One Engine. Every Data Model.
SynergyDB is an AI-native database that unifies relational, document, graph, vector, and time-series data in a single engine. Query with SQL, MongoDB MQL, Cypher, or GraphQL across 16+ wire protocols — your existing drivers work out of the box.
Your data shouldn't be scattered across five databases
Relational data in Postgres, documents in MongoDB, graphs in Neo4j, vectors in Pinecone, metrics in InfluxDB. Your infrastructure sprawls, data goes out of sync, and every cross-model query is an application-level join.
5+ separate databases
PostgreSQL for tables, MongoDB for documents, Neo4j for graphs, Pinecone for vectors, InfluxDB for metrics — and Redis for caching between them all
5+ operational burdens
Separate backups, upgrades, monitoring, security patches, and scaling strategies. Each system needs its own DBA expertise.
Data synchronization hell
CDC pipelines, ETL jobs, and reconciliation scripts. ACID transactions can't span multiple databases without Saga patterns or 2PC.
No cross-model queries
Want to join a SQL table with a graph traversal and a vector search? That's three round-trips and application-level joins.
1 engine, 5 data models
Relational, document, graph, vector, and time-series — all in one LSM-tree storage engine with full ACID transactions across models
16+ wire protocols
PostgreSQL, MySQL, MongoDB, Neo4j Bolt, Redis, Cassandra, Elasticsearch, gRPC, REST, and more — all served by a single binary
True cross-model queries
Join SQL tables with graph traversals and vector searches in one query using SynergyQL. No ETL, no sync, no eventual consistency.
AI-native from day one
Built-in HNSW vector indexing, OpenAI/HuggingFace embedding integration, and LangChain compatibility for RAG applications
Built for the engineers who build everything else
Built from first principles in Rust to solve the polyglot persistence problem. One storage engine that natively understands relational, document, graph, vector, and time-series data.
AI-Native Vector Search
First-class VECTOR(n) data type with HNSW indexing for sub-millisecond similarity search. Built-in OpenAI and HuggingFace embedding integration. Power RAG apps, semantic search, and recommendations natively.
5 Data Models, 1 Engine
Relational, document, graph, vector, and time-series data in a single storage engine. Query with SQL, MongoDB MQL, Cypher, GraphQL, or SynergyQL. Your existing drivers and ORMs work out of the box.
16+ Wire Protocols
Native PostgreSQL, MySQL, MongoDB, Neo4j Bolt, Redis, Cassandra, Elasticsearch, InfluxDB, gRPC, and REST protocols. Connect with existing drivers, zero code changes. Import from any source with live migration.
Enterprise Security
7-layer security: RBAC, row-level security, TLS 1.2/1.3, AES-256-GCM at rest, SCRAM-SHA-256 auth, full audit logging, and transparent encryption proxy. HIPAA, SOC 2, GDPR, and PCI DSS compliant.
How it works under the hood
SynergyDB sits between your applications and storage, translating any protocol into optimized operations on a unified storage engine.
Your Applications
Web App
PostgreSQL driver
API Service
MongoDB driver
AI/ML Pipeline
Vector API / REST
Graph Analytics
Neo4j Bolt driver
Unified Storage Layer
LSM-tree storage engine with WAL, MVCC, Raft consensus, and configurable compression (LZ4, Zstd)
Performance that speaks for itself
Built in Rust with an LSM-tree storage engine, HNSW vector indexing, and Tantivy-powered full-text search. These numbers come from our benchmark suite on production-grade hardware.
0+
Wire Protocols
0.000ms
p50 Point Query
0K
Rows/sec Batch Insert
0
Data Models, 1 Engine
Trusted by engineering teams at
Ready to unify your data infrastructure?
Replace your multi-database stack with a single AI-native engine. Written in Rust, built for production. Get started in 30 seconds.
Or get started in 30 seconds:
$ curl -sSL https://install.synergydb.io | sh