List of MosaicML Platform Customers
San Francisco, 94107, CA,
United States
Since 2010, our global team of researchers has been studying MosaicML Platform customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased MosaicML Platform for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using MosaicML Platform for ML and Data Science Platforms include: Replit, a United States based Professional Services organisation with 150 employees and revenues of $20.0 million, Hippocratic AI, a United States based Healthcare organisation with 205 employees and revenues of $16.0 million, MosaicML, a United States based Professional Services organisation with 62 employees and revenues of $7.0 million, Allen Institute For Artificial Intelligence, a United States based Professional Services organisation with 50 employees and revenues of $5.0 million and many others.
Contact us if you need a completed and verified list of companies using MosaicML Platform, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The MosaicML Platform customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
Apply Filters For Customers
| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
|---|---|---|---|---|---|---|---|---|---|---|---|
 |
Allen Institute For Artificial Intelligence | Professional Services | 50 | $5M | United States | MosaicML | MosaicML Platform | ML and Data Science Platforms | 2023 | n/a |
In 2023 Allen Institute for Artificial Intelligence implemented MosaicML Platform to support training and experimentation with large generative models in the United States. The MosaicML Platform is categorized as ML and Data Science Platforms and was applied to accelerate research workflows across AI2 research teams.
Deployment emphasized training and inference capabilities, including distributed model training, experiment tracking, model versioning, and compute orchestration consistent with ML and Data Science Platforms functionality. Configuration focused on enabling large-model training workflows, hyperparameter sweeps, dataset management, and reproducible evaluation pipelines to support iterative experimentation.
Operational scope covered AI2 research and engineering groups within the United States, where governance prioritized standardized experiment tracking and version control to improve reproducibility of model development. The MosaicML Platform usage was directed at accelerating research workflows and reducing training cost and time for large generative model experimentation.
|
 |
Hippocratic AI | Healthcare | 205 | $16M | United States | MosaicML | MosaicML Platform | ML and Data Science Platforms | 2023 | n/a |
In 2023, Hippocratic AI deployed the MosaicML Platform to train safety-focused healthcare language models in the United States, using MosaicML Platform as its ML and Data Science Platforms solution. The MosaicML Platform supported model training and inference workflows for large language models, and the program emphasized clinical applicability while preserving data control and cost efficiency.
Implementation centered on core ML workflows typical of ML and Data Science Platforms, including model training, inference-serving, fine-tuning, and experiment tracking to iterate on clinical safety behaviors. Configuration workstreams targeted pipeline orchestration and dataset versioning to support reproducible LLM development and evaluation, aligning model evaluation workflows with clinical relevance requirements.
Operational coverage was focused on Hippocratic AI engineering and clinical research teams within the United States, with deployment and data handling structured to maintain data control consistent with healthcare operational needs. No external system integrators are referenced, and no named downstream integrations were specified in source material.
Governance concentrated on access controls, model evaluation gates, and controlled rollout practices to maintain safety posture during iterative training and inference cycles. The initiative aimed to improve clinical applicability while maintaining organizational data control and cost efficiency, with implementation choices reflecting the priorities of a healthcare-focused ML program.
|
|
|
MosaicML | Professional Services | 62 | $7M | United States | MosaicML | MosaicML Platform | ML and Data Science Platforms | 2024 | n/a |
In 2024, MosaicML implemented MosaicML Platform, an ML and Data Science Platforms application to centralize model development and operational workflows for its research and professional services functions. The deployment targeted internal engineering and data science teams, providing experiment management, reproducible training pipelines, and centralized model lifecycle tracking across the organization.
Configuration of the MosaicML Platform emphasized core ML and Data Science Platforms capabilities including model training orchestration, distributed training management, experiment tracking, dataset versioning, a model registry, and automated pipeline orchestration for continuous training and deployment. The implementation included compute orchestration and job scheduling, centralized metadata management for reproducibility, and API first access to support in-house tooling and automation.
Governance and operational controls were scoped to enforce role based access controls, model version governance, audit logging, and environment separation between development and production. Rollout followed a staged adoption approach starting with core engineering users and expanding to professional services teams, accompanied by updated model review and deployment approval processes.
|
 |
|
Professional Services | 150 | $20M | United States | MosaicML | MosaicML Platform | ML and Data Science Platforms | 2023 | n/a |
|
Buyer Intent: Companies Evaluating MosaicML Platform
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
|---|---|---|---|---|---|---|---|---|
| No data found | ||||||||