Vearch
6.4#12 in vector databasesFree planFree planAPILinuxmacOSSelf-hosted
VisitVearch vs Vespa
What both publish
| Fact | Vearch | Vespa |
|---|---|---|
| API | The Router exposes REST endpoints for upsert, delete, search, and query operations. | Applications can write data through HTTP/2, the Document API, or client tools, and can query through the Query API or custom request handlers. |
| Deployment | Documented deployment options include Kubernetes with Helm, Docker Compose, Docker, and source compilation. | Vespa can be self-managed, run with the Vespa Kubernetes Operator, or deployed on Vespa Cloud. |
| Integrations | The project lists integrations with LangChain, LlamaIndex, LangChainGo, and LangChain4j. | Vespa Cloud Enclave can run applications in customer accounts on AWS, Azure, or Google Cloud Platform. |
| Notable limit | The Vearch 3.5 database guide says a database cannot be deleted while it contains a space. | All Vespa Cloud plans have a quota, and the Startup plan is limited to development zones. |
| Product | Vearch is a cloud-native distributed vector database for similarity search of embedding vectors in AI applications. | Vespa is an AI search platform for online data-driven applications that combines retrieval, ranking, machine-learning inference, and real-time serving. |
| Support | The project directs users to GitHub Issues, a public email discussion list, and Slack for community support. | The Startup plan includes community support only with no SLA; Cloud plans list unlimited support cases, with response times varying by plan. |
Vearch plans
- VearchFree
Vespa plans
- Startup—
- Basic—
- Commercial—
- Enterprise—
- Enterprise *$0.18 a month
- Self ManagedPriced on request
Pick Vearch if…
Vearch suits teams building AI applications that need vector similarity search with metadata filtering. It is also relevant to teams that want to choose among documented deployment methods and use supported client SDKs or framework integrations.
Pick Vespa if…
Vespa suits teams building AI applications that depend on search quality, ranking, fresh real-time data, or increasing retrieval traffic. It offers local, self-managed, and cloud deployment options.