Mountain View, California, United States
Provides database server so companies can compile and analyze large amounts of data
- Revenue
- $42.8M
- Year founded
- 2009
- Team size
- 287
As of September 2026, Latka tracks 3 NoSQL databases SaaS companies with $10M–$100M in annual revenue. They have combined revenues of $83.1M and employ 437 people. They have raised $53M and serve 1K customers combined.
Every company below sells NoSQL 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.
NoSQL databases represent a category of database management systems designed to handle large volumes of unstructured and semi-structured data. Unlike traditional SQL databases, NoSQL solutions offer flexible data models, enabling users to store and retrieve data in various formats, such as key-value pairs, documents, column-family, or graphs. This flexibility makes them particularly suited for real-time analytics, content management, and applications requiring high scalability and performance. The primary use cases for NoSQL databases include managing big data, facilitating high-velocity data ingestion, and supporting applications in industries such as e-commerce, finance, healthcare, and social media. Common features of NoSQL databases include horizontal scaling, schema-less data storage, and support for distributed computing. Users typically include IT professionals, data analysts, and developers who seek to implement systems that can efficiently adapt to changing data requirements and support complex query operations. Organizations adopting NoSQL technology often require solutions that can manage a mix of structured and unstructured data, integrate seamlessly with existing architectures, and scale dynamically based on user demand. As companies increasingly focus on harnessing data for insights and operational efficiencies, NoSQL databases have become an essential component of modern data architecture.
Sorting: Highest -> Lowest
Showing 3 of 3 companies ranked by annual revenue.
Mountain View, California, United States
Provides database server so companies can compile and analyze large amounts of data
Shenzhen, Guangdong, China
Developer of database software designed to offer distributed database access for enterprises. The company's software helps enterprises securely store large amounts of transaction data and create data centers, enabling enterprises to improve their data storage and retrieval abilities.
Redwood City, California, United States
Developer of AI-oriented intelligent data processing platform intended to improve query performance. The company's OLAP database systems are based on GPU hardware acceleration which focuses on multiple fields including finance, telecommunications, healthcare and retail, enabling its customers to reduce hardware, operation and maintenance cost by over 10 times.
Latka tracks 3 NoSQL databases SaaS companies with $10M–$100M in annual revenue. Together they generate $83.1M in annual revenue and employ 437 people.
Aerospike is the largest, with $42.8M in annual revenue, founded in 2009.
The average NoSQL databases SaaS company in this list makes $27.7M a year, across 3 companies with reported revenue. They serve 1K customers combined.
Ranked by annual revenue, the leaders are Aerospike, SequoiaDB and ZILLIZ.
The 3 NoSQL databases SaaS companies with $10M–$100M in annual revenue tracked here have raised $53M in disclosed funding between them.
- The product must support various data models such as key-value, document, column-family, or graph. - Must provide capabilities for real-time data processing and analytics. - Should enable horizontal scaling to handle large volumes of data efficiently. - Must allow for schema-less data storage to accommodate unstructured and semi-structured data. - Should support distributed computing and high availability to minimize downtime. - Not just for read-heavy applications; must also effectively manage write-heavy workloads.
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