List of Hugging Face Platform Customers
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Since 2010, our global team of researchers has been studying Hugging Face 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 Hugging Face Platform for AI infrastructure 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 Platform for AI infrastructure include: databricks, a United States based Professional Services organisation with 5500 employees and revenues of $1.60 billion, Banque Des Territoires France, a France based Banking and Financial Services organisation with 1500 employees and revenues of $1.00 billion, Capital Fund Management France, a France based Banking and Financial Services organisation with 300 employees and revenues of $200.0 million and many others.
Contact us if you need a completed and verified list of companies using Hugging Face 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.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Banque Des Territoires France | Banking and Financial Services | 1500 | $1.0B | France | Hugging Face | Hugging Face Platform | AI infrastructure | 2024 | Telekom Healthcare Solutions Germany |
In 2024, Banque Des Territoires implemented the Hugging Face Platform as AI infrastructure to pilot a sovereign Retrieval Augmented Generation RAG assistant supporting the EduRénov school renovation program. The pilot started in January 2024 and focused on program management and customer support workflows to improve responsiveness to local authorities.
The implementation used the Hugging Face Platform with Text Generation Inference as the core inference engine, configured to operate in French for public sector interactions. Architecturally the solution centers on a RAG pattern, combining a retrieval layer that surfaces EduRénov program documents and guidance with text generation to produce contextual responses for local authority queries.
Deployment and operational controls were implemented on French cloud providers to guarantee data sovereignty, and the project was executed with system integrator Polyconseil alongside Telekom Healthcare Solutions Germany. Integrations emphasize secure hosting and retrieval pipelines tied to program documentation and case management workflows, aligning the Hugging Face Platform AI infrastructure with existing public sector program processes.
Governance measures prioritize sovereign data residency and operational oversight for a public sector use case, with the pilot scoped to validate responsiveness and support procedures for local authorities. The program is positioned as a customer support and program management assistant, and Banque Des Territoires reports improved responsiveness to local authorities as an explicit outcome.
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Capital Fund Management France | Banking and Financial Services | 300 | $200M | France | Hugging Face | Hugging Face Platform | AI infrastructure | 2024 | n/a |
In 2024, Capital Fund Management France deployed the Hugging Face Platform as its AI infrastructure to support quantitative research and Named Entity Recognition on financial news. The engagement prioritized production inference and annotation workflows to improve entity extraction accuracy for finance oriented content.
Implementation used Hugging Face Inference Endpoints and Expert Support to deploy Llama models and to operationalize LLM assisted labeling for annotations. The Hugging Face Platform provided model serving and inference orchestration while Expert Support guided model calibration and labeling workflow design. Functional capabilities implemented included model deployment, continuous inference, and human in the loop labeling to accelerate NER model development.
Operational scope centered on quantitative research teams in France, integrating LLM assisted labeling into existing annotation pipelines and reviewer workflows. Governance adjustments introduced labeling quality checks and iterative retraining cycles to refine entity schemas and model outputs across research projects.
Outcomes published in a 2024 case study reported NER performance gains up to 6.4% accuracy and inference cost reductions cited as up to approximately 80x cost efficiency versus large LLMs. The case study links these results to deploying Llama models on Hugging Face Inference Endpoints combined with targeted labeling and Expert Support.
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databricks | Professional Services | 5500 | $1.6B | United States | Hugging Face | Hugging Face Platform | AI infrastructure | 2023 | n/a |
In 2023, Databricks integrated the Hugging Face Platform into its AI infrastructure to accelerate data engineering and model training workflows. The deployment centered on embedding Hugging Face Platform capabilities directly into Databricks Spark runtimes, providing first class Spark support and a direct Dataset.from_spark path that feeds Spark data into model training and hyperparameter tuning pipelines.
The implementation emphasized data preparation and ML infrastructure modules, enabling data engineers to convert Spark DataFrames into Hugging Face Datasets for training and tuning, and to orchestrate training pipelines within Databricks notebooks and jobs. Functional capabilities implemented include the Dataset.from_spark ingestion path, streamlined data serialization for model input, and integration points for training and tuning workflows consistent with standard ML infrastructure patterns.
Operational scope covered Databricks global and US data engineering and ML teams, with the use case focused on speeding training pipelines and improving cost and time efficiency. Integration work tied the Hugging Face Platform to Databricks runtime and Spark processing, aligning ingestion and training orchestration between engineering and ML functions.
Governance and rollout followed documented patterns in the 2023 Hugging Face case post, with process changes to pipeline authorship and data preparation steps to leverage the Dataset.from_spark path. The documented outcome from the integration included cutting data preparation time for model training and tuning by up to ~40 percent, supporting faster iteration in model development and more efficient ML infrastructure operations.
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Buyer Intent: Companies Evaluating Hugging Face Platform
- Serco North America (holdings), a United States based Professional Services organization with 20000 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
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