Kinetica
Kinetica is a GPU-accelerated, in-memory database platform for real-time analytics on streaming and historical data.
Profile
GPU-accelerated, in-memory SQL database for real-time analytics on streaming and historical data with sub-second query latency.
Kinetica is a GPU-accelerated, in-memory database platform for real-time analytics on streaming and historical data. Founded in 2009 by Amit Vij and Nima Negahban, the company emerged from a consulting project for the U.S. Army Intelligence and Security Command and NSA, which needed a system to track threats in real time when no commercial solution existed. Originally called GIS Federal and later GPUdb, Kinetica rebranded in 2016 to reflect its expanded market focus beyond geospatial workloads.
The company is headquartered in Arlington, Virginia, with offices in San Francisco and across Europe and Asia-Pacific. As of 2024, Kinetica operates with approximately 69 employees and generated $10.4M in annual recurring revenue with a $31.1M valuation, achieving profitability without raising Series B funding despite its $77.5M in earlier rounds (a $50M Series A in 2017 led by Canvas Ventures and Meritech Capital Partners).
Kinetica's platform supports multi-modal analytics—structured data, time-series, geospatial, and vector workloads—on a single SQL database, with sub-second query response times powered by GPU and CPU vectorization. The company ships native SQL-GPT, a language-to-SQL model fine-tuned for telecommunications, financial services, automotive, and logistics. Recent deployments include Foursquare leveraging Kinetica for real-time spatial queries across billions of location data points, and the company serves enterprise customers including Verizon, Citibank, Liberty Mutual, Ford, USPS, and T-Mobile. In August 2025, Kinetica was recognized as both a Leader and Fast Mover in the GigaOm Sonar Report for Real-Time Analytical Databases.
Who buys this
- Telecommunications operators analyzing real-time network and anomaly detection data
- Financial services firms running millisecond-latency trading and risk analytics
- Defense and intelligence agencies performing geospatial situational awareness
- Location-intelligence platforms accelerating spatial queries at billion-scale data volumes
- Logistics and automotive companies optimizing operations from IoT and sensor streams
Publicly disclosed clients
- Verizon
- Citibank
- Liberty Mutual
- Ford
- Foursquare
- U.S. Postal Service
- T-Mobile
Strengths and what to watch
Strengths
- Native SQL-GPT module for language-to-SQL conversion, industry-tuned for telco and finance without exposing data to public LLMs
- Multi-modal single-platform support for structured, time-series, geospatial, and vector analytics—GigaOm reports Kinetica covers the widest range of workload types
- Profitable and bootstrapped to $10.4M ARR on a 69-person team without Series B, indicating capital-efficient go-to-market
Watch for
- No Series B funding since 2017 despite 16+ year tenure; capital constraints may limit sales/marketing scale relative to well-funded competitors like Snowflake and ClickHouse
- Geospatial and defense origins create concentration risk; satellite revenue from financial and telecom sectors remains unproven at scale beyond named references
- GPU commodity pricing and NVIDIA dependency; if compute costs fall or competitors optimize CPU-only offerings, performance-per-dollar advantage narrows
Recent moves
Key Information
- Industry
- GPU Databases
- Founded
- 2009
- Headquarters
- Arlington, Virginia
Frequently Asked Questions
What is Kinetica?
Kinetica is a GPU-accelerated, in-memory SQL database designed for real-time analytics on streaming and historical data. Founded in 2009, it delivers sub-second query latency by combining GPU and CPU vectorization, supporting structured data, time-series, geospatial, and vector workloads on a single platform.
How fast is Kinetica's query performance?
Kinetica achieves sub-second query response times using GPU and CPU vectorization. This enables millisecond-latency applications like financial trading, real-time network anomaly detection, and spatial queries across billions of data points, as demonstrated by deployments at Verizon and Foursquare.
What industries use Kinetica?
Kinetica serves telecommunications operators for network analytics, financial services firms for trading and risk analysis, defense agencies for geospatial awareness, location-intelligence platforms like Foursquare, and logistics companies optimizing IoT sensor streams. Key clients include Verizon, Citibank, Liberty Mutual, and Ford.
What is SQL-GPT and how does it work?
SQL-GPT is Kinetica's native language-to-SQL module that converts natural language queries to SQL without exposing data to public LLMs. It's fine-tuned for telecommunications, financial services, automotive, and logistics industries, keeping sensitive customer and trading data private while enabling AI-powered analytics.
