One engine under every kind of data.
SynergyDB isn't a bundle of separate databases behind one login. Relational rows, JSON documents, cached keys, graph edges, and vectors all live in a single transactional store — so the same data can be queried five ways without copying it between systems, and there's one thing to run, back up, and secure.
Relational, document, key-value, graph, and vector — one store
A single engine holds tables, documents, cached keys, relationships, and embeddings under one transactional layer. You choose the model that fits each job instead of standing up a new database for it, and everything shares the same durability, backup, and access controls.
- One dataset, queried as tables, documents, or a graph
- No copy pipelines to keep systems in sync
- One engine to deploy, monitor, and back up
It speaks the protocols your apps already use
SynergyDB answers the PostgreSQL, MySQL, MongoDB, Redis, Neo4j Bolt, and Kafka wire protocols, among others. Your application connects with its existing driver and your ORM issues the same queries — the database on the other end is what changed, not your code.
- Connect with the drivers and clients you already have
- Common ORMs work against it unchanged
- Migrate one service at a time, not all at once
Live event streams next to the data they touch
Publish and consume events over a Kafka-compatible interface in the same engine that stores your tables and documents. Producers and consumers read and write the underlying data directly, so you don't run a separate broker and a separate database and wire them together.
- Kafka-compatible publish and subscribe
- Events and stored state in one system
- One place to secure, back up, and observe
A truth substrate — long-term memory for AI
Store embeddings alongside the records they describe and run similarity search in the same query. Because the vectors sit next to your source-of-truth data, retrieval reads from the same store your application writes to — one system to keep an AI feature's memory current, not a database plus a separate vector store to reconcile.
- Vector similarity search built into the engine
- Embeddings stored beside the data they index
- Retrieval and records stay consistent — one source of truth
One engine underneath all of it
Every model shares one transactional core with the same durability, backup, and access controls — the same "own every byte" approach behind everything Synergy Technologies builds.
Bringing an existing app across? See how compatibility works →