As of September 2026, Latka tracks 3 relational databases SaaS companies under $1M in annual revenue. They have combined revenues of $1.6M and employ 18 people.
Every company below sells relational 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.
Relational databases are organized collections of data that are structured into tables, consisting of rows and columns. This architecture allows for efficient data management and retrieval, as related data points can be accessed and manipulated using a structured query language (SQL). They are widely used in various applications, such as financial systems, e-commerce platforms, and customer relationship management systems, where handling structured data with complex relationships is essential. The primary features of relational databases include data integrity, normalization, and the ability to handle complex queries. Users can perform operations like adding, updating, and deleting data, as well as executing complex queries that join multiple tables to extract meaningful insights. Typical buyer personas for this software include database administrators, IT professionals, and business analysts, who rely on these systems to store and analyze large volumes of data and support decision-making processes.
Sorting: Highest -> Lowest
Showing 3 of 3 companies ranked by annual revenue.
Stockholm, Sweden
Provider of a database management system intended to revitalize enterprise software systems. The company's database management system is a combined in-memory database engine and application server for development of high performance business applications enabling enterprises to make their software applications perform much faster.
San Diego, California, United States
Database Lab Engine (DLE) enables 🖖 DB branching and ⚡️ thin cloning for *any* Postgres database and empowers DB testing in CI/CD. This improves product quality and time-to-market. GitHub ⭐️👍 https://github.com/postgres-ai/database-lab-engine
San Francisco, California, United States
The amount of time businesses spend on their databases is altogether too much time. The Citus Data team is fixing this problem. Citus is worry-free Postgres. Built to scale out, Citus is an extension to Postgres that transforms Postgres into a distributed database. Citus is available as open source, as on-prem software, as a fully-managed database as a service—and now Citus is also available on Microsoft Azure, as Hyperscale (Citus) on Azure Database for PostgreSQL. Whether you have a multi-tenant application that needs to scale out, or you need millisecond performance for your real-time analytics customers, with Citus, you can focus on your application—not your database. Founded in 2011 and acquired by Microsoft in 2019, the Citus Data team is a Y Combinator alumnus with offices in San Francisco, Istanbul, and Amsterdam. The Series A for Citus Data was led by Khosla Ventures and Data Collective. For more information about Microsoft's acquisition of Citus Data, read the post from our co-founders: https://www.citusdata.com/blog/2019/01/24/microsoft-acquires-citus-data/ Citus online: www.citusdata.com Documentation: docs.citusdata.com GitHub: github.com/citusdata/citus
Latka tracks 3 relational databases SaaS companies under $1M in annual revenue. Together they generate $1.6M in annual revenue and employ 18 people.
Starcounter is the largest, with $578.7K in annual revenue, founded in 2006.
The average relational databases SaaS company in this list makes $522.9K a year, across 3 companies with reported revenue.
Ranked by annual revenue, the leaders are Starcounter, Postgres.AI 💙💛 and Citus Data.
- Must support structured data organization into tables with defined schemas - Must allow complex querying through SQL or similar languages - Must provide features for data integrity and consistency - Must facilitate data relationships through foreign keys and indexing - Targeted towards use cases requiring secure and reliable data management - Not purely focused on unstructured data handling; must deal with structured relational data as a priority
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