Made for developers who see the value of data
Git-for-data features such as version control, authentication, and pull requests, all in the data layer. Our products combine the best of graph databases and document stores to help developers make data the star of their builds.
Products
TerminusDB
Open-source document graph database
A multi-model and distributed open-source graph database and document store. At its heart is a collaboration model providing Git-for-data features such as branch, diff, merge, workflows, and revision control.
TerminusDB
Open-source document graph database
A multi-model and distributed open-source graph database and document store. At its heart is a collaboration model providing Git-for-data features such as branch, diff, merge, workflows, and revision control.TerminusCMS
Semantic Headless Architecture with AI Search
A cloud headless CMS for data and complex documentation. Built on TerminusDB, developers can semantically manage a headless architecture with all the database collaboration features and the addition of AI-powered semantic search, resolution and clustering.TerminusCMS
Semantic Headless Architecture with AI Search
A cloud headless CMS for data and complex documentation. Built on TerminusDB, developers can semantically manage a headless architecture with all the database collaboration features and the addition of AI-powered semantic search, resolution and clustering.
VectorLink
VectorLink is a vector database that is included in TerminusCMS. It provides versioned indexing of your data and content. Using advanced AI techniques and OpenAI, VectorLink provides semantic search, entity resolution, and clustering to bring more power to the applications built with TerminusCMS.
The Building Blocks of TerminusDB
TerminusDB and TerminusCMS are built to provide developers with a suite of tools to solve complex problems and develop innovative applications and services. The key features that enable collaborative, relationship-aware, and data-focused applications include –
Git-Like Collaboration
Branch, clone, merge, just like in Git, but with data. Work in parallel branches, see complete audit logs and document histories. Git-like collaboration for data and content.
Scale Big, Store Small
We have the lowest memory overhead around. We use succinct data structures and delta encoding to keep things small, just 13.57 bytes per triple. We can scale to any size, with the only limitation being the memory available.
AI Best Practice
Graph relationships, metadata, and version control, coupled with VectorLink, the TerminusDB semantic indexer, provide the backbone to use AI with your data and in your applications.
Pull Request Workflows
Pull request workflows are built into the data layer to keep a human in the loop to ensure changes are correct.
Graph Data
JSON documents are connected in a knowledge graph providing relationship-focused data products with greater query power.
Portable Data
Data and content is stored as human and machine readable JSON documents - the structured format for the web.
Flexible Schema Design
The schema is based on a simple JSON syntax. Build it with a UI or in code. Schema migration tools help to evolve your applications over time.
API First
Connect with REST and GraphQL APIs and with Python and JavaScript clients.
Distributed Design
Work locally and offline and push changes to the cloud using the clients and command line interface.
Who We Are
TerminusDB is home to some of the most off-beat, hardworking, and creative engineers in the world. We cherish the oddballs, we are the oddballs. We are also our community which continues to help evolve and improve our products with their brilliance.
Got questions? Want to say hi? You can reach us at –
Latest from the Blog
TerminusDB vs Neo4j – Graph Database Performance Benchmark
Graph database performance benchmark: TerminusDB vs Neo4j using WDBench benchmark using Wikidata a real world queries.
Smaller Is Better: Ultra-Compact Graph Representations for a Big Graph
A technical blog about how we loaded 17 billion triples into TerminusDB to deliver a queryable big graph with a low memory overhead.
Building a Vector Database to Make Use of Vector Embeddings
Vector databases are all the rage at the moment and it’s not just hype. The advance of AI, which is making use of vector embeddings, has significantly increased the buzz. This article talks about how we implemented a vector database in Rust in a week to give us semantic indexing and entity resolution using OpenAI to define our embeddings.
Sign up to TerminusCMS
Get started in minutes and for free with the TerminusCMS Community Package. Clone an example from the dashboard to learn how TerminusCMS works as a semantic headless architecture.