Accelerated by

GPU + CPU VECTORIZED · LOCKLESS · SUB-SECOND

NEW · Agent Skills

Stop duct-taping five databases
behind your agent stack.

Q: What problem does Kinetica solve?

Kinetica replaces the five specialty databases typically duct-taped behind an agent stack with a single engine that handles all workloads simultaneously.

Q: How fast can Kinetica ingest while serving live queries?

On an 8-node cluster with NVIDIA T4 GPUs, Kinetica sustained 800k records/sec ingest while serving 40+ concurrent dashboards — no degradation.

Q: How does performance hold up as data volume grows?

Query throughput degrades less than 6% scaling from 2B to 8B rows, with 100+ TPS sustained throughout.

Q: How does Kinetica compare to specialty databases on its core workloads?

10x faster vector indexing than Milvus, 6x faster graph than Neo4j, 240x faster spatial than PostGIS, 3x faster time-series than SingleStore.

Q: How do I actually try Kinetica?

Free Developer Edition: docker run -p 9191:9191 kinetica/developer-edition. Full feature set, no credit card.

Real-time, multi-modal, sub-second — so your agents stop waiting and start reasoning.

Give your agents the database they deserve

npx skills add kineticadb/agent-skillsNEW

10x

Vector Search

10x faster vector indexing than Milvus & Qdrant

Graph

6x faster graph queries than Neo4j at scale

240x

Spatial

240x faster spatial queries than PostGIS at scale

< 50ms

Time Series

Sub-50ms time-series queries on billion+ records — no pre-aggregation

10x

Key Value Lookup

10x higher key value query throughput than ClickHouse

Architecture

Why Kinetica is faster

Built from scratch for modern hardware — not retrofitted onto legacy engines.

Traditional databases process one row at a time. Kinetica processes all of them simultaneously.

Experience unparalleled performance with Kinetica's vectorized engine.

No serialization bottleneck

TRADITIONAL — SEQUENTIAL ROW PROCESSING

← waiting← processing...

1 core active · N cores idle · high latency

KINETICA — VECTORIZED STENCIL ACROSS ALL CORES

Core 1

Core 2

Core 3

R10

R11

R12

Core 4

R13

R14

R15

R16

GPU 1

GPU 2

All cores active simultaneously · memory bandwidth saturated · sub-second result

Real-time

One engine. Every workload. Ingest fast. Query immediately.

No specialty databases, no ETL pipelines, no tradeoffs between speed and breadth.

In practice

One query. Five retrieval modes.

Every analytical type — vector, graph, spatial, time-series — in a single SQL statement.

vector·SQL filter·graph·spatial·time-series

A real prompt, the SQL Kinetica generates ↓

USER

Predict ETA of vehicles within 2km to an accident location among available within the last 5 minutes

AGENT

› decomposing: spatial join + time-series forecast + graph routing

AGENT

SELECT INDEX AS VEHICLE_ID,
       COST / 60 AS ETA_MINUTES
FROM   MATCH_GRAPH(
         GRAPH         => 'osm_seattle',
         SOLVE_METHOD  => 'match_batch_solves',
         SAMPLE_POINTS => INPUT_TABLES((
           SELECT ID AS OD_ID, -- vehicle id
                  ST_GEOMFROMTEXT('POINT(-122.379273 47.515505)') AS ORIGIN_WKTPOINT, -- accident location
                  LOCATION AS DESTINATION_WKTPOINT -- vehicle location
           FROM   osm_seattle_fleet
           WHERE  ST_DWITHIN(LOCATION, ST_GEOMFROMTEXT('POINT(-122.379273 47.515505)'), 2000, 1)
           AND    ACTIVETIME > NOW() - INTERVAL '5' MINUTE
         )),
         OPTIONS       => KV_PAIRS(inverse_solve = 'true')
       )
ORDER BY 2 ASC;

KINETICA

100ms14 rows returned· 1 engine · 1 query · no pre-aggregation

AGENT

› done. drafting reroute for 14 vehicles →

1 SQL: spatial + time-series + graph100ms240× vs PostGIS

Trusted by teams in defense, finance, and enterprise AI

Independent Benchmark Study

Radiant Advisors · 2025

SpatialGraphTime-seriesStreaming

“Kinetica outperformed PostGIS in every query and was the only database to pass all feasibility tests across geospatial, time-series, graph, and streaming.”

All feasibility tests passed

Director of Data Engineering

Enterprise Logistics · Fortune 500

SpatialTime-seriesStreaming

“We went from hours of batch processing to sub-second queries on live vehicle telemetry across our entire fleet. The geospatial join performance is unlike anything we’d seen before.”

Fleet-scale telemetry · real-time

Chief Data Architect

Defense & Intelligence · Federal

VectorGraphSpatialStreaming

“Real-time situational awareness at a scale we couldn’t achieve with any other platform. Kinetica fuses multi-source sensor feeds in milliseconds — that’s operationally decisive.”

Multi-source sensor fusion · federal

Up and running in 5 minutes

Developer edition is free. Full feature set. No credit card.

Try Free Read the docs

Frequently asked questions

What problem does Kinetica solve?

Kinetica replaces the five specialty databases typically duct-taped behind an agent stack with a single engine that handles all workloads simultaneously.

How fast can Kinetica ingest while serving live queries?

On an 8-node cluster with NVIDIA T4 GPUs, Kinetica sustained 800k records/sec ingest while serving 40+ concurrent dashboards — no degradation.

How does performance hold up as data volume grows?

Query throughput degrades less than 6% scaling from 2B to 8B rows, with 100+ TPS sustained throughout.

How does Kinetica compare to specialty databases on its core workloads?

10x faster vector indexing than Milvus, 6x faster graph than Neo4j, 240x faster spatial than PostGIS, 3x faster time-series than SingleStore.

How do I actually try Kinetica?

Free Developer Edition: docker run -p 9191:9191 kinetica/developer-edition. Full feature set, no credit card.

Stop duct-taping five databasesbehind your agent stack.

Why Kinetica is faster

One engine. Every workload. Ingest fast. Query immediately.

One query. Five retrieval modes.

Up and running in 5 minutes

Frequently asked questions

Stop duct-taping five databases
behind your agent stack.