Who founded Kinetica and what's its origin story?
Kinetica was founded in 2009 by Amit Vij and Nima Negahban from a consulting project for the U.S. Army Intelligence and Security Command and NSA. Originally called GIS Federal and later GPUdb, the company rebranded to Kinetica in 2016 to reflect expanded market focus beyond geospatial workloads.
Is Kinetica profitable and well-funded?
Yes. Kinetica generates $10.4M annual recurring revenue and achieved profitability on a 69-person team without Series B funding, despite raising $77.5M in earlier rounds. This capital-efficient model since 2017 suggests strong unit economics, though capital constraints persist relative to competitors like Snowflake.
How Kinetica compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Kinetica
- Positioning
- GPU-accelerated, in-memory SQL database for real-time analytics on streaming and historical data with sub-second query latency.
- Customer segments
- Telecommunications operators analyzing real-time network and anomaly detection data
- Strengths
- Native SQL-GPT module for language-to-SQL conversion, industry-tuned for telco and finance without exposing data to public LLMs
- Watch for
- No Series B funding since 2017 despite 16+ year tenure; capital constraints may limit sales/marketing scale relative to well-funded competitors like Snowflake and ClickHouse
- Recent moves
- Kinetica named Leader and Fast Mover in 2025 GigaOm Sonar Report for Real-Time Analytical Databases
SingleStore
- Positioning
- Unified in-memory SQL database for OLTP and OLAP workloads, positioning as infrastructure for operational AI on live data.
- Customer segments
- Financial services, logistics, and SaaS firms needing real-time analytics embedded in applications; data engineering and platform teams.
- Strengths
- Single engine handles concurrent transactional and analytical queries, eliminating separate ETL pipelines for operational analytics and AI serving.
- Watch for
- Customers report pricing escalates sharply beyond a few terabytes; large-cluster deployments frequently exceed initial business-case estimates.
- Recent moves
- Acquired BryteFlow, a CDC and ERP/CRM data integration platform (October 2024), rebranded as SingleStore Flow.
ClickHouse
- Positioning
- Columnar real-time analytics database with open-source origin and managed cloud service, targeting event-stream and observability workloads at scale.
- Customer segments
- Engineering teams at fintech, ad-tech, and cybersecurity firms; over 3,000 cloud customers including Anthropic, Tesla, and Mercado Libre.
- Strengths
- Sub-second query latency on petabyte-scale event and time-series data via columnar compression and vectorized execution.
- Watch for
- Multi-table JOIN queries are a documented performance limitation; customers with relational workloads must denormalize data or rewrite queries.
- Recent moves
- Raised $400M Series D led by Dragoneer and acquired Langfuse for LLM observability, both announced January 2026.
Yellowbrick Data
- Positioning
- Hybrid cloud data warehouse for enterprise modernization, deployable across on-premises appliances, private Kubernetes clusters, and public cloud.
- Customer segments
- Large enterprises (10,000-plus employees) in financial services, insurance, and government modernizing legacy warehouses; IT infrastructure buyers.
- Strengths
- Flexible deployment across appliances, Kubernetes, and public cloud without architecture changes; validated Dell and Red Hat OpenShift reference architecture.
- Watch for
- No external funding since November 2021 Series C1; limited capital constrains R&D and sales expansion relative to recently funded competitors.
- Recent moves
- CEO Neil Carson publicly launched Floe (floedb.ai), a parallel lakehouse SQL venture built with Yellowbrick engineers, early 2026.
Sources
- getlatka.com — Revenue ($10.4M ARR), valuation ($31.1M), employee count (69), profitability status
- www.crunchbase.com — Total funding ($77.5M), funding rounds and investors, Series A details, company status
- techcrunch.com — Series A funding ($50M), lead investors Canvas Ventures and Meritech Capital Partners, founders Amit Vij and Nima Negahban, origin story (NSA/Army project)
- www.prnewswire.com — 2025 GigaOm Sonar recognition, platform capabilities (multi-modal analytics, vectorization), customer use cases (telecom, defense, location intelligence)
- www.kinetica.com — March 2025 partnership announcement with Lander Analytics
- foursquare.com — Foursquare partnership, Kinetica as compute engine for spatial queries
- www.glassdoor.com — Employee reviews, culture ratings, workplace feedback