model-deployment
(6)
Automaton AI Infosystem Pvt. Ltd.
Automaton AI is betting that enterprise AI will be won in the unglamorous stretch between a promising model and a system that survives production. Its answer is ADVIT Studio - a self-hosted workspace built to keep data, training, deployment and oversight under one roof.
DataRobot
DataRobot is an enterprise AI platform company founded in 2012 that helps organizations build, deploy, govern, and monitor predictive and generative AI. A pioneer of automated machine learning (AutoML), the company has since expanded into MLOps, generative AI, and agentic AI, letting technical and business teams move models from experimentation into production with governance and observability built in. More than 1,000 organizations - including enterprises, hospitals, and government agencies - rely on its platform, now centered on an Agent Workforce Platform co-engineered with NVIDIA.
VESSL AI
VESSL AI is an MLOps and GPU-cloud company that helps AI teams train, deploy, and scale machine learning models without wrestling with infrastructure. Its platform pools GPU capacity across on-premise clusters and multiple cloud providers, automatically routing workloads to the cheapest available resources - a hybrid, multi-cloud approach the company says can cut GPU spend by up to 80%. Founded in 2020 by ex-Google and PUBG engineers and split between Seoul and the San Francisco Bay Area, VESSL serves roughly 50 enterprise customers and 2,000-plus users, and has expanded into AI-agent tooling with its open-source Hyperpocket project.
Cake
Cake is a New York-based startup that bundles the sprawling open-source AI stack into a single managed platform, so companies - especially in regulated industries like finance, insurance and healthcare - can deploy AI inside their own cloud without wiring together 100+ tools by hand. Founded in 2022 by Misha Herscu and Skyler Thomas and launched from stealth in December 2024 with a $13M seed led by Google's Gradient Ventures, Cake handles the integration, security, governance and cost-monitoring glue around open-source components like LangChain, Ray, MLflow and vector databases, all deployed inside a customer's own VPC so data never leaves.
Striveworks
Striveworks is an Austin-based AI operations company whose Chariot platform helps organizations build, deploy, monitor, and retrain machine learning models in hours rather than months. Built for highly regulated and contested environments - especially U.S. national security and allied defense - Chariot pairs a fast MLOps workflow with end-to-end data and model lineage, giving customers governance and provenance over models that must keep working when conditions change.
TrueFoundry
TrueFoundry is a San Francisco-based enterprise agentic AI platform that helps Fortune 1000 companies deploy, govern, and scale AI and machine learning applications. Founded in 2021 by former Meta engineers from IIT Kharagpur, it provides an end-to-end MLOps/LLMOps platform covering model deployment, AI gateway, model registry, prompt lifecycle management, and observability - all on the customer's own infrastructure with SOC 2, HIPAA, and GDPR compliance. With $21.3M in total funding led by Intel Capital and Peak XV, TrueFoundry's platform manages 1,000+ clusters, processes 10+ billion monthly API requests, and delivers 40-50% infrastructure cost reductions for customers including Siemens Healthineers, ResMed, NVIDIA, and Automation Anywhere.