Top 4,322 Data Science and Machine Learning Platforms SaaS Companies in July 2026
As of July 2026, there are 4,322 SaaS companies in Data Science and Machine Learning Platforms. They have combined revenues of $53.9B and employ 458.6K people. They have raised $66.8B and serve 3.8B customers combined.
Data Science and Machine Learning Platforms are software solutions designed to facilitate the analysis, modeling, and interpretation of complex data sets using statistical and machine learning techniques. These platforms enable data scientists to build, train, and deploy machine learning models efficiently, often featuring tools for data visualization, preprocessing, and collaboration among team members. They support various workflows, including data ingestions, model development, and performance monitoring, catering to organizations aiming to leverage data-driven insights for decision-making.
Typical use cases for these platforms include predictive analytics, customer segmentation, and operational optimization. They are utilized across various industries such as finance, healthcare, marketing, and technology. Common buyer personas include data scientists, data analysts, business intelligence professionals, and IT managers, who seek to extract actionable insights from data and improve business processes through advanced analytics and machine learning capabilities.
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Vectorize is an innovative company that provides AI-powered solutions designed to enhance the processing of unstructured data and improve the efficiency of enterprise operations. It offers tools for data ingestion, vector search, and deep research applications.
Sizolution is an AI based SaaS for fashion ecommerce.
Revenue
$1M
Customers
2M
Year founded
2015
Funding
-
Team size
1
Growth
-
Inclusion Criteria
- Must provide comprehensive tools for data preparation, analysis, and model deployment.
- Must support collaboration features for data scientists and business stakeholders.
- Must include capabilities for both supervised and unsupervised machine learning.
- Must allow for the integration of various data sources and formats.
- Not just for analysis; must also provide tools for model training and evaluation.
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