As of September 2026, Latka tracks 3 key value databases SaaS companies with $1M–$5M in annual revenue. They have combined revenues of $8.1M and employ 68 people. They have raised $3M.
Every company below sells key value databases 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.
Key-value databases are a type of non-relational database, commonly categorized under NoSQL databases, that store data as a collection of key-value pairs. This structure provides a simple and efficient way to access and manipulate data, making these databases particularly suited for scenarios requiring high-speed transactions and scalability. They are widely used in applications such as caching, session storage, and real-time analytics, where quick retrieval and storage of data is paramount. Typical features of key-value databases include high availability, partitioning, and various data expiration policies. They often support horizontal scaling, allowing them to handle increased loads seamlessly. Common buyer personas for key-value databases include software developers and data engineers, who seek to implement flexible and scalable data solutions, as well as IT operations teams focused on maintaining performance and availability in dynamic environments.
Sorting: Highest -> Lowest
Showing 3 of 3 companies ranked by annual revenue.
Dragonfly 2345 Yale Street First Floor 94306-1449 Palo Alto, United States
Dragonfly is a drop-in Redis replacement, designed to meet the performance and efficiency requirements of modern cloud-based applications. Organizations that switch to Dragonfly require less hardware and achieve dramatically improved data performance.
Madrid, Spain
Developer of an ultra-scalable operational database designed to provide full SQL and ACID transactions to cloud applications with standard interfaces. The company's ultra-scalable operational database offers real-time analytics blending the capabilities of an operational database and the ones of a data warehouse in a single platform, empowering its customers to implement professional results without the need to copy their data in time and resource-consuming projects.
Federal Way, Washington, United States
McObject develops the eXtremeDB database system for intelligent devices, embedded systems, and scalable distributed applications. Why should you learn more about the eXtremeDB database management system? • The only commercially supported DBMS that covers data management from hard real-time device to server. • Active Replication Fabric™ solves 5 key challenges for IoT developers • Platform independent interoperability • Footprint starting at 150kb • Type-safe API • Advanced debugging and corruption prevention • 99.999% High Availability options • Distributed query processing • Elastic scalability via sharding • Optimized for time series data • The exclusive Pipelining feature offers the lowest possible latency eXtremeDB/rt is the only commercial off-the-shelf hard real-time DBMS. • Adaptable real-time transaction managers • A highly configurable kernel • Support for all major commercial RTOS • Ideal for time sensitive sensor data fusion • Support for shared data in Asymmetric Multiprocessing (AMP) configurations
Latka tracks 3 key value databases SaaS companies with $1M–$5M in annual revenue. Together they generate $8.1M in annual revenue and employ 68 people.
Dragonfly is the largest, with $3.6M in annual revenue, founded in 2022.
The average key value databases SaaS company in this list makes $2.7M a year, across 3 companies with reported revenue.
Ranked by annual revenue, the leaders are Dragonfly, LeanXcale and McObject.
The 3 key value databases SaaS companies with $1M–$5M in annual revenue tracked here have raised $3M in disclosed funding between them.
- The product must store data primarily in key-value pairs. - It should support high availability and scalability for large datasets. - The database should allow for quick retrieval and storage of data. - It must include features for data expiration or time-to-live settings. - The system should not be limited to relational data structures; must also support unstructured or semi-structured data.
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