Weaviate
Weaviate is a cloud and self-hosted vector database offering hybrid dense+sparse search, strong metadata filtering, and a modular storage layer. It is designed for enterprise and production RAG workloads that require flexibility and scalable cloud hosting. If you want to compare the best vector databases for your data, try Agentset.
Vector Databases Are Just One Piece of RAG
Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no vector database to operate.
Trusted by teams building production RAG applications
Deployment
Self-Hosted, Managed Cloud
Cost
Free (self-host), cloud starts ~$25/mo
Index Types
HNSW, Hybrid dense+sparse
Deployment
Infrastructure Options
Deployment Types
- Self-Hosted
- Managed Cloud
Cloud Providers
- AWS
- GCP
Strengths
What Weaviate Does Well
- Excellent hybrid dense+sparse search built-in
- Strong schema and data modeling capabilities
- Modular storage layer (pluggable backends)
- Built-in vectorization modules (transformers, OpenAI, etc.)
- Strong multi-tenancy support
- OIDC authentication support for SSO integration
- Excellent documentation and community
- Good balance of features and performance
Weaknesses
Potential Drawbacks
- GraphQL API can be complex for simple use cases
- Limited to AWS and GCP for managed cloud
- Can be resource-intensive for small deployments
- Steeper learning curve than simpler alternatives
- Schema management adds complexity
- Less cost-effective than pure open-source options
Use Cases
When to Choose Weaviate
Ideal For
- Enterprise applications needing hybrid search
- Projects requiring strong metadata modeling
- Teams wanting built-in vectorization
- Applications with complex filtering needs
- Multi-tenant SaaS platforms
- Organizations needing OIDC/SSO integration
Not Ideal For
- Simple prototypes needing minimal setup
- Very cost-sensitive projects
- Teams needing Azure deployment
- Applications wanting API simplicity over features
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with fully managed vector storage and retrieval. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Compare Databases
See how it stacks up
Compare Weaviate with other vector databases to understand the differences in deployment options, cost, and features.
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