Hugging Face

The world's largest AI model and data set hosting platform, GitHub of the AI open source community

Developer services AI model hosting AI open source community Dataset hosting

https://huggingface.co/

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Hugging Face is the world's largest AI model and data set hosting platform, with more than 1 million open source models and 700,000 public data sets, and more than 13 million daily active developers. This startup, which started in France in 2016, became the de facto standard in the NLP field with its "Transformers" library, and then gradually developed into a complete AI development ecosystem covering model sharing, training fine-tuning, inference deployment, and application hosting. It will be profitable in 2025, with a valuation of US$20 billion, but it rejected Nvidia's US$500 million investment intention and adhered to the independent route.
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond and Thomas Wolf. The initial product was a chatbot application for teenagers, but after open-sourcing the underlying model, the team found that the developer community’s demand for model sharing far exceeded expectations, and decisively pivoted to the direction of an AI platform. The company is headquartered in New York, USA, and has a main R&D center in Paris. As of 2026, Hugging Face has raised a total of US$2.295 billion in financing, with a valuation reaching US$20 billion. Investors include Salesforce Ventures, Google, Amazon, NVIDIA, AMD, Intel, IBM, Qualcomm and almost all technology giants. A Series D round in August 2023 raised $235 million at a $4.5 billion valuation, with follow-on rounds in 2024-2025 pushing the valuation even higher. It is worth noting that the company rejected a $500 million investment intention from Nvidia at the end of 2025 (the deal would have pushed the valuation to $7 billion) because it "did not want a single dominant investor to influence decision-making." Hugging Face has been profitable in 2025, but suffered losses in the first quarter of 2026 due to investment in increasing data sets. The company still has half of the financed funds on its books, and its financial position is sound.
Hugging Face’s product matrix covers the complete life cycle of AI development: Model Center (Hub) is the core product, hosting more than 1 million open source models and more than 700,000 data sets. Regardless of Llama, Qwen, Gemma or DeepSeek, Hugging Face is the first choice for almost all mainstream open source models to be released. The platform provides model cards, version management, discussion forums and collaboration functions, and is called "GitHub in the AI field" by the industry. The Transformers library is the most influential open source project of Hugging Face. It supports the three major frameworks of PyTorch, TensorFlow, and JAX, and provides a unified API for thousands of pre-trained model architectures. NLP tasks such as text classification, question answering, summarization, and translation can be completed with a few lines of Python code. This library allows researchers to quickly reproduce results and engineering teams to easily deploy models. AutoTrain provides low-code model training services. Users upload data sets, select basic models, and configure parameters to complete fine-tuning. It supports common tasks such as text classification, named entity recognition, and image classification. It is suitable for teams that do not have ML engineering experience but need to customize models. Inference Endpoints is the core commercial product of Hugging Face, providing the ability to deploy models to the production environment with one click. Supports automatic expansion and contraction, zero-downtime updates, and private link connections. GPU prices range from T4 ($0.50/hour) to H100 (quote on demand) to cover a variety of needs. Users select a model, configuration zone, and scaling strategy to complete the deployment without having to worry about Kubernetes, CUDA versions, or VPN configurations. Inference Providers is a new solution launched in 2026. It aggregates multiple inference service providers (such as Replicate) and unifies the API interface. Users can obtain inference capabilities without choosing a specific deployment solution. Spaces is a free ML application hosting platform based on Gradio or Streamlit. Developers can obtain public URL access by uploading Python applications. The free version offers CPU hosting, and the Pro version can be upgraded to GPU. The open Spaces ecosystem has spawned a large number of AI functional prototypes and teaching demonstrations.The open source tool chain also includes Datasets (dataset processing library), PEFT (parameter efficient fine-tuning), TRL (reinforcement learning training), smolagents (lightweight AI Agent framework), etc., forming a complete developer tool ecosystem. In terms of user experience, Hugging Face has a very low threshold—you can browse and use millions of models after registering an account. But for novices, choosing the right model and configuration requires a certain amount of domain knowledge, and the documentation is wide-ranging but uneven in depth, sometimes confusing novice developers.
Hugging Face adopts a "freemium" model, with about 97% of users using the free service and 3% of paying customers (mainly large enterprises) paying for additional features: Free version: Unlimited access to public models and datasets, basic inference API (rate limited), CPU version of Spaces Pro version: $9/month, provides higher API rate limits, GPU upgraded Spaces, and priority access to new features Enterprise Hub: $20/user/month, provides SSO, private repository, audit logs, EU data residency Inference Endpoints: Pay-as-you-go, GPU instances from $0.06/hour (CPU) to $4+/hour (A100/H100) AutoTrain: billed based on computing resource consumption CEO Delangue said: "We do not pursue revenue maximization, but encourage developers to provide open source alternatives for text, image and visual models." This persistence allows Hugging Face to maintain its unique independence and community trust in the wave of AI commercialization.
