As of September 2026, Latka tracks 6 vector database software companies with $5M–$10M in annual revenue. They have combined revenues of $41.4M and employ 487 people. They have raised $164M.
Every company below sells vector database software to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.
Vector Database Software is designed to store, manage, and retrieve high-dimensional data representations, typically in the form of vectors. These databases enable efficient similarity searches and rapid data retrieval, which are essential for applications like machine learning and artificial intelligence. Primary use cases include natural language processing, recommendation systems, and image or video search, where traditional databases fall short in handling complex data structures.
Sorting: Highest -> Lowest
Showing 6 of 6 companies ranked by annual revenue.
Palo Alto, California, United States
Provider of an open source in-memory data grid platform designed to modernize existing applications. The company's open source in-memory data grid platform with installed clusters offer operational in-memory computing, enabling companies to manage their data and distribute processing using in-memory storage and parallel execution for breakthrough application speed and scale.
Berlin, Germany
Qdrant is an open-source vector search engine. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more.
Reno, Nevada, United States
Positron delivers vendor freedom and faster inference for both enterprises and research teams, by allowing them to use hardware and software explicitly designed from the ground up for generative and large language models (LLMs). Through lower power usage and drastically lower total cost of ownership (TCO), Positron enables you to run popular open source LLMs to serve multiple users at high token rates and long context lengths. Positron is also designing its own ASIC to expand from inference and fine tuning to also support training and other parallel compute workloads.
New York, New York, United States
Timescale is addressing one of the largest challenges (and opportunities) in databases for years to come: helping developers, businesses, and society make sense of the data that humans and their machines are generating in copious amounts. TimescaleDB is the only open-source time-series database that natively supports full-SQL, combining the power, reliability, and ease-of-use of a relational database with the scalability typically seen in NoSQL systems. It is built on PostgreSQL and optimized for fast ingest and complex queries. TimescaleDB is deployed for powering mission-critical applications, including industrial data analysis, complex monitoring systems, operational data warehousing, financial risk management, and geospatial asset tracking across industries as varied as manufacturing, space, utilities, oil & gas, logistics, mining, ad tech, finance, telecom, and more. Timescale is backed by NEA, Benchmark, Icon Ventures, Redpoint Ventures, Two Sigma Ventures, and Tiger Global. Documentation: https://docs.timescale.com GitHub: https://github.com/timescale/timescaledb Twitter: https://twitter.com/timescaledb
Latka tracks 6 vector database software companies with $5M–$10M in annual revenue. Together they generate $41.4M in annual revenue and employ 487 people.
Hazelcast is the largest, with $9.2M in annual revenue, founded in 2008.
The average vector database software company in this list makes $6.9M a year, across 6 companies with reported revenue.
Ranked by annual revenue, the leaders are Hazelcast, qdrant, Vespa.ai, CrateDB and Positron AI.
The 6 vector database software companies with $5M–$10M in annual revenue tracked here have raised $164M in disclosed funding between them.
- Must efficiently store and index high-dimensional vectors. - Should support fast retrieval and similarity search capabilities. - Must provide capabilities for CRUD (Create, Read, Update, Delete) operations specifically for vector data. - Should allow for metadata filtering to enhance search results. - Not just a general-purpose database; must be optimized specifically for vector data handling.
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