Top 24 Machine Learning startups in USA
May 19, 2026
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24
1
Funding: $189B
OpenAI develops generative AI models: GPT for language, DALLE for images and Codex for code. The company's main product is the chatbot ChatGPT, which is used as a personal smart assistant and in enterprise customer support and knowledge management systems via APIs. The company also conducts scientific research aimed at achieving artificial general intelligence. The company's initial goal was the security and openness of artificial intelligence, but it has gradually moved toward closing and commercializing its ML and NLP technologies. According to CEO Sam Altman, AGI is achieving a certain amount of revenue. The primary OpenAI's investor is Microsoft and it has strategic partnership with NVidia. OpenAI is also a primary AI provider for the US Department of War
OpenAI develops generative AI models: GPT for language, DALLE for images and Codex for code. The company's main product is the chatbot ChatGPT, which is used as a personal smart assistant and in enterprise customer support and knowledge management systems via APIs. The company also conducts scientific research aimed at achieving artificial general intelligence. The company's initial goal was the security and openness of artificial intelligence, but it has gradually moved toward closing and commercializing its ML and NLP technologies. According to CEO Sam Altman, AGI is achieving a certain amount of revenue. The primary OpenAI's investor is Microsoft and it has strategic partnership with NVidia. OpenAI is also a primary AI provider for the US Department of War
2
Funding: $1.1B
Physical Intelligence develops general-purpose ML models for robots and other physical devices. The company collects data from robots in its lab and from real-world robots in other locations - warehouses, shops, homes - and uses this data to train universal base robot models. The goal is to create a pre-trained model (similar to GPT). The idea is that if someone creates a new hardware platform, they won't have to start collecting data from scratch—they can transfer all the knowledge the model already has. The company already works with a small number of companies in various industries - logistics, grocery stores and chocolate makers. When researchers train a new model, it comes back to stations like these for evaluation.
Physical Intelligence develops general-purpose ML models for robots and other physical devices. The company collects data from robots in its lab and from real-world robots in other locations - warehouses, shops, homes - and uses this data to train universal base robot models. The goal is to create a pre-trained model (similar to GPT). The idea is that if someone creates a new hardware platform, they won't have to start collecting data from scratch—they can transfer all the knowledge the model already has. The company already works with a small number of companies in various industries - logistics, grocery stores and chocolate makers. When researchers train a new model, it comes back to stations like these for evaluation.
3
Funding: $255M
Fundamental develops AI models to extract useful insights from the massive volumes of structured data generated by large enterprises. It combines legacy predictive AI systems with more modern tools. Unlike traditional LLMs, Fundamental's large table model, called Nexus, is deterministic - meaning it produces the same answer every time it's asked a given question, and does not use the Transformer architecture. Because Transformer-based AI models can only process data within their context window, they often struggle to analyze extremely large datasets, for example, a spreadsheet with billions of rows. However, such huge structured datasets are common in large enterprises, creating significant opportunities for models capable of handling such scale.
Fundamental develops AI models to extract useful insights from the massive volumes of structured data generated by large enterprises. It combines legacy predictive AI systems with more modern tools. Unlike traditional LLMs, Fundamental's large table model, called Nexus, is deterministic - meaning it produces the same answer every time it's asked a given question, and does not use the Transformer architecture. Because Transformer-based AI models can only process data within their context window, they often struggle to analyze extremely large datasets, for example, a spreadsheet with billions of rows. However, such huge structured datasets are common in large enterprises, creating significant opportunities for models capable of handling such scale.
4
Funding: $250M
LMArena, a startup originally launched as a research project at the Berkeley University, tests and ranks the performance of AI models. The company evaluates various models across a range of tasks, including text processing, code generation, computer vision, text-to-image conversion and other tasks. Models tested include various versions of OpenAI GPT, Google Gemini, Anthropic Claude and Grok, as well as models focused on specialized areas such as image generation, text-to-image conversion and logical reasoning. The company has also created a commercial service, AI Evaluations, through which businesses can hire the company to conduct model evaluations.
LMArena, a startup originally launched as a research project at the Berkeley University, tests and ranks the performance of AI models. The company evaluates various models across a range of tasks, including text processing, code generation, computer vision, text-to-image conversion and other tasks. Models tested include various versions of OpenAI GPT, Google Gemini, Anthropic Claude and Grok, as well as models focused on specialized areas such as image generation, text-to-image conversion and logical reasoning. The company has also created a commercial service, AI Evaluations, through which businesses can hire the company to conduct model evaluations.
5
Funding: $1.5B
Inflection AI is an artificial intelligence startup that develops personal AI chatbots.
Inflection AI is an artificial intelligence startup that develops personal AI chatbots.
6
Funding: $1.1B
DataRobot has pioneered some of the most important applied technological advancements in AI and machine learning, from inventing AutoML and Automated Time Series to trailblazing MLOps and generative AI. Its platform and solutions integrate into core business processes so teams can build, operate and govern AI at scale. Using the platform companies can develop, deliver, and govern agentic AI apps with a production-grade sandbox, library of tool accelerators, and comprehensive platform.
DataRobot has pioneered some of the most important applied technological advancements in AI and machine learning, from inventing AutoML and Automated Time Series to trailblazing MLOps and generative AI. Its platform and solutions integrate into core business processes so teams can build, operate and govern AI at scale. Using the platform companies can develop, deliver, and govern agentic AI apps with a production-grade sandbox, library of tool accelerators, and comprehensive platform.
