Qdrant vs Vearch
Qdrant
qdrant.techVearch
vearch.github.ioSide by side
| Figure | Qdrant | Vearch |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 5 | 1 |
| Spec score · Vector Databases | 8.0 (#9) | 7.8 (#16) |
| Deployment | Qdrant can be run locally using its Docker image or used through Qdrant Cloud. | Documented deployment options include Kubernetes with Helm, Docker Compose, Docker, and source compilation. |
| Integrations | Qdrant lists integrations including LangChain, LlamaIndex, Airbyte, Unstructured, DocArray, and AutoGen. | The project lists integrations with LangChain, LlamaIndex, LangChainGo, and LangChain4j. |
| Product | Qdrant is a vector similarity search engine with an API to store, search, and manage vectors with additional payload data. | Vearch is a cloud-native distributed vector database for similarity search of embedding vectors in AI applications. |
| Support | The pricing page lists community support as Discord-only and paid support options with 10h/day business-hours or 24/7 coverage. | The project directs users to GitHub Issues, a public email discussion list, and Slack for community support. |
Plans
Qdrant
- Free TierFree
- Hybrid CloudPriced on request
- Premium TierPriced on request
- Private CloudPriced on request
- Standard TierPriced on request
In their own words
Qdrant
Qdrant is a vector similarity search engine and API for storing, searching, and managing vectors with payload data. A point can pair a vector with JSON metadata that filters search results. It supports dense, sparse, and multivector configurations, plus hybrid retrieval that combines semantic and lexical search. These capabilities can serve semantic search and recommendation systems. Qdrant can run locally with its Docker image or through Qdrant Cloud. REST and gRPC APIs are available, along with official client libraries for Python, JavaScript/TypeScript, Rust, Go, .NET, and Java. Listed integrations include LangChain, LlamaIndex, Airbyte, Unstructured, DocArray, and AutoGen. The free cloud tier is intended for testing and prototypes, with a single-node cluster, 1GB RAM, and 4GB disk. Qdrant Cloud enables TLS and encryption at rest; self-hosted users must configure these controls, and authentication and encryption are off by default in self-hosted open-source deployments. Qdrant was founded in 2021 and is headquartered in Berlin, Germany.
Vearch
Vearch is a free, open-source, cloud-native distributed vector database for similarity search in AI applications. It searches embedding vectors and can combine vector queries with scalar filtering for metadata conditions. The project describes replication and elastic scale-out and says it can retrieve vectors among millions of objects in milliseconds. Its Router exposes REST operations for upserting, deleting, searching, and querying data. Client SDKs are listed for Python, Go, Java, and Rust, with integrations for LangChain, LlamaIndex, LangChainGo, and LangChain4j. Deployment options include Kubernetes through Helm, Docker Compose in standalone or cluster mode, Docker, and compilation from source. PartitionServer uses Raft-based replication for document partitions, while Gamma is a Faiss-based vector search engine. Vearch is licensed under Apache License 2.0 and supports Linux and macOS self-hosted deployments. Community support is available through GitHub Issues, an email discussion group, and Slack. The VisualSearch demo requires an image retrieval plugin for object detection and feature extraction.