Practical overview
When it fits
Use Weaviate when an application needs a database layer for vector-based retrieval, hybrid search, RAG or agent memory, and when cloud or self-hosted infrastructure suits the deployment.
Who it is for
- Developers building semantic search services over embedded documents.
- Teams adding a retrieval layer to RAG applications.
- Builders combining keyword and vector retrieval or partitioning a knowledge base by tenant.
- Developers storing and retrieving vector-based agent memory.
Documented uses
- Search stored objects by vector similarity, keywords or a hybrid of both.
- Retrieve relevant knowledge for a RAG application.
- Filter search results and partition a multi-tenant knowledge base.
- Store and retrieve agent memory using vectors.
Requirements & limits
Weaviate is open source and supports cloud and self-hosted deployment. Its pricing page describes an always-free managed database option alongside plans for production teams.
- Choose between cloud and self-hosted deployment.
- Vector, keyword and hybrid search are supported, along with filtering.
- Multi-tenancy supports partitioning knowledge bases.
- The managed database has an always-free option; production plans are also described, but plan limits and rates are not specified here.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Choose a Weaviate deployment option
Select a documented deployment path for evaluation, development, or production.
Before you start
- Decide whether the use case is evaluation, development, or production.
- Choose among Weaviate Cloud, Docker, Kubernetes, or Embedded Weaviate.
Steps
Use Weaviate Cloud for managed deployment, from evaluation through production.
Use Docker for local evaluation and development.
Use Kubernetes for development through production, with self-deployment or Marketplace deployment available.
Use Embedded Weaviate for basic, quick evaluation from Python or JavaScript/TypeScript.
Expected outcome
A deployment option is selected that matches the documented use case and deployment stage.
Guide limitations
- The documentation identifies deployment options but does not provide the detailed setup steps for them on this page.
- Embedded Weaviate is described for basic, quick evaluation, not as a production deployment option.
Compare options for Vector search
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Weaviate | Search stored objects by vector similarity, keywords or a hybrid of both. | Cloud, Self-hosted | Weaviate is open source and supports cloud and self-hosted deployment. Its pricing page describes an always-free managed database option alongside plans for production teams. |
| Mettis | Index text documents through an HTTP endpoint. | Linux, Self-hosted, Server |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 7 October 2026
- Original website title
- The AI database developers love | Weaviate · Official homepage title · retained in its source language