Kinetica is a database engine built specifically for AI agent workflows rather than general-purpose storage retrofitted for AI: it supports GraphRAG (knowledge-graph-enhanced retrieval-augmented generation) natively, and ships Agent Skills and a Toolbelt for building directly on the platform rather than stitching an agent stack together across multiple separate database products.
Single-SQL-query access across vector search, graph, spatial, and time-series operations is what the “stop duct-taping five databases” positioning actually refers to – a typical AI agent stack today might combine a vector database, a graph database, and a time-series store, each with its own query language and consistency model, while Kinetica consolidates that into one engine an agent can query consistently. Vectorized processing across GPU and CPU cores backs the performance claims: 10x faster vector indexing than competitors and sub-50ms time-series queries on billion-row datasets.
Real-time ingestion and immediate querying without an ETL pipeline in between removes another common bottleneck for agent-facing data infrastructure specifically, where stale data undermines the value of an agent’s response. A free Docker-based Developer Edition lets you evaluate locally; Kinetica Cloud offers pay-as-you-go pricing, with Enterprise Edition available by contacting sales.