Hugging Face has a very high reputation in the developer community, and common positive comments focus on the following aspects: "Hugging Face turned me from a front-end engineer who knew nothing about ML to a developer who can deploy AI models. The ease of use of the Transformers library is a big contributor." - A developer who commented on Reddit "Inference Endpoints saved us a week of DevOps work. Now we spend almost all our time on R&D instead of fiddling with AWS configuration." - Andrea Boscarino, data scientist at Musixmatch "The coolest thing is the ease of defining a completely custom interface. Within a few hours we adapted the code and had a working custom endpoint." - Bryce Harlan, software engineer at Phamily "Pinecone takes just a few clicks to set up embedded endpoints that can handle 100+ QPS, something that was previously unthinkable." - Gareth Jones, senior product manager at Pinecone Negative feedback mainly focuses on: "There are too many models and I don't know how to choose. I want to find a suitable small text classification model, but I can't see the thousands of options." - A Chinese independent developer complained on Xiaohongshu "The cold start time of the free inference API is too long and has too many restrictions, making it unsuitable for real implementation. But Inference Endpoints are more expensive." - Comment on Zhihu "The documentation is comprehensive but the structure is confusing. Sometimes it takes several pages to find a specific parameter." - A British developer
Industry media and analysts generally position Hugging Face as "a key node in the AI infrastructure layer." EurpoeanStack's evaluation is representative: "The free tier is surprisingly generous - access to millions of models, the Transformers library and basic hosting are all at zero cost. The Pro version is priced at an affordable price, and the Enterprise Hub price is also competitive. The real cost increase is production inference, and this is due to the inherent cost of GPU computing, not the premium of Hugging Face." Data from July 2026 shows that there are more than 113,000 derivative models of Alibaba’s Qwen series on Hugging Face, exceeding the sum of the derivative models of Google and Meta. For the first time, Hugging Face spent its own money to provide 6 hours of global free computing power for a single model (China Wisdom’s GLM-5.2), highlighting its role as a “neutral platform” in the open source ecosystem. In the geopolitical game of AI, Hugging Face is at the center of the storm. The spring 2026 report shows that China’s open source model downloads on the platform accounted for 41% of the world’s total, surpassing the United States for the first time. The company's neutral strategy — hosting models in both China and the U.S. — has exposed it to scrutiny from both sides. Former U.S. Google CEO Eric Schmidt publicly criticized "China's AI open source is not under our control," while China is considering tightening controls on the export of open source models.
The main controversies Hugging Face faces revolve around content moderation and geopolitical neutrality. In 2023, the platform sparked controversy when it removed an AI singing model involving Chinese leader Xi Jinping. An investigation by 404 Media found that Hugging Face did not remove similar models targeting Biden during the same period, exposing inconsistent content moderation standards. Hugging Face cited the terms of the underlying project's license ("Use for political purposes is strictly prohibited") as the basis for the removal, but critics saw this as selective enforcement under government pressure. Another risk comes from the ambiguity of the definition of "open source." Hugging Face allows models with any license to be listed, including non-standard custom licenses, which puts the platform in a dilemma between content moderation and legal liability. The platform's self-executed license compliance skips the traditional copyright owner notification process, which is an unprecedented problem in Internet platform governance. China blocked Hugging Face, citing "national cyber security," a move the company said it "regrets." At the same time, China is encouraging its own open source models to go global through Hugging Face—in the spring of 2026, Chinese models accounted for more than 40% of downloads on the platform. This paradox of "the platform is banned and the model is selling well" makes Hugging Face the most subtle node in the AI competition between China and the United States. There are also hidden concerns in the business model: if more companies choose to directly use closed-source APIs (such as OpenAI’s ChatGPT or Anthropic’s Claude) in the future, bypassing the open-source model ecosystem, Hugging Face’s community activity and business conversion may be affected.
Hugging Face is suitable for almost all developers and teams related to AI. If you are an individual developer or researcher, the free version + Transformers library can meet most of your learning and experimentation needs. If you want to quickly build an AI product prototype, Spaces is the fastest choice. If your team needs to deploy models to production, Inference Endpoints can significantly reduce the operational burden. Who it’s not for: If you need complete control over model training—customizing the optimizer, fine-tuning the learning rate, or modifying the model architecture—the Transformers library’s Trainer API is flexible but has a steep learning curve. For large-scale deployments where inference cost control is extremely important, it may be more cost-effective to purchase GPUs directly from cloud service providers.
Hugging Face is not just a model hosting platform, it is the "infrastructure" of the entire AI open source ecosystem. From a niche chatbot project to an indispensable tool for developers around the world, Hugging Face is a testament to the power of the open source community. Today, when AI giants are scrambling to build walls, this company insists on being an open source neutral platform, which is both its moat and its biggest bet. The direction of open source AI depends largely on whether Hugging Face can maintain its neutral position under the pressure of Sino-US competition and commercialization.

