- Revenue
- $1M
- Customers
- -
- Year founded
- 2023
- Funding
- -
- Team size
- 5
- Growth
- -
Top 4,321 Data Science and Machine Learning Platforms SaaS Companies in May 2026
As of May 2026, there are 4,321 SaaS companies in Data Science and Machine Learning Platforms. They have combined revenues of $52B and employ 466.7K people. They have raised $55.6B 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.
Filters
Sorting: Highest -> Lowest
Top Data Science and Machine Learning Platforms Companies
Showing 10 of 1,666 companies ranked by annual revenue.
- Revenue
- $1M
- Customers
- -
- Year founded
- 2023
- Funding
- -
- Team size
- 5
- Growth
- -
- Revenue
- $1M
- Customers
- -
- Year founded
- 2022
- Funding
- -
- Team size
- 7
- Growth
- -
4
Socital
London, England, United Kingdom
Socital is a SaaS tool for onsite campaigns, specialized to ecommerce. We help online retailers to collect more rich data, engage more with their customers and turn unknown visitors to paying clients. Socital's A.I. processes social media data for all your visitors. Our smart and fully social engagement tools, combined with the latest data science and seamless integration across the marketing ecosystem, give digital marketers insights into target audiences and enhances their ability to deliver effective personalized experiences across paid and owned media. Learn more or request a demo at www.socital.com
- Revenue
- $1M
- Customers
- -
- Year founded
- 2016
- Funding
- -
- Team size
- 8
- Growth
- 21.76%
- Revenue
- $1M
- Customers
- 50
- Year founded
- 2021
- Funding
- -
- Team size
- 22
- Growth
- 38.89%
- Revenue
- $1M
- Customers
- -
- Year founded
- 2022
- Funding
- -
- Team size
- 6
- Growth
- -
- Revenue
- $1M
- Customers
- 2M
- Year founded
- 2015
- Funding
- -
- Team size
- 1
- Growth
- 45.7%
- Revenue
- $1M
- Customers
- -
- Year founded
- 2023
- Funding
- -
- Team size
- 5
- Growth
- -
9
Vectorize
Boulder, Colorado, United States
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.
- Revenue
- $1M
- Customers
- -
- Year founded
- 2023
- Funding
- -
- Team size
- 7
- Growth
- -
10
Lead Onion
Portstewart, Northern Ireland, United Kingdom
Find in-market accounts at every buyer journey stage. By capturing buyer research signals across 20 distinct sources, Lead Onion gives you market-leading coverage and intelligence on your target audience. With built-in AI and sales tools, it ensures your outreach succeeds by connecting you with companies precisely when they need you.
- Revenue
- $1M
- Customers
- -
- Year founded
- 2020
- Funding
- -
- Team size
- 11
- 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.