Top 3,358 Machine Learning Software SaaS Companies in May 2026
As of May 2026, there are 3,358 SaaS companies in Machine Learning Software. They have combined revenues of $46.8B and employ 425.9K people. They have raised $59.3B 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.
OmniJobs analyses and categorises open job ads using AI so that anyone can find matching positions in seconds, regardless of their location or profession. Currently we list over +180K open jobs from +15K companies globally.
SambaNova is the leading Enterprise AI company that delivers a full-stack infrastructure from silicon to software, specializing in machine learning and big data analytics platforms.
Degreed is an AI-driven learning platform that helps identify skill gaps, provides personalized learning, and automates learning experiences for upskilling and certification.
Provider of a Saas recruitment management system designed to make the organizational recruitment process more effective. The company's platform uses artificial intelligence enabled screening program that automatically filters candidates and makes recommendations for companies, enabling recruiters to post job listings across multiple platforms with one click, saving them from the hassle of hopping between portals and achieve better recruitment results by employing suitable candidates.
BUSINESSNEXT is a universe of composable enterprise solutions with a focus on banks and financial services globally.
Recognized as a Leader by leading industry analysts, it leverages technology, innovation, and experience to relentlessly deliver incredible, unique, and human experiences, acing the volatile and complex business environment.
BUSINESSNEXT platforms namely CRMNEXT, CUSTOMERNEXT, DATANEXT & WORKNEXT are AI and ML-driven cloud-agnostic platforms dedicated to enabling digital transformations. It comprises an enriched portfolio of hyper SaaS modular solutions that are responsive, can readily plug & play, and have superlative integration capabilities with the eco-system. BUSINESSNEXT today powers 1 million+ users across 65,000 branches and call centers, managing 1 billion end customers worldwide.
BUSINESSNEXT has its USA headquarter in Raleigh, North Carolina, and its international headquarters in Noida, India. It has a footprint across 5 continents and direct offices in 14 countries across the U.S.A, MEA, and APAC.
ibi™ - A global trusted leader in business intelligence, analytics, and data management solutions.
Our fully-integrated platform empowers organisations to make smarter decisions, strengthen customer and supplier relationships, and drive efficiency growth.
ibi solutions can be utilized together as a comprehensive solution set or separately to complement a company’s existing investments. Our modern BI approach to integrated data science allows companies to leverage their data strategies for their business needs.
Headquartered in Fort Lauderdale, Florida, ibi is a proud business unit of Cloud Software Group.
technology company specializing in software solutions
Revenue
$97M
Customers
-
Year founded
2017
Funding
$209.7M
Team size
443
Growth
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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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