User Reviews

林博

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Inference Endpoints has been a huge help after the company switched from OpenAI to a self-deployed model. There is no need to set up K8s by yourself. You can select a model and go online with just a few clicks. The operation and maintenance costs have plummeted. It was not so worry-free when using SageMaker in the past.

TYlew

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Spaces is awesome. It only takes a few minutes to set up a Gradio demo. It even saves deployment. It is invincible for prototype verification.

KennethSimmons_2020

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Inference Endpoints has been a huge help after the company switched from OpenAI to a self-deployed model. There is no need to set up K8s by yourself. You can select a model and go online with just a few clicks. The operation and maintenance costs have plummeted. It was not so worry-free when using SageMaker in the past.

Jason_KellyIII93

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Spaces is awesome. It only takes a few minutes to set up a Gradio demo. It even saves deployment. It is invincible for prototype verification.

Jennifer_SmithSr2

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After buying PRO, I found that several high-scoring models were still unavailable in HuggingChat, such as LLaMA 3.1 405B. It was recommended to me in the chat that PRO was available, but after paying the money, I found that it was not available at all. I sent an email for a refund and took two weeks to get back. The experience was very bad.

CarolReedII

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After buying PRO, I found that several high-scoring models were still unavailable in HuggingChat, such as LLaMA 3.1 405B. It was recommended to me in the chat that PRO was available, but after paying the money, I found that it was not available at all. I sent an email for a refund and took two weeks to get back. The experience was very bad.

realMalathyGugale

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The model center is indeed convenient, but if I find someone re-uploading someone else's model with a different name, the copyright management is too loose.

AndreaGonzales

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The model center is indeed convenient, but if I find someone re-uploading someone else's model with a different name, the copyright management is too loose.

DEgar

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After Hugging Face was blocked by China, domestic developers could only use proxy access, which greatly reduced the speed. Some large model files weighed several gigabytes a day. But on the other hand, the number of downloads of Chinese open source models on the platform has increased significantly, exceeding 40%. This situation is quite subtle.

bigbutterfly250

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The pipeline API of the Transformers library is so powerful. Three lines of code can run through sentiment analysis, and the experience of getting started with ML is excellent.

