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Top 50 AI & Machine Learning Operationalization (MLOps) Software Companies With $5M–$10M Revenue (August 2026)

As of August 2026, Latka tracks 438 AI & machine learning operationalization (MLOps) software companies with $5M–$10M in annual revenue. They have combined revenues of $3B and employ 25.3K people. They have raised $3.1B and serve 12M customers combined.

Every company below sells AI & machine learning operationalization (MLOps) 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 AI & Machine Learning Operationalization (MLOps) Software Companies do

AI & Machine Learning Operationalization (MLOps) software refers to a suite of tools and practices designed to facilitate the deployment, monitoring, and management of machine learning models in production environments. These solutions aim to streamline the entire machine learning lifecycle, enabling teams to transition from model development to operationalization more efficiently. MLOps software addresses critical stages such as model training, versioning, and continuous integration/continuous deployment (CI/CD), allowing organizations to rapidly iterate and improve their models over time. The primary use cases for MLOps software span various industries, including healthcare, finance, and retail, where organizations apply machine learning to enhance decision-making, forecasting, and operational efficiency. Typical features of MLOps platforms include automated workflows, data management tools, and model performance monitoring, which help ensure that deployed models operate optimally. Common buyer personas typically include data scientists, machine learning engineers, IT operations teams, and project managers focused on leveraging AI technologies to drive business outcomes.

Companies
438
Revenue
$3B
Funding
$3.1B
Employees
25.3K

Filters

Sorting: Highest -> Lowest

Filters

Top AI & Machine Learning Operationalization (MLOps) Software Companies With $5M–$10M Revenue

Showing 10 of 438 companies ranked by annual revenue.

1Introhive logo
Introhive

Chicago, Illinois, United States

relationship intelligence and sales automation platform

Revenue
$10M
Customers
50
Year founded
2012
Funding
$127.6M
Team size
235
2Next Gate Tech logo
Next Gate Tech

Luxembourg

Next Gate Tech, a Luxembourg-based FundTech, provides SaaS solutions for asset management with machine learning, data analytics, and cloud-based automation.

Revenue
$10M
Year founded
2019
Team size
44
3SuperAGI logo
SuperAGI

Palo Alto, California, United States

SuperAGI is an open-source autonomous AI agent framework designed for developers to build, manage, and run autonomous agents efficiently. It supports concurrent agent operations, integrates with various tools, and provides a graphical user interface for easy interaction. The framework is scalable, allowing for complex AI operations, and includes features like performance telemetry, agent memory storage, and optimized token usage to enhance functionality and cost management.

Revenue
$9.9M
Year founded
2023
Team size
90
4MediaMath logo
MediaMath

New York, NY, United States

We are building a new accountable & addressable supply chain for the industry. Brands and their partners use our technology and services to achieve the performance they deserve with less effort. We created the first software for real-time media buying in 2007 and today work with over one-third of the Fortune 500 and more than 3,500 brands and their agency partners to grow and deepen direct customer relationships. Our clients can access the purest supply to reach real humans at the scale they need across channels like mobile, CTV and display and can use a combination of advanced AI and human expertise to drive improved results over time.

Revenue
$9.9M
Year founded
2007
Funding
$607.5M
Team size
90
5Entera logo
Entera

New York, New York, United States

Entera is the leading 3-sided marketplace focused on connecting Enterprise and Mid-Market investors, sellers and services providers in the single-family residential market. Powered by modern data science and Artificial Intelligence (AI), Entera’s marketplace offers the most seamless solution for investors to access, evaluate, transact and operate their single-family real estate investments. Backed by leading venture capital investors Goldman Sachs Growth, Bullpen Capital, and Craft Ventures, Entera is focused on delivering software innovation and offline service solutions that enable its’ investor clients to scale their operations quickly, make more market data-driven decisions confidently, and maximize financial returns. Since its inception in 2018, Entera has transacted on more than 15,000 single family homes valued at $5 Billion across 29 US markets. The company is headquartered in New York City, New York and Houston, Texas. Learn more at www.entera.ai.

Revenue
$9.9M
Year founded
2018
Team size
90
6neptune.ai logo
neptune.ai

Palo Alto, California, United States

Neptune is the most scalable experiment tracker for teams that train foundation models. Monitor and visualize months-long model training with multiple steps and branches. Track massive amounts of data, but filter and search through it quickly. Visualize and compare thousands of metrics in seconds. And deploy Neptune on your infra from day one. Get to the next big AI breakthrough faster, using fewer resources on the way.

