12 companies tracked in this category. Names and one-line descriptions are free to browse — pricing model, funding, founding year, headquarters, and cited sources are in the full dataset.
| Company | Description |
|---|---|
| Anyscale | Anyscale, founded by the creators of the open-source Ray framework, provides a managed platform for scaling and productionizing distributed AI/ML and LLM workloads. |
| Baseten | An AI inference infrastructure platform for deploying, scaling, and serving machine learning models in production via dedicated GPU deployments and hosted model APIs. |
| ClearML | ClearML (formerly Allegro AI) provides an open-core MLOps/LLMOps platform covering experiment tracking, orchestration, data management, and model deployment for ML pipelines. |
| Comet ML | Comet provides a platform for experiment tracking, model production monitoring, and LLM observability to help data science and ML teams manage the model lifecycle. |
| Determined AI | Determined AI developed an open-source deep learning training platform that automates distributed training, hyperparameter tuning, and infrastructure management. |
| Domino Data Lab | Domino Data Lab offers an enterprise MLOps platform that unifies data science tooling, model training, deployment, and governance across hybrid/multi-cloud infrastructure. |
| Fireworks AI | Fireworks AI is a generative AI cloud platform that lets enterprise developer teams deploy, fine-tune, and run inference on open-source and custom AI models with fast, low-cost serverless and on-demand infrastructure. |
| Hugging Face | Hugging Face operates an open collaboration hub and toolset for hosting, sharing, and deploying machine learning models, datasets, and applications, often described as 'GitHub for AI'. |
| MosaicML | MosaicML built a platform (and the MPT model family) that let enterprises efficiently train and fine-tune large language and generative AI models on their own proprietary data. |
| Neptune.ai | Neptune.ai provides an experiment tracking and model registry tool that helps ML teams log, organize, and compare model training runs and metadata. |
| Replicate | A platform that lets developers run and deploy open-source and custom machine learning models in production via a simple cloud API. |
| Run:ai | Run:ai provides a Kubernetes-based orchestration and GPU virtualization platform that pools and dynamically allocates compute for AI training and inference workloads. |