Top 3,360 Machine Learning Software SaaS Companies in July 2026
As of July 2026, there are 3,360 SaaS companies in Machine Learning Software. They have combined revenues of $51.8B and employ 420.9K people. They have raised $68.6B and serve 2.9B customers combined.
Machine Learning Software encompasses tools and applications that enable systems to learn from data and improve their performance over time without explicit programming. These solutions often utilize algorithms and statistical models to analyze patterns, make predictions, and automate decision-making processes across various domains. Common use cases include predictive analytics, natural language processing, image recognition, and anomaly detection.
Commonly adopted in sectors such as finance, healthcare, marketing, and IT, machine learning software is primarily used by data scientists, business analysts, and IT professionals. Typical features include data preprocessing, model training, evaluation, and deployment, which facilitate the integration of machine learning capabilities into existing workflows. By leveraging large datasets, organizations can enhance operational efficiency, improve customer experiences, and make better-informed strategic decisions.
Developer of a cloud-based emotional analytics platform created to measure and understand emotions in real-time. The company's platform combines neuroscience, cognitive technology and machine learning to understand emotions and their impact on learning, health and well-being as well as tracks, predicts and recommends customers based on relevant emotional states, enabling users to receive a new technology of artificial intelligence with empathy to improve their lives.
Innovation OS - one platform for everything innovation The ITONICS Innovation OS combines human ingenuity and machine intelligence in a collaborative innovation management software.
Developer and provider of data science platform intended for data scientists. The company's SaaS platform which equips data science teams with high-leverage automation tools which include one-click publishing and sharing, scaling to GPUs, flexible custom environments, and eliminating hours of traditional, manual work, enabling companies to perform data science at a new level of scale, with one-click solutions.
Next Gate Tech, a Luxembourg-based FundTech, provides SaaS solutions for asset management with machine learning, data analytics, and cloud-based automation.
indigitall, founded in 2013, develops and manages a Mobile Engagement Automation (MEA) marketing platform that allows its clients and users to efficiently manage all of their digital marketing and communication campaigns from a single dashboard while constantly improving data segmentation and conversion rates through Artificial Intelligence.
Our Cloud and SaaS platform is focused on customer loyalty, engagement and retention through the most advanced digital marketing tools that allow personalized communication and messages to be segmented and sent at the most precise times and through the best channel.
Indigitall, fundada en 2013, es una empresa que desarrolla una plataforma de marketing MEA (Mobile Engagement Automation) que permite a los Clientes gestionar eficazmente sus campañas de marketing digital y de comunicación para mejorar la fidelización de sus usuarios y audiencia y las conversiones en cuanto a su contenidos, servicios y productos.
La solución de la empresa es una plataforma SaaS enfocada en la fidelización y retención de los clientes a través de las herramientas digitales de marketing más avanzadas que permite una comunicación personalizada, segmentada en el momento más preciso y de la manera más adecuada utilizando funcionalidades de Inteligencia Artificial.
Revenue
$9.9M
Customers
-
Year founded
2013
Funding
-
Team size
90
Growth
-
Inclusion Criteria
- Must provide tools for data preprocessing and model training
- Should support both supervised and unsupervised learning methods
- Must enable the deployment of trained models for real-world application
- Should include analytics capabilities for model evaluation and performance tracking
- Must cater to users such as data scientists, analysts, and software engineers
- Not just for simple data visualization; must also enable predictive modeling and automation
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