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Start here for the decision-making essentials: what SemaDB does, who it is for, how it is accessed, and the first-party sources behind this profile.
SemaDB Cloud Beta
SemaDB is an open-source vector database offering vector, hybrid, multi-vector, and quantized search via a JSON or MessagePack REST API. It supports flexible deployment (standalone, container, cloud, Kubernetes), Prometheus monitoring, and uses a graph-based Vamana algorithm for efficient similarity search.
Read-only public captures of SemaDB’s homepage. Screenshots are dated, never live embeds, and open full-screen.
Short answers to the questions buyers and builders commonly ask about SemaDB. Each answer cites the shared ledger below, where every source is listed once.
Multi-index, multi-vector, document-based vector database and search engine · Graph-based Vamana (DiskANN) approximate nearest neighbour vector search · Hybrid, multi-index search combining vector, keyword/text, geo, and filter queries in a si · Cluster mode with automatic sharding across multiple servers and multi-tenancy with per-us
SemaDB is a multi-index, multi-vector, document-based vector database / search engine.
Semantic Search · Image Similarity Search · Recommendation Engines · Fraud Detection
Semantic Search: Search engines that understand the meaning and nuances of your query, not just keywords.
Cold disk starts can be very slow (1–10+ seconds) due to random disk reads during graph tr · Bulk insert capped at 10000 points per request; bulk update and bulk delete capped at 100 · Search requests return between 1 and 100 points, default 10 · The vectorFlat index is not recommended for collections larger than 10k points due to slow
Cold disk starts can be really slow.
SemaDB Cloud Beta | Free | free tier for cloud beta users
You can try SemaDB Cloud Beta for free without having to install anything and follow this guide.
JSON (and MessagePack) RESTful HTTP API · RESTful HTTP API for managing collections and points (insert POST, search POST, update PUT · SemaDB REST API version 2.0.0 · JSON or MessagePack REST API with no custom client library required
It is designed to offer a clear and easy-to-use JSON RESTful API.
Distributed via RapidAPI (semadb.p.rapidapi.com)
base_url = "https://semadb.p.rapidapi.com"
This profile connects the jobs SemaDB is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
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SemaDB
Multi-index, multi-vector, document-based vector database and search engine · JSON and MessagePack RESTful HTTP API (no custom client required) · Graph-based Vamana (DiskANN) approximate nearest neighbour vector search
Multi-index, multi-vector, document-based vector database and search engine
JSON (and MessagePack) RESTful HTTP API
Originated at Semafind from a knowledge-management project before becoming a standalone project
Graph-based Vamana (DiskANN) approximate nearest neighbour vector search
Hybrid, multi-index search combining vector, keyword/text, geo, and filter queries in a single request
Cluster mode with automatic sharding across multiple servers and multi-tenancy with per-user collections and plans
Open-source project
Deployable as standalone process, Docker/Podman container, Docker Compose cluster, or Kubernetes StatefulSet
Container image available at ghcr.io/semafind/semadb (tagged release versions)
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
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This profile is being checked for dated releases and material product changes. Nothing appears here until the exact date and event can be verified from a recorded source.
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These tools share a workflow, capability or audience with SemaDB. They may complement it rather than replace it, and are not presented as integrations or endorsements.
Written in Go; pre-built standalone binaries provided for linux/amd64
Cold disk starts can be very slow (1–10+ seconds) due to random disk reads during graph traversal; SSDs strongly recommended
Open-source vector database
Vector database / vector search engine
Semantic Search
Image Similarity Search
Recommendation Engines
Fraud Detection
RESTful HTTP API for managing collections and points (insert POST, search POST, update PUT, delete DELETE)
Self-hostable via a single YAML configuration file specified by the SEMADB_CONFIG environment variable, with configurable cluster nodes
SemaDB Cloud Beta offers live interactive examples without requiring a local instance
Vector database for AI applications
Vector search with two index types (Flat and Vamana) over points in a collection
Vamana index is the recommended vector index for most use cases
Hybrid search combining vector and text queries via _or/_and composite queries with weighted scores
Retrieval augmented generation, recommender systems, and similarity search
Similarity search, recommendation systems, and anomaly detection
SemaDB REST API version 2.0.0
Self-hostable with configurable point size limits via configuration file
Vamana vector index implements the greedy search algorithm from the DiskANN paper
Unstructured data including text, images, and audio stored as high-dimensional vectors
Bulk insert capped at 10000 points per request; bulk update and bulk delete capped at 100 points per request
Search requests return between 1 and 100 points, default 10
Hybrid search combining vector and text queries with weighted scores
JSON or MessagePack REST API with no custom client library required
SemaDB Cloud Beta available without installation
Self-hosted SemaDB instance supported alongside the cloud option
SemaDB Cloud Beta | Free | free tier for cloud beta users