Revenue
$9.9M
Year founded
2017
Team size
90
7indigitall logo
indigitall

Madrid, Madrid, Spain

indigitall, founded in 2013, develops and manages a Mobile Engagement Automation (MEA) marketing platform that allows its clients and users to efficiently manage all of their digital marketing and communication campaigns from a single dashboard while constantly improving data segmentation and conversion rates through Artificial Intelligence. Our Cloud and SaaS platform is focused on customer loyalty, engagement and retention through the most advanced digital marketing tools that allow personalized communication and messages to be segmented and sent at the most precise times and through the best channel. Indigitall, fundada en 2013, es una empresa que desarrolla una plataforma de marketing MEA (Mobile Engagement Automation) que permite a los Clientes gestionar eficazmente sus campañas de marketing digital y de comunicación para mejorar la fidelización de sus usuarios y audiencia y las conversiones en cuanto a su contenidos, servicios y productos. La solución de la empresa es una plataforma SaaS enfocada en la fidelización y retención de los clientes a través de las herramientas digitales de marketing más avanzadas que permite una comunicación personalizada, segmentada en el momento más preciso y de la manera más adecuada utilizando funcionalidades de Inteligencia Artificial.

Revenue
$9.9M
Year founded
2013
Team size
90
8Slingshot Aerospace logo
Slingshot Aerospace

El Segundo, California, United States

Developer of a cloud-based data platform designed to empower businesses with data-driven machine intelligence. The company's platform offers timely, enriched data through a streaming geospatial intelligence engine that provides ready access to enriched and accurate data leads when crop are failing, energy of water usage is excessive or when economic and other intel is needed, enabling clients to take timely strategic decisions while reducing risk and increasing profitability.

Revenue
$9.8M
Year founded
2016
Funding
$18M
Team size
147
9Worth AI logo
Worth AI

Orlando, Florida, United States

Worth AI is a fintech platform that automates onboarding and underwriting for financial institutions, utilizing AI to provide real-time insights and risk management.

Revenue
$9.8M
Year founded
2023
Team size
46
10Towards AI Inc. logo
Towards AI Inc.

New York, New York, United States

This job search engine exclusively shows Machine Learning and Data Science jobs and is tailored to tag and process sector specific skills (fine-tuning, model optimization), frameworks (e.g. Pytorch, LlamaIndex) and acronyms. We update the live jobs listed from over 50,000 companies each hour. It contains a custom LLM/Retrieval Augmented Generation (RAG) model to query our full jobs database with natural language.

Revenue
$9.8M
Team size
89

Frequently asked questions about AI & Machine Learning Operationalization (MLOps) Software Companies With $5M–$10M Revenue

How many AI & machine learning operationalization (MLOps) software companies with $5M–$10M in annual revenue are there?

Latka tracks 438 AI & machine learning operationalization (MLOps) software companies with $5M–$10M in annual revenue. Together they generate $3B in annual revenue and employ 25.3K people.

Which AI & machine learning operationalization (MLOps) software company with $5M–$10M in annual revenue is the largest?

Introhive is the largest, with $10M in annual revenue, founded in 2012.

How much revenue does a typical AI & machine learning operationalization (MLOps) software company with $5M–$10M in annual revenue make?

The average AI & machine learning operationalization (MLOps) software company in this list makes $6.9M a year, across 438 companies with reported revenue. They serve 12M customers combined.

Who are the leading AI & Machine Learning Operationalization (MLOps) software vendors with $5M–$10M in annual revenue?

Ranked by annual revenue, the leaders are Introhive, Next Gate Tech, SuperAGI, MediaMath and Entera.

How much funding have AI & machine learning operationalization (MLOps) software companies with $5M–$10M in annual revenue raised?

The 438 AI & machine learning operationalization (MLOps) software companies with $5M–$10M in annual revenue tracked here have raised $3.1B in disclosed funding between them.

Related Artificial Intelligence Software categories

Inclusion Criteria

- The software must provide capabilities for model deployment and management in production environments. - It should enable monitoring and maintenance of machine learning models post-deployment. - The solution must facilitate collaboration among data scientists and operations teams through streamlined workflows. - It should support version control and rollback features for models. - Not just a data analytics tool; must also offer integrated machine learning lifecycle management functionalities.