":{"additions":null,"preset":"en","removals":null},"usingBlockMaxWAND":true},"multiTenancyConfig":{"autoTenantActivation":false,"auto
{"classes":[{"class":"PaProbe","invertedIndexConfig":{"bm25":{"b":0.75,"k1":1.2},"cleanupIntervalSeconds":60,"stopwords":{"additions":null,"preset":"en","removals":null},"usingBlockMaxWAND":true},"multiTenancyConfig":{"autoTenantActivation"
proves: Self-host the core productproves: Drive the product through a documented public APIrecorded 2026-09-04

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

5 of 15 testable claims verified · 0 contradicted → integrity 33/100

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

5

Verified

10

Unverified

0

Contradicted

17

Undersold

Verified (6)
Unverified (17)
Undersold (17)
Claims outside our story set (2)

Real capability claims found in Weaviate’s own materials, but no story in this arena’s taxonomy covers them yet — that’s feedback on the taxonomy, not a mark against the product.

  • “Can act as the retrieval backend for RAG pipelines, feeding context to generative models”

    source ↗

  • “Agents can use semantic insights from stored data to make decisions or trigger actions”

    source ↗
Suggest a story for these →

Pricing signals

  • $0.00465per 1M vector dimensions/monthpay-as-you-goFlex (shared) plan vector-dimension rate; Premium shared $0.003875/1M and Premium dedicated $0.002718/1M also listedsource ↗as of 2026-09-07
  • $45per month (entry plan)entry planFlex plan minimum monthly pay-as-you-go pricesource ↗as of 2026-09-07
  • freeper 1M vector dimensions/monthfree tierFree tier: 100,000 objects, 1GB memory, 10GB disk, always free, no vector-dimension chargesource ↗as of 2026-09-07

Extracted verbatim from the vendor’s own pricing page — hover a figure for the exact quote.

Business model

open-sourcefree-tierusage-basedenterprise-custom

BSD-3 open-source vector database, self-hostable for free; Weaviate Cloud adds a free sandbox, usage-based serverless plans priced per stored vector dimensions, and enterprise contracts.

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 Score30 (Sep 4 '26)29 (Sep 15 '26)
Agent-ready43 (Sep 4 '26)43 (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)