As of August 2026, Latka tracks 17 data observability software companies under $1M in annual revenue. They have combined revenues of $7.9M and employ 86 people. They serve 1K customers combined.
Every company below sells data observability 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.
Data observability software is designed to help organizations monitor, analyze, and understand the state of their data throughout its lifecycle. These tools provide insights into data quality, data lineage, and the overall health of data pipelines, allowing teams to quickly identify and troubleshoot issues. Typical use cases include enhancing data reliability, improving operational efficiency, and ensuring compliance with data governance standards. Common features of data observability software include automated monitoring of data flows, alerts on anomalies, and analytics dashboards that present key metrics. Many tools also enable collaboration among teams, empowering data engineers, data analysts, and decision-makers to effectively manage data operations. Typical buyers include IT professionals, data engineers, and business intelligence analysts, aiming to enhance their data management capabilities and drive data-driven decisions.
Sorting: Highest -> Lowest
Showing 10 of 17 companies ranked by annual revenue.
San Francisco, California, United States
Postgres performance at any scale. Deliver consistent database performance and availability through intelligent tuning advisors and continuous database profiling.
Oslo, Norway
Eyer is a headless AI-powered observability and AIOps platform that easily integrates into your existing tooling. Eyer is built to be better, faster and cheaper than the established players.
Grand Rapids, Michigan, United States
We’re the company that set the standard for enterprise-grade integration for IT monitoring. We believe that data is dimensional, not flat, and health and performance metrics must include their relationship to other components in the IT stack to truly provide insights. We deliver this Dimensional Data using the industry’s first IT monitoring integration service (MIaaS), BindPlane. For more information, visit bluemedora.com.
London, England, United Kingdom
Provider of centralized analytics service intended to monitor the performance of MySQL database servers. The company's services offer MySQL monitoring and database performance analytics through an intuitive online interface, enabling clients to configure parameters and identify the key areas where improvements can be made.
United States
InsightCat is a SaaS-based full-stack monitoring system, suited for both cloud and on-premise environments. System can analyze bodies of big data in real time, identify anomalies using machine learning, predict downtimes and provide timely notifications on events.
San Francisco, California, United States
Software to monitor, control, and optimize data centers.
Latka tracks 17 data observability software companies under $1M in annual revenue. Together they generate $7.9M in annual revenue and employ 86 people.
pganalyze is the largest, with $880K in annual revenue, founded in 2012.
The average data observability software company in this list makes $463.2K a year, across 17 companies with reported revenue. They serve 1K customers combined.
Ranked by annual revenue, the leaders are pganalyze, Revyl, Eyer, Blue Medora and DBVu.
- The software must provide real-time monitoring of data quality and health - It should include features for data pipeline visualization and lineage tracking - The product must offer automated alerting mechanisms for data anomalies - Analytics and reporting capabilities to uncover insights about data performance are essential - Solutions should cater to data operations teams, including data engineers and analysts - Not just providing data storage solutions; must also facilitate data quality management and observability - The platform should integrate with existing data infrastructure and tools
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