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Weaviate
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Databases & SQL (33)

Weaviate Verified Tool

Store vectors with fast search.

Last Update: August 18, 2026

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Starting price Free + from $25/mo

Tool Information

Weaviate is an open-source vector database that allows users to store data objects and vector embeddings from ML-models and scale to billions of data objects seamlessly. The tool provides lightning-fast pure vector similarity search over data objects or raw vectors and supports a combination of keyword-based search and vector search techniques for state-of-the-art search results. Weaviate also enables users to use any generative model in combination with your data to create next-gen search experiences.

Starting price: Free + from $25/mo. Features, limits, credits, seats, billing periods, taxes, regional availability, and promotions can change, so confirm the current official checkout or sales quote.

Vector search quality depends on schema embeddings and data; test recall security tenancy filters backups and costs.

F.A.Q (14)

Weaviate is an open-source vector database for semantic search retrieval and AI applications.

Yes. Self-hosted open-source use is available, while managed cloud plans start from about $25 per month.

Vector search finds items by numerical semantic similarity rather than exact keyword matching alone.

Yes. It can combine vector similarity and keyword-based search in supported configurations.

Yes. Technical teams can deploy the open-source database, with infrastructure security upgrades and backups under their control.

It is the managed service option for teams that prefer hosted deployment and operational support.

Weaviate supports integrations and user-supplied vectors; model availability and costs depend on configuration.

Yes. It is commonly used as a retrieval layer, but retrieval evaluation citations prompt design and access control still matter.

Managed pricing depends on current resources and usage; model APIs and infrastructure may add separate costs.

Multi-tenant patterns are supported, but isolation filters authorization and operational limits require testing.

Use representative queries and measure recall precision latency freshness and permission correctness.

Plan authentication authorization encryption backups monitoring capacity upgrades and disaster recovery.

Developers and data teams building semantic search recommendation or retrieval-augmented generation systems.

Yes. Weaviate operates an official partner program for eligible technology providers systems integrators managed service providers and consultancies. Program availability, eligibility, rewards, attribution, and payment terms are subject to the current official program agreement.

Pros and Cons

Pros

  • Stores vector embeddings
  • Scales to billions objects
  • Lightning-fast vector similarity search
  • Supports keyword-based search
  • Supports vector search
  • Allows any generative model
  • Wide neural search integrations
  • Supports vectorization
  • Zero to production design
  • Community and open-source focus
  • Backup and restore capabilities
  • Variety of learning resources
  • Free to use
  • Well-integrated with embedding providers
  • Simultaneous keyword and vector search
  • Provides state-of-the-art search experiences
  • Efficient Q&A over dataset
  • Supports innovative applications development
  • Seamless vector indexing
  • Fast pure vector search
  • Extensive module support
  • User-friendly developer experience
  • Open-source with Slack community
  • Provides SaaS services
  • Good for data-intensive applications
  • Community inspirations for usage

Cons

  • Limited integrations
  • No commercial support
  • Open-source drawbacks
  • Requires ML model building
  • Learning curve
  • Limited search options
  • Inadequate community support
  • Insufficient documentation

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Overview

Pricing
Freemium

Applicable Tasks

vector database LLM machine learning
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