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Top 50 Machine Learning Software Companies (September 2026)

As of September 2026, Latka tracks 3,363 machine learning software companies. They have combined revenues of $48.9B and employ 419.3K people. They have raised $65.5B 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
3,363
Revenue
$48.9B
Funding
$65.5B
Employees
419.3K

Filters

Sorting: Highest -> Lowest

Filters

Top Machine Learning Software Companies by revenue

Showing 10 of 3,363 companies ranked by annual revenue.

1Databricks logo
Databricks

San Francisco, California, United States

a cloud-based data engineering, data science, and machine learning platform

Revenue
$7B
Customers
10K
Year founded
2013
Funding
$9B
Team size
8K
Growth
45.83%
2MegazoneCloud logo
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
3LendingPoint logo
LendingPoint

Kennesaw, Georgia, United States

LendingPoint is an AI-driven consumer and small business lending platform founded in 2014 and headquartered in the United States. The company originates personal loans ranging from $5,000 to $50,000 for borrowers across the full credit spectrum, from scores of 550 to 850, using proprietary machine learning models to price risk across five credit grade buckets and more than 400 pricing points. The company grew net revenue 144% from 2020 to 2021, reaching approximately $330 million in net revenue for the full year 2021, with management guiding toward roughly $600 million for 2022. LendingPoint originated $2.1 billion in loans in 2021 and had already deployed close to $900 million in the first quarter of 2022 alone. LendingPoint raised approximately $220 million from friends and family across four to five tranches before completing its first institutional equity round in 2020, when Warburg Pincus invested $175 million with no secondary component. As of early 2022, the company reported profitability of $100 million to $120 million in net income and held debt capacity of $1 billion to $1.5 billion across warehouse lines, asset-backed securities, forward flows, and bank commitments.

Revenue
$865.3M
Customers
300K
Year founded
2014
Funding
$125M
Team size
288
4Celonis logo
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
3K
5Advance Intelligence Group logo
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
6Hopper logo
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
7Metropolis logo
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
8TELUS Digital AI Data Solutions logo
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
9Simulation AI logo
Simulation AI

London, United Kingdom

Harnessing the power of artificial intelligence to create realistic, adaptable simulations for pilot training, aircraft design testing, air traffic management scenarios, and more. Enhance safety, efficiency, and decision-making in the aviation industry with AI -powered virtual environments.

Revenue
$459.4M
10Cartrack logo
Cartrack

United States

Cartrack is a leading global Software-as-a-Service (SaaS) platform provider for small, medium and large businesses, as well as consumers needing a software platform for data analytics to optimise fleets, driver behaviour, insurance risk, safety and asset recovery. Data analytics remain Cartrack’s primary offering, in addition to growing its artificial intelligence and value-added services to deliver a tangible return on investment to its subscribers. Cartrack is also renowned for its agility and speed in developing innovative, first-to-market solutions that are aimed at further enhancing the customer experience. Cartrack’s impressive organic growth since being launched in South Africa in 2004 has resulted in an extensive footprint in 23 countries across Africa, Europe, North America, Asia Pacific, and the Middle East. With an active subscriber base in excess of 1.5 million, the Group ranks among the largest of its peer companies globally. Cartrack is a vertically integrated service-centric organisation owning all its unique intellectual property and business processes, ranging from in-house design, device and software development, mobile-technical workshops and sales. Hence, Cartrack is in full control of delivering superior service while also protecting its industry-leading margins and clean balance sheet.

Revenue
$355.6M
Year founded
2004
Team size
3.2K

Frequently asked questions about Machine Learning Software Companies

How many machine learning software companies are there?

Latka tracks 3,363 machine learning software companies with reported revenue. Together they generate $48.9B in annual revenue and employ 419.3K people.

Which machine learning software company is the largest?

Databricks is the largest, with $7B 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 $14.5M a year, across 3,363 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, MegazoneCloud, LendingPoint, Celonis and Advance Intelligence Group.

How much funding have machine learning software companies raised?

The 3,363 machine learning software companies tracked here have raised $65.5B 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