List of Hugging Face Customers
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Since 2010, our global team of researchers has been studying Hugging Face 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 Hugging Face 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 Hugging Face for ML and Data Science Platforms include: databricks, a United States based Professional Services organisation with 5500 employees and revenues of $1.60 billion, Fetch Rewards, a United States based Professional Services organisation with 550 employees and revenues of $20.0 million, Richard James Rogers, a Thailand based Education organisation with 10 employees and revenues of $2.0 million, Ryght, a United States based Life Sciences organisation with 20 employees and revenues of $1.0 million, LeafMachine2, a United States based Life Sciences organisation with 10 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using Hugging Face, 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 Hugging Face 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!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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AlbumForge France | Retail | 10 | $1M | France | Hugging Face | Hugging Face | ML and Data Science Platforms | 2025 | n/a |
In 2025, AlbumForge France implemented Hugging Face as its ML and Data Science Platforms capability to embed model inference directly into its customer-facing website. The deployment leverages Hugging Face model hosting and inference APIs to enable personalization, automated content generation workflows, and image-related processing tied to product configuration. Hugging Face is the application layer for model serving, version management, and inference orchestration within the ML and Data Science Platforms category for the company website.
The architecture uses cloud-managed Hugging Face endpoints invoked by the website frontend and a lightweight backend service, with model artifacts and versions maintained in the Hugging Face environment. Functional modules include model serving, inference orchestration, and monitoring workflows consistent with ML and Data Science Platforms deployments. Operational scope covers AlbumForge France product and engineering functions, with a small cross-functional team responsible for integration, model updates, staged rollouts, and governance through model version control and configuration management.
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databricks | Professional Services | 5500 | $1.6B | United States | Hugging Face | Hugging Face | ML and Data Science Platforms | 2023 | n/a |
In 2023, Databricks integrated Hugging Face to link Spark and Databricks dataflows with Hugging Face Datasets and model training tooling, operating within its ML and Data Science Platforms environment. The effort targeted the data engineering and ML platform process areas and was executed on a global scale with US led coordination.
The implementation configured end to end dataset ingestion and preparation pipelines that push Spark transformed data into Hugging Face Datasets, and orchestrated model fine tuning using Hugging Face model training tooling. Functional capabilities implemented included automated dataset ingestion, preprocessing in Databricks notebooks, dataset versioning using Hugging Face Datasets, and orchestration of training and tuning jobs from the Databricks environment.
Integrations connected Databricks Spark jobs and notebooks to Hugging Face tooling through pipeline orchestration, supporting handoff from data engineering teams to ML platform and ML engineering teams. Operational coverage spanned global data engineering and ML platform functions, aligning dataset ingestion and model training workflows to speed training and tuning.
Governance adjustments established joint ownership between data engineering and ML platform teams and formalized orchestration and release workflows for datasets and models. Hugging Face and Databricks improvements cut a 16GB dataset pipeline time by approximately 40 percent, reducing dataset load and preparation time and accelerating downstream training and tuning workflows.
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Fetch Rewards | Professional Services | 550 | $20M | United States | Hugging Face | Hugging Face | ML and Data Science Platforms | 2022 | n/a |
In 2022, Fetch Rewards implemented Hugging Face within the ML and Data Science Platforms category to rebuild its receipt document AI pipeline. Hugging Face supported Fetch Rewards through the AWS Expert Acceleration partner program on a project focused on receipt parsing and data ingestion across the United States.
The engagement centered on training in‑house transformer models for receipt parsing and analytics and on rearchitecting the document AI ingestion pipeline. Implementation work included standardized model fine tuning and evaluation workflows, model training orchestration, and production inference patterns to support high throughput document parsing.
Integration work leveraged Hugging Face model tooling alongside AWS support to operationalize model training and serving, enabling Fetch Rewards to process at production scale. Operational coverage spanned Fetch Rewards data engineering and machine learning teams, with the rebuilt pipeline instrumented for large scale ingestion and analytics.
The project delivered measurable improvements, cutting model development time by approximately 30 percent and reducing processing latency by approximately 50 percent, enabling production scale processing of millions of receipts per day. Hugging Face served as the application under the ML and Data Science Platforms category that enabled these document AI and data ingestion capabilities.
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Life Sciences | 10 | $1M | United States | Hugging Face | Hugging Face | ML and Data Science Platforms | 2023 | n/a |
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Education | 10 | $2M | Thailand | Hugging Face | Hugging Face | ML and Data Science Platforms | 2023 | n/a |
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Life Sciences | 20 | $1M | United States | Hugging Face | Hugging Face | ML and Data Science Platforms | 2023 | n/a |
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Buyer Intent: Companies Evaluating Hugging Face
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