AI BriefingSlater – A Low-Memory graphdb for Read-Intensive Graphs
Key point
A new graphdb has been released that can handle large-scale graph data while minimizing memory usage.
Details
Slater is a low-memory graph database that can handle hundreds of millions of nodes and billions of edges using only a fixed cache budget, regardless of graph size.
Unlike existing graph DBs such as Neo4j or Memgraph, which require RAM proportional to data size, Slater compiles the graph into a disk-based image and loads it via paging only when needed. This allows graph size and memory cost to be decoupled.
Key features include:
- Bolt protocol support: Existing Neo4j drivers can be used as-is.
- RAG and vector search: Disk-native vector search is integrated alongside graph data, optimized for building knowledge graphs for RAG (Retrieval-Augmented Generation).
- Read-Write support: Provides durable write capability to add nodes or modify properties directly via Bolt, without needing to rebuild the data image.
- Scalability: Cheap, stateless read replicas can be easily deployed, dramatically lowering the cost of operating large-scale knowledge graphs.
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