Weaviate

aid: weaviate
name: Weaviate
description: >-
  Weaviate is an open-source, AI-native vector database that enables developers to build semantic search and AI-powered
  applications. It stores data as vector embeddings alongside structured properties, enabling lightning-fast similarity
  search using HNSW or flat indexes. Weaviate supports multi-tenancy, automatic vectorization via configurable modules,
  GraphQL and REST APIs, and enterprise features including authentication, authorization, backups, and replication.
type: Index
position: Consumer
access: 3rd-Party
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
tags:
- Vector Database
- AI
- Machine Learning
- Semantic Search
- Open Source
- GraphQL
- Kubernetes
url: https://raw.githubusercontent.com/api-evangelist/weaviate/refs/heads/main/apis.yml
created: '2024-06-18'
modified: '2026-05-19'
specificationVersion: '0.19'
apis:
- aid: weaviate:weaviate-rest-api
  name: Weaviate REST API
  description: >-
    The Weaviate REST API provides full programmatic access to vector database operations including object CRUD,
    schema management, GraphQL vector search, multi-tenancy, backups, authentication, authorization, and cluster
    management.
  humanURL: https://weaviate.io/developers/weaviate/api/rest
  tags:
  - Vector Database
  - Objects
  - Schema
  - GraphQL
  - Search
  - AI
  properties:
  - url: openapi/weaviate-openapi.yml
    type: OpenAPI
  - url: https://weaviate.io/developers/weaviate/api/rest
    type: Documentation
  - url: https://weaviate.io/developers/weaviate/quickstart
    type: GettingStarted
  - url: graphql/weaviate-graphql.md
    type: GraphQL
common:
- type: LinkedIn
  url: https://www.linkedin.com/company/weaviate-io
- url: https://weaviate.io/developers/weaviate/api/rest
  type: Documentation
- url: https://github.com/weaviate/weaviate
  type: GitHubRepository
- url: https://github.com/weaviate
  type: GitHubOrganization
- url: https://weaviate.io/developers/weaviate/quickstart
  type: GettingStarted
- url: https://weaviate.io/developers/academy
  type: Learn
- url: https://weaviate.io/blog
  type: Blog
- url: https://weaviate.io/community
  type: Community
- url: https://forum.weaviate.io/
  type: Forum
- url: https://weaviate.io/slack
  type: Slack
- url: https://weaviate.io/pricing
  type: Pricing
- url: https://weaviate.io/podcast
  type: Podcast
- url: https://newsletter.weaviate.io/
  type: Newsletter
- url: https://weaviate.io/community/events
  type: Events
- url: https://github.com/weaviate/weaviate/blob/master/LICENSE
  type: TermsOfService
- url: https://weaviate.io/security
  type: Security
- url: https://github.com/weaviate/weaviate/blob/master/CHANGELOG.md
  type: ChangeLog
- url: https://github.com/weaviate/weaviate/issues
  type: Support
- type: SpectralRules
  url: rules/weaviate-spectral-rules.yml
- type: Vocabulary
  url: vocabulary/weaviate-vocabulary.yml
- type: Features
  data:
  - Free Trial 14 days then pay-as-you-go
  - 'Flex from $45/mo: $0.255/GiB storage, $0.0264/GiB backup'
  - 'Premium from $400/mo: $0.31875/GiB storage, $0.033/GiB backup'
  - Hybrid search (vector + BM25)
  - Dynamic index, compression, multi-tenancy
  - REST, GraphQL, and gRPC APIs
  - Throughput scales with cluster size
  - Batch import recommended at 100 objects/request
  - Built-in modules for OpenAI, Cohere, HuggingFace embeddings
  - Generative search modules (RAG-style)
  - Multi-tenancy with strict isolation
  - Bring Your Own Vectors (BYOV)
  - RBAC baseline security
  - 99.5% SLA Flex, up to 99.95% Premium
  - Available on AWS, GCP, Azure
  - Open-source self-hosted alternative
  sources:
  - https://weaviate.io/pricing
  updated: '2026-05-04'
- type: UseCases
  data:
  - name: Semantic Search
    description: Build semantic and hybrid search applications using vector similarity and BM25 keyword search combined.
  - name: RAG Applications
    description: >-
      Power Retrieval Augmented Generation (RAG) pipelines by storing and retrieving relevant context for large
      language model prompts.
  - name: Multi-Modal Search
    description: Search across text, images, and other modalities using unified vector representations.
  - name: AI-Powered Recommendations
    description: Build recommendation engines using object similarity search to find related items based on vector proximity.
- type: Integrations
  url: https://weaviate.io/partners
- name: MCP Server
  url: https://github.com/weaviate/mcp-server-weaviate
  type: MCPServer
- name: Agent Skills
  url: https://github.com/weaviate/agent-skills
  type: AgentSkill
- type: LLMsTxt
  url: https://weaviate.io/llms.txt
integrations:
- name: AWS
- name: Google
- name: Snowflake
- name: Databricks
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com