Weaviate
Weaviate: A flexible vector database for managed or self-hosted RAG search. Ranked #9 of 27 in Vector Databases by our editors (8.5/10); pricing: Free plan; best for teams needing managed or self-hosted RAG search.
At a glance
- Editor score8.5 / 10
- PricingFree plan
- Best forTeams needing managed or self-hosted RAG search
- Free planYes
- Paid from$45/mo
- DeploymentBoth
- Founded2019
- Facts checked22 Sep 2026
Where it wins
- Combines vector, BM25/BM25F keyword, and hybrid search
- Supports filtering, multi-tenancy, compression, and replication
- Offers managed cloud and self-hosted deployment options
Where it doesn't
- The Free plan is limited to 100,000 objects and 1 GB memory
- Flex starts at $45 monthly, with usage-based charges potentially applying
- Premium requires a prepaid contract starting at $400 per month
Our verdict on Weaviate
Weaviate is an open-source vector database for developers and teams building AI applications. It stores data objects and vector embeddings, then supports vector similarity search, BM25/BM25F keyword search, and hybrid search that combines both approaches. Teams can use it through managed cloud or self-hosted deployments, including Docker and Kubernetes environments. Its fit is strongest for retrieval-augmented generation workflows, searchable AI applications, and projects that need control over deployment and data organization.
The pricing structure covers both experimentation and higher service levels. The Free plan is always free and includes one cluster per user, 100,000 objects, 1 GB of memory, 10 GB of disk, one collection, and up to three tenants. Flex starts at $45 per month with pay-as-you-go billing, no commitment, a shared cloud cluster, unlimited objects, replication, and role-based access control. Usage-based charges may apply. Premium starts at $400 per month with a prepaid contract, shared or dedicated deployment, unlimited objects, up to 99.95% uptime, and enterprise support.
Weaviate’s technical breadth is a key consideration. Metadata and scalar filtering support more targeted retrieval, while HNSW, flat, dynamic, and HFresh indexes provide multiple indexing choices. Vector compression, multi-tenancy, replication, and high availability address larger or more structured deployments. Official client libraries support Python, TypeScript/JavaScript, Go, Java, and C#. Choose Weaviate when hybrid retrieval, deployment flexibility, or RAG workflows matter. Teams seeking a narrowly focused hosted database with simpler pricing may prefer a less configurable alternative.
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Weaviate pricing
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Weaviate fact sheet
| Free plan | Yes |
|---|---|
| Paid from | $45/mo |
| Deployment | Both |
| Hybrid search | Yes |
| Metadata filtering | Yes |
| Sparse vectors | Yes |
| Maximum dimensions | Not verified |
| Index types | HNSW, Flat, Dynamic, HFresh |
| SDK languages | Python, TypeScript/JavaScript, Go, Java, C# |
| Deployment | Cloud, Self-hosted |
| Platforms | Web |
| Compliance & security | SOC 2, HIPAA, SSO/SAML |
| Support | Email, Phone, Community, Docs · 24/7 |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Pricing | Free plan (source) |
| Website | weaviate.io |
| Facts checked | 22 Sep 2026 |
Alternatives to Weaviate
- ElasticsearchA broad hybrid-search platform for teams adding vectors to mature analytics stacks.9.3
- Vertex AI Vector SearchA managed Google Cloud option for teams building dense, sparse, and hybrid search.9.2
- VikingDBA cloud vector database with dense, sparse, hybrid, filtered, and multimodal retrieval.9.1
See all Weaviate alternatives →
Also Ranked In
Weaviate is ranked in 6 of our directories.
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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