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SPECIFICTION
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EmergentDB vs Vearch

EmergentDB

emergentdb.com

Vearch

vearch.github.io

Side by side

FigureEmergentDBVearch
Price from$2/moFree
Free planYesYes
Free trial——
Open source——
WebYes—
macOS—Yes
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published51
Spec score · Vector Databases8.4 (#5)7.8 (#16)
ProductEmergentDB is an open-source, self-hosted vector database licensed under AGPL-3.0.Vearch is a cloud-native distributed vector database for similarity search of embedding vectors in AI applications.
SupportThe Community plan includes community support.The project directs users to GitHub Issues, a public email discussion list, and Slack for community support.

Plans

EmergentDB

  • CommunityFree
  • Launch$24 a year
  • Launch$29 a month
  • Scale$66 a year
  • Scale$79 a month

EmergentDB pricing →

Vearch

  • VearchFree

Vearch pricing →

In their own words

EmergentDB

EmergentDB is an open-source, self-hosted vector database under the AGPL-3.0 license, also available as managed cloud. It automatically selects a search strategy for a dataset, without requiring manual tuning. Vector insertion and search are available through REST API endpoints; vectors can also be inserted with the TypeScript SDK, using embeddings from any source. Listed index types are Flat, HNSW, IVF, PQ, and Emergent, and SDK languages include TypeScript and Python. The free Community plan includes unlimited vectors, queries, and writes, plus community support and the Bolt-lite engine. Managed Launch is listed at $24 per year, billed annually, with a 500K-vector limit; Scale is $66 per year, billed annually, with a 2.5M-vector limit and dedicated instance. The site lists ISO 27001, SOC 2 Type II, GDPR, HIPAA-ready status, and US, EU, and Asia-Pacific data residency. It also reports a crash-safe write-ahead log and performance figures for specified workloads.

Visit emergentdb.com

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.

Visit vearch.github.io