ThomasJamesQ36

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After Hugging Face was blocked by China, domestic developers could only use proxy access, which greatly reduced the speed. Some large model files weighed several gigabytes a day. But on the other hand, the number of downloads of Chinese open source models on the platform has increased significantly, exceeding 40%. This situation is quite subtle.

RichardHoward

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The pipeline API of the Transformers library is so powerful. Three lines of code can run through sentiment analysis, and the experience of getting started with ML is excellent.

Arthur.Sanders_76

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I have been using the enterprise version for half a year. The SSO and audit logs are good but the price is not cheap. What's more annoying is that if you want to change the EU region deployment, you have to talk to their sales separately, and you can't configure it directly on the console. This is very basic for European data compliance requirements.

JAdams_2020

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The community atmosphere is good, and people will respond quickly when you ask questions. It feels much friendlier than the cold-blooded replies on GitHub.

Angela_Young_66810

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I have been using the enterprise version for half a year. The SSO and audit logs are good but the price is not cheap. What's more annoying is that if you want to change the EU region deployment, you have to talk to their sales separately, and you can't configure it directly on the console. This is very basic for European data compliance requirements.

MelissaWolf

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The community atmosphere is good, and people will respond quickly when you ask questions. It feels much friendlier than the cold-blooded replies on GitHub.

Nathan705

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Many of the model cards were unclearly written. After downloading, I found out that they did not support Chinese or were just demo weights. It was difficult to pick the model.

ScottPatel

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Many of the model cards were unclearly written. After downloading, I found out that they did not support Chinese or were just demo weights. It was difficult to pick the model.

Maria.ThompsonX

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AutoTrain is really great for business students. You can upload a data set, select a base model, and click a button to complete the training. There is no need to trouble our ML team. However, the upper limit of accuracy is there. If you really want SOTA, you still have to write your own training script and adjust parameters.

evADA

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AutoTrain is really great for business students. You can upload a data set, select a base model, and click a button to complete the training. There is no need to trouble our ML team. However, the upper limit of accuracy is there. If you really want SOTA, you still have to write your own training script and adjust parameters.

TigerTradeGonzalez

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The model center is really convenient and has all kinds of models, but I found that the copyright management is too loose. Some people change other people's weights and re-upload them. This is what those Stanford students did before MiniCPM was exposed. Hopefully HF will step up model review.

BrooklynLarsen

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The model center is really convenient and has all kinds of models, but I found that the copyright management is too loose. Some people change other people's weights and re-upload them. This is what those Stanford students did before MiniCPM was exposed. Hopefully HF will step up model review.

JosephTurner

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I am looking for a small model for text classification. There are more than a thousand of them, and I don’t know how to choose one. Finally, I used distilled-bert-base recommended by others.

卢梅

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I am looking for a small model for text classification. There are more than a thousand of them, and I don’t know how to choose one. Finally, I used distilled-bert-base recommended by others.

4vd_491

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Hugging Face is indeed convenient, but it feels more and more like a gated community. Some good models have to fill out forms to apply, which is annoying.

Gloria.Russell369

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NLP is basically inseparable from the Transformers library. Three lines of code can run through sentiment analysis, which is an excellent experience for getting started. However, although the document is comprehensive in content, its organizational structure is quite messy. Sometimes I have to jump through several pages to find an explanation of a parameter. I hope it can be improved.

NLeeQ

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Hugging Face is indeed convenient, but it feels more and more like a gated community. Some good models have to fill out forms to apply, which is annoying.

jUDY399

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NLP is basically inseparable from the Transformers library. Three lines of code can run through sentiment analysis, which is an excellent experience for getting started. However, although the document is comprehensive in content, its organizational structure is quite messy. Sometimes I have to jump through several pages to find an explanation of a parameter. I hope it can be improved.

Betty_CastilloQ08

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The company uses Enterprise Hub, SSO and audit logs are done well, and the support response speed is much faster than free.

DrRolandoRegalado_2024

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The company uses Enterprise Hub, SSO and audit logs are done well, and the support response speed is much faster than free.

Hugging Face

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