TerminusDB
Git-for-data document graph database with built-in version control
Founded
2019
Starting Price
Free
Category
Developer Tools
Last updated March 14, 2026
About TerminusDB
TerminusDB is an open-source, model-driven graph database and document store that brings Git-like collaboration workflows to structured data. It allows teams to link JSON documents in a semantic RDF knowledge graph with native support for branching, merging, cloning, time-travel, and diff/patch operations. Available as self-hosted open-source (Apache 2.0) and as a managed cloud service via DFRNT.com.
Pros & Cons
Pros
- Only major graph DB with Git-like branching, time-travel, and diff natively in the storage layer
- Truly open-source under Apache 2.0 with no feature lock behind paywall
- Multi-paradigm query support covering REST, GraphQL, WOQL, and SPARQL
- Actively maintained with v12.0.4 released February 2026
- Ideal for collaborative data workflows with data review and merge capabilities
Key Features
Git-for-Data Version Control
Every write creates a commit with full revision history, branching, merging, squashing, and rollback
Time-Travel Queries
Query any past state of the database using historical commits
Knowledge Graph + Document API
Store JSON documents linked in an RDF semantic graph with closed-world semantics
Multi-Query Interface
Supports REST API, GraphQL, WOQL, SPARQL, and RDF query languages
In-Memory Succinct Data Structures
High-performance auto-indexing using delta encoding for efficient storage and fast reads
Schema Validation
Built-in data model validation with complex schema constraints enforced at write time
Distributed Push/Pull/Clone
Push, pull, and clone databases across nodes like distributed Git workflows
Pricing
Open Source
Free
- Full engine (Apache 2.0)
- Self-hosted
- All query interfaces
- Version control
- Community support
Best For
Collaborative Knowledge Graph Management
Research teams and enterprises building shared ontologies where contributors propose, review, and merge changes
Versioned Data Products
Data engineering teams building data mesh architectures with versioned, immutable data products
Scientific Data Management
Experimental data tracking where full lineage, reproducibility, and rollback are regulatory requirements
Structured Content Management
Managing relationship-heavy content like product catalogs and technical documentation with auditing and branching
