Weaviate
by Weaviate · Open-source vector database for search, RAG and agents
BenchRank score
Screenshots of Weaviate
Homepage
Overview
Weaviate is an open-source vector database that stores, indexes and searches high-dimensional vectors, supporting pure vector, semantic and hybrid (vector plus keyword) queries through Python, Go, TypeScript and JavaScript SDKs or GraphQL and REST APIs. It adds built-in embedding models, a natural-language Query Agent and Engram, and runs self-hosted or on Weaviate Cloud.
- Best for
- Teams building RAG, search and agent features who want a managed or self-hosted vector database
- Pricing
- Free tier at $0/month, then Flex pay-as-you-go from $45/month and Premium from $400/month on a prepaid contract, with usage-based charges for vector dimensions, storage, backups and AI services.
- Runs on
- Self-hosted
Strengths and trade-offs
Strengths
- Open source and self-hostable; 20M+ downloads claimed
- Hybrid search combines vector and keyword in one query
- Built-in embeddings, so no external embedding pipeline
- SDKs for Python, Go, TypeScript and JS, plus GraphQL/REST
Trade-offs
- Free tier capped at 100,000 objects, 1 collection, 3 tenants
- No HA, replication or backups on the free tier
- SSO/SAML requires Premium, from $400/month
- Vector-dimension rates vary by region and index type; costs hard to forecast
How Weaviate markets itself
A structured read of the promise, proof and page design on Weaviate’s captured homepage.
Homepage capture
“Design, build and ship complete AI experiences”
- Angle: AI-native
- Hero: 3D render
Pricing
Published plans and prices from Weaviate’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Pricing transparency
Whether real prices are published, self-serve signup exists, and usage costs are knowable without a sales call.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The Weaviate pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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