Use Cases
Graph databases win wherever the connections matter more than the rows — the many-hop questions a relational join chokes on. The catch has always been the bill: classic pointer-and-index engines keep the whole graph resident in memory to stay fast, so you size machines to your data, tune the heap, and rent reserved RAM by the hour. Tetra keeps the Cypher and the traversal power, and drops the RAM bill, the cluster, and the can’t-embed-it wall.
AI · Retrieval
“We want our LLM app to reason over a knowledge graph — but standing up and paying for a whole graph cluster next to the vector store is overkill, and we can’t ship a heavyweight server inside an agent.”
One small embeddable engine with full Cypher means the knowledge graph lives inside the retrieval service itself — no cluster, and cheap enough to spin up per tenant.
For AI platform & applied-AI teams building RAG and agents
Where Tetra fits
“We want our LLM app to reason over a knowledge graph — but standing up and paying for a whole graph cluster next to the vector store is overkill, and we can’t ship a heavyweight server inside an agent.”
One small embeddable engine with full Cypher means the knowledge graph lives inside the retrieval service itself — no cluster, and cheap enough to spin up per tenant.
For AI platform & applied-AI teams building RAG and agents
“Catching mule networks and synthetic identities in real time means holding a huge transaction graph in memory around the clock — the RAM bill and the OOM firefighting scale faster than the fraud team.”
Deep traversal without keeping the whole graph resident in expensive RAM, so real-time scoring stops being a memory-budget negotiation.
For fraud & risk engineering at banks, lenders, payments
“Our sites and devices go offline and can’t call home, but the connected data still has to be queried locally — a managed cloud graph or a multi-node cluster is a non-starter on a box at the edge.”
A single-file binary runs embedded on-device, on-prem, or fully air-gapped. Offline-first graph queries — nothing to reach, nothing to operate.
For IoT & industrial, defense/gov, telco & edge, desktop apps
“Who-can-reach-what is inherently a graph problem, but our security data is sensitive and often on-prem — a hosted graph service doesn’t fit our threat model or our ops capacity.”
Embeddable and self-contained: the access graph ships inside the security tool and runs in the customer’s own environment, with no cluster to babysit.
For security engineering, Zero-Trust & IAM, CSPM/ITDR vendors
“Our service-dependency and observability topology is a graph, but keeping it in a memory-resident cluster to answer blast-radius and root-cause queries is a line item I can’t keep justifying as it grows.”
A tiny footprint and far less memory to hold the same topology — so impact-analysis queries stay economical as the estate grows, and the engine can sit next to the collector.
For platform/SRE, observability, ITOps & network teams
“The embedded, Cypher-speaking engine we built on just got abandoned. We can’t sit on an unmaintained dependency — but re-architecting around a new database, or betting on a volunteer fork, is the last thing our roadmap needs.”
Tetra is a maintained, single-binary, Cypher-compatible engine — a like-for-like home. Repoint the driver and import; your queries come along, so it’s a migration, not a rebuild.
For product engineers on a discontinued embedded graph
Recognize your problem above? We’ll walk you through Tetra on a graph shaped like yours — or just get on the list and we’ll keep you posted as we open up.
