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Top 21 Predictive Maintenance Software SaaS Companies in May 2026

As of May 2026, there are 21 SaaS companies in Predictive Maintenance Software. They have combined revenues of $199.8M and employ 1.5K people. They have raised $242.8M and serve - customers combined.

Predictive maintenance software is designed to forecast when equipment failures might occur, allowing for proactive maintenance actions. By utilizing various condition-monitoring techniques, such as vibration analysis, thermal imaging, and IoT-driven data collection, these solutions help organizations minimize downtime and extend the operational lifespan of their assets. Typically employed in industries like manufacturing, energy, and transportation, predictive maintenance software streamlines workflows that involve data analysis and equipment monitoring. Common features include real-time alerts, analytical tools for diagnosing issues, and dashboards for tracking equipment health, which cater to maintenance engineers, operations managers, and IT professionals alike. Users benefit from a shift away from traditional time-based maintenance schedules, instead focusing on the actual condition of assets and predicting failures before they happen. This results in cost savings, enhanced safety, and improved reliability while enhancing overall operational efficiency.

Companies
21
Revenue
$199.8M
Funding
$242.8M
Employees
1.5K

Filters

Sorting: Highest -> Lowest

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Top Predictive Maintenance Software Companies

Showing 10 of 2 companies ranked by annual revenue.

1
DesignX

Noida, Uttar Pradesh, India

DesignX specializes in migrating and bridging manufacturing companies to Industry 4.0, offering functionalities including production audit and management, quality checks, safety audits, maintenance management, and supply chain management.

Revenue
$19.3M
Customers
-
Year founded
2015
Funding
-
Team size
121
Growth
-
2
FRACTTAL

Madrid, Spain

cloud-based enterprise asset management software

Revenue
$15M
Customers
-
Year founded
2015
Funding
-
Team size
190
Growth
-

Inclusion Criteria

- The software must use data analysis and monitoring techniques to predict equipment failures. - It should enable real-time condition monitoring through sensors or IoT technology. - Must provide features for setting alerts and notifications based on equipment status. - The solution should include analytics tools for diagnosing potential issues before they occur. - Must demonstrate functionality in industries relying on heavy machinery or critical equipment. - Not just focused on scheduling maintenance; must also prioritize predictive insights and analytics.