Weaviate

Open source vector database with hybrid search, modular retrieval and managed cloud options for production RAG and semantic apps at any scale.

vector-db rag semantic-search

data

What is Weaviate?

Discover how Weaviate can enhance your workflow

Weaviate stores embeddings with schema aware classes and provides robust search operators for semantic, hybrid and filtered queries. Developers build retrieval augmented generation backends with tight control over recall and latency, while the modular stack supports pluggable text and multimodal embeddings. The managed Weaviate Cloud offers shared and dedicated clusters with simplified pricing and add ons like hosted embeddings and a query agent that translates natural language to database operations. Enterprises deploy with HA, backups and role controls, while open source users self host for full flexibility. Clear SDKs in Python, TypeScript and Go and a GraphQL like API make integration straightforward across services and frameworks.

Key Capabilities

What makes Weaviate powerful

Schema and Vectors

Define classes properties and metadata then index embeddings to enable filtered semantic retrieval.

Implementation Level Professional

Hybrid and Filters

Combine vector similarity with BM25 and metadata filters for precise low latency results.

Implementation Level Professional

Managed Cloud

Use shared HA clusters with backups metrics and add ons like hosted embeddings and query agent.

Implementation Level Intermediate

SDKs and API

Adopt Python TypeScript and Go SDKs or call the HTTP API directly for any stack.

Implementation Level Intermediate

Key Features

What makes Weaviate stand out

Schema aware vector store with filters hybrid BM25 and metadata
Managed cloud with shared clusters and HA plus backups
Hosted embeddings add on for simple end to end setup
Query Agent to convert natural language into operations
SDKs for Python TypeScript Go and a clean HTTP API
Sharding replication and snapshots for resilience at scale
Multimodal vectors for text image and custom embeddings
Observability and usage metering in cloud console

Use Cases

How Weaviate can help you

Power RAG backends that mix semantic and keyword filters
Search product catalogs with facets and relevance controls
Index documents and images for unified multimodal retrieval
Prototype quickly in OSS then migrate to managed cloud
Serve low latency queries for chat memory or agents
Automate backups and snapshots for compliance
Use cloud hosted embeddings to skip model hosting
Apply fine grained filters for policy aware responses

Perfect For

ML engineers platform teams data engineers and startups that need reliable vector search with OSS flexibility and managed cloud simplicity

Quick Information

Category data

Pricing Model Free trial / credits

Last Updated 3/19/2026

Compare Weaviate with Alternatives

See how Weaviate stacks up against similar tools

Weaviate VS Akkio Weaviate VS Algolia Weaviate VS Alteryx

Frequently Asked Questions

How does pricing start?

Open source is free, Weaviate Cloud lists entry shared clusters with HA around $45 per month plus usage based add ons.

Can I bring my own embeddings?

Yes, you can store any vectors and also use hosted embeddings as an add on.

Is it suitable for production RAG?

Yes, teams deploy Weaviate for low latency hybrid retrieval with filters and backups.

Does it support multimodal data?

Text and image vectors are supported with flexible schema for custom types.

Can I self host?

Yes, run OSS on your own infra and migrate to cloud later.

Is there a query builder?

A Query Agent add on translates natural language to database operations to speed prototyping.

What SDKs are available?

Python, TypeScript and Go SDKs are maintained along with a GraphQL like API.

How are backups handled?

Cloud includes snapshot and backup features with console controls and APIs.

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