1
Databricks
San Francisco, California, United States
a cloud-based data engineering, data science, and machine learning platform
- Revenue
- $5.4B
- Customers
- 10K
- Year founded
- 2013
- Funding
- $9B
- Team size
- 8K
- Growth
- 45.95%
Top 50 Machine Learning Software Companies (August 2026)
As of August 2026, Latka tracks 3,360 machine learning software companies. They have combined revenues of $51.8B and employ 420.9K people. They have raised $68.4B and serve 2.9B customers combined.
Every company below sells machine learning software to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.
What Machine Learning Software Companies do
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.
- Companies
- 3,360
- Revenue
- $51.8B
- Funding
- $68.4B
- Employees
- 420.9K
Filters
Sorting: Highest -> Lowest
Top Machine Learning Software Companies by revenue
Showing 10 of 3,360 companies ranked by annual revenue.
2
Cursor by Anysphere
San Francisco, United States
Cursor is an AI-first Integrated Development Environment (IDE) built to streamline and accelerate software development built by Anysphere. It leverages advanced AI models like GPT-4 to provide real-time code suggestions, auto-debugging, and code generation. Cursor integrates seamlessly with Visual Studio Code (VSCode) extensions and ensures a smooth transition for developers familiar with VSCode. With its focus on pair-programming and AI assistance, Cursor enhances coding efficiency by understanding the entire codebase, fixing lint errors, and generating new code based on natural language instructions.
- Revenue
- $4B
- Customers
- 50K
- Year founded
- 2022
- Funding
- $2.7B
- Team size
- 300
- Growth
- 300%
3
MegazoneCloud
Seoul, South Korea
MegazoneCloud accelerates your journey to the AI era with comprehensive cloud, AI, and cybersecurity solutions.
- Revenue
- $1.2B
- Year founded
- 2018
- Funding
- $510M
- Team size
- 1K
- Revenue
- $865.3M
- Customers
- 300K
- Year founded
- 2014
- Funding
- $125M
- Team size
- 288
5
Celonis
Munich, Bayern, Germany
process mining platform to help businesses analyze, visualize, and optimize their processes.
- Revenue
- $771M
- Customers
- 500
- Year founded
- 2011
- Funding
- $2.4B
- Team size
- 3.7K
6
Advance Intelligence Group
Singapore
Advance Intelligence Group is an AI-driven technology company specializing in financial technology and services, founded in 2016 in Singapore. They provide AI-powered credit-enabled products and services in various sectors including financial services and retail.
- Revenue
- $770M
- Year founded
- 2016
- Funding
- $80M
- Team size
- 1.7K
7
Hopper
Montreal, Quebec, Canada
Hopper is a mobile app that leverages big data and machine learning to predict and analyze airfare
- Revenue
- $686.9M
- Year founded
- 2007
- Funding
- $175M
- Team size
- 1.2K
8
Metropolis
Los Angeles, California, United States
Metropolis is a proprietary AI- and computer vision-based operating system designed to revolutionize parking management and support the advancement of mobility capabilities.
- Revenue
- $580M
- Year founded
- 2017
- Funding
- $1.8B
- Team size
- 603
9
TELUS Digital AI Data Solutions
Las Vegas, Nevada, United States
TELUS Digital AI & Data Solutions partners with a diverse and vibrant community to help our customers enhance their AI and machine learning models. The work of our AI Community contributes to improving technology and the digital experiences of many people around the world. Our AI Community works in our proprietary AI ...
- Revenue
- $507.1M
- Year founded
- 2005
- Funding
- $1.6M
10
Tenstorrent
Toronto, Ontario, Canada
Tenstorrent is a next-generation computing company that builds computers for AI.
- Revenue
- $501.6M
- Year founded
- 2016
- Funding
- $793M
- Team size
- 1.2K
Frequently asked questions about Machine Learning Software Companies
How many machine learning software companies are there?
Latka tracks 3,360 machine learning software companies with reported revenue. Together they generate $51.8B in annual revenue and employ 420.9K people.
Which machine learning software company is the largest?
Databricks is the largest, with $5.4B in annual revenue, founded in 2013.
How much revenue does a typical machine learning software company make?
The average machine learning software company in this list makes $15.4M a year, across 3,360 companies with reported revenue. They serve 2.9B customers combined.
Who are the leading Machine Learning software vendors?
Ranked by annual revenue, the leaders are Databricks, Cursor by Anysphere, MegazoneCloud, LendingPoint and Celonis.
How much funding have machine learning software companies raised?
The 3,360 machine learning software companies tracked here have raised $68.4B in disclosed funding between them.
Related Artificial Intelligence Software categories
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