7
Funding: $970.6M
Complete next-generation endpoint security: EDR, NGAV, anti-ransomware, fileless malware protection, and managed services powered by a proprietary AI hunting engine. Cybereason uses machine learning to increase the number of endpoints a single analyst can manage across a network of distributed resources
Complete next-generation endpoint security: EDR, NGAV, anti-ransomware, fileless malware protection, and managed services powered by a proprietary AI hunting engine. Cybereason uses machine learning to increase the number of endpoints a single analyst can manage across a network of distributed resources
8
Funding: $533.5M
Together provides a cloud platform for developing AI applications, accelerating training, fine-tuning and inference on performance-optimized GPU clusters. The cloud uses proprietary optimization technologies at the inference and training stages (ATLAS speculator system and Together Inference Engine) to improve performance and reduce overall costs and allows inference to be run with an API call. It contains a library of over 200 open-source models for chat, images, video, code and more, allowing migration from proprietary models to OpenAI-compatible APIs. The system allows to fine-tune open-source models and train your own models from the ground up, leveraging research breakthroughs such as Together Kernel Collection (TKC) for reliable and fast training.
Together provides a cloud platform for developing AI applications, accelerating training, fine-tuning and inference on performance-optimized GPU clusters. The cloud uses proprietary optimization technologies at the inference and training stages (ATLAS speculator system and Together Inference Engine) to improve performance and reduce overall costs and allows inference to be run with an API call. It contains a library of over 200 open-source models for chat, images, video, code and more, allowing migration from proprietary models to OpenAI-compatible APIs. The system allows to fine-tune open-source models and train your own models from the ground up, leveraging research breakthroughs such as Together Kernel Collection (TKC) for reliable and fast training.
9
Funding: $293.2M
Liquid AI is a developer of AI applications that help improve human experience.
Liquid AI is a developer of AI applications that help improve human experience.
10
Funding: $250M
Weights & Biases is on a mission to build the best software tools for machine learning.
Weights & Biases is on a mission to build the best software tools for machine learning.
11
Funding: $132M
OctoML develops Apache TVM machine learning compiler stack project that allows to use machine learning to optimize machine learning models so they can more efficiently run on different types of hardware.
OctoML develops Apache TVM machine learning compiler stack project that allows to use machine learning to optimize machine learning models so they can more efficiently run on different types of hardware.
12
Funding: $84.8M
The training data platform trusted by the world's most ambitious organization to develop accurate machine learning models.
The training data platform trusted by the world's most ambitious organization to develop accurate machine learning models.
13
Funding: $69.9M
Comet.ml allows data scientists to automatically track their datasets, code changes, experimentation history, and production models.
Comet.ml allows data scientists to automatically track their datasets, code changes, experimentation history, and production models.
14
Funding: $62M
Streamlit is an app framework for quickly creating and deploying data science apps
Streamlit is an app framework for quickly creating and deploying data science apps
15
Funding: $57M
Landing AI applies AI and deep learning to help manufacturers solve challenging visual inspection problems and generate business value.
Landing AI applies AI and deep learning to help manufacturers solve challenging visual inspection problems and generate business value.
16
Funding: $44M
Robust Intelligence eliminates AI Failures by detecting model vulnerabilities and automatically preventing bad outcomes.
Robust Intelligence eliminates AI Failures by detecting model vulnerabilities and automatically preventing bad outcomes.
17
Funding: $44M
Coactive AI is a machine learning platform that unlocks analytics and insights from unstructured image and video data.
Coactive AI is a machine learning platform that unlocks analytics and insights from unstructured image and video data.
18
Funding: $42.3M
Truera provides AI Quality management solutions that evaluate, optimize, and monitor machine learning models.
Truera provides AI Quality management solutions that evaluate, optimize, and monitor machine learning models.
19
Funding: $22.5M
Latent AI accelerates AI implementation and workflows for the enterprise cost-effectively anywhere on the edge continuum with Adaptive AI.
Latent AI accelerates AI implementation and workflows for the enterprise cost-effectively anywhere on the edge continuum with Adaptive AI.
20
Funding: $20.5M
Motivo enables IC design houses to unleash a wave of innovations and products by delivering a new paradigm, an AI-driven design approach that reduces by a factor of 10 the time and cost to design and ramp chips.
Motivo enables IC design houses to unleash a wave of innovations and products by delivering a new paradigm, an AI-driven design approach that reduces by a factor of 10 the time and cost to design and ramp chips.
21
Funding: $19.6M
Activeloop structures the unstructured data to seamlessly connect your computer vision data to machine learning models.
Activeloop structures the unstructured data to seamlessly connect your computer vision data to machine learning models.
23
Funding: $1.3M
Datrics democratizes the creation of self-service analytics and machine learning solutions by providing an intuitive drag-n-drop interface.
Datrics democratizes the creation of self-service analytics and machine learning solutions by providing an intuitive drag-n-drop interface.
24
AppTek combines cutting-edge artificial intelligence research with meaningful and transformative real-world applications. Our team consists of world-leading scientists with an extensive list of patents, innovations and academic publications contributing to the advancement of neural network and machine learning science and technology. Based on our scientific research, our engineering team helps convert these advancements into commercially viable real-world applications that improve the daily life in areas including accessibility, commerce, trade, and communication across languages.
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