Sold by: Hugging Face
Deployed on AWS
The Hugging Face Hub is the leading open platform for AI builders.
4.1
Overview
The Hugging Face Hub is the leading open platform for AI builders, with over 1 million models, datasets, and AI applications for building AI features that process and generate text, images, audio, video, and more. Subscribe to the Hugging Face Hub to give your team access to all its premium features:
- Inference Endpoints to deploy models for production,
- Spaces to create AI applications easily,
- Enterprise Hub to give your company additional security, access controls, and compute features, including Single Sign-On, Resource Groups, Audit Logs, Storage Regions, Train on DGX Cloud, and more (https://www.youtube.com/watch?v=CPQGBn-yXJQ )
To use the Hugging Face Hub and get billed with your AWS Account, follow the steps in our tutorial (https://huggingface.co/blog/enterprise-hub-aws-marketplace ).
By subscribing to the Hugging Face Hub, you will be billed for what you use as paid features. For instance, if you consume $30 worth of inference endpoints, $5 worth of Spaces, and 7 seats for an Enterprise Hub license (one month), we would bill your AWS account with 30x1000 + 5x1000 + 7x20x1000 Hugging Face Billing Units ($0.001 per unit).
Inference Endpoint and Spaces Upgrade use a usage-based, pay-as-you-go pricing: https://huggingface.co/pricing . Enterprise Hub uses seat-based pricing ($20 per seat per month)
Highlights
- Inference Endpoints: Deploy any model as a secure, production-ready API for fast inference.
- Spaces: Build and host any ML application on huggingface.co, batteries and GPUs included
- Enterprise Hub: advanced security, access controls, collaboration and compute for companies
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Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
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Dimension | Cost/unit |
|---|---|
Hugging Face Billing Unit | $0.001 |
Hugging Face Usage Fee | $0.01 |
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No refunds
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SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
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AI generated from product descriptions
Model Deployment Infrastructure
Inference Endpoints enable deployment of models as secure, production-ready APIs with fast inference capabilities
Application Hosting Platform
Spaces provides hosting for machine learning applications with integrated GPU resources and pre-configured dependencies
Enterprise Security and Access Management
Enterprise Hub includes Single Sign-On, Resource Groups, Audit Logs, and Storage Regions for advanced security and access controls
Model and Dataset Repository
Access to over 1 million pre-trained models, datasets, and AI applications for text, image, audio, and video processing
Multi-Node Distributed Training
Supports multi-node training capabilities enabling scalable AI model training across multiple machines with on-demand compute resources including A100 and H100 GPUs.
Integrated Development Environment
Provides unified platform integrating data preparation, model development, distributed training, and application deployment within a single cohesive interface.
Pre-built Model Templates
Includes pre-built studios from expert contributors and PyTorch ecosystem optimized for state-of-the-art AI applications including LLMs, Diffusion models, and Graph Neural Networks.
Enterprise Security and Isolation
Offers enterprise-grade security features including Bring Your Own Cloud (BYOC) capability, fine-grained access control, and private networking to ensure data remains within customer accounts.
Serverless Deployment
Supports serverless deployment options enabling application deployment without infrastructure management overhead.
Model Integration and Management
Access to 1,000+ models including AWS Bedrock and SageMaker with centralized management and side-by-side performance comparison capabilities.
Agentic Workflows and RAG Pipelines
Design advanced agentic workflows with multi-step logic, context-aware agents, and cross-modal integration (LLM, TTS, STT) combined with built-in RAG pipelines for data extraction, transformation, and indexing.
AI Application Orchestration
Seamless integration of AI workflow orchestration, agent capabilities, model management, and observability through an intuitive no-code/low-code interface.
Application Distribution and Deployment
Publish AI applications as WebApps, embed into websites, or integrate via API with custom branding options for professional user experience.
Performance Monitoring and Optimization
LLMOps tools for performance monitoring, metrics analysis, experimentation, and ongoing optimization of deployed AI applications.
Standard contract
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Customer reviews
Mihir Jadhav
Integration of open-source models and deployment in cloud apps has drastically improved productivity
Reviewed on Nov 20, 2025
Review from a verified AWS customer
What is our primary use case?
My main use case for Hugging Face is to download open-source models and train on a local machine. We use Hugging Face Transformers for simple and fast integration in our applications and AI-based applications. We also use Hugging Face Spaces to deploy and test applications.
A specific example of how I have used Hugging Face in one of my projects is that we have downloaded Llama models, and currently, there is a model from AI for Bharat, which is a voice model. They have deployed the model on Hugging Face to test, and we have also downloaded the model from Hugging Face and used it locally for customer support and voice agents. I have covered everything from models to Hugging Face Spaces and Transformers as well, including the single-click deployment that it offers on SageMaker .
What is most valuable?
The best features Hugging Face offers are Transformers and Spaces to deploy the app in clicks.
What I like most about Transformers and Spaces is the ease of use. Hugging Face is like a Git repository, so it is very helpful and easy to use.
Hugging Face has positively impacted my organization because we can deploy open-source applications for testing on Spaces, and one of the main things is the models that it provides and the number of open-source models to compare with. The main part is that it offers inference as well for free for many of the models, so we can use it directly in our applications with a few lines of code.
What needs improvement?
Everything is pretty much sorted in Hugging Face, but it could be improved if there was an AI chatbot or an AI assistant in Hugging Face platform itself, which can guide you through the whole platform, making it easier for the user.
For how long have I used the solution?
I have been using Hugging Face for three years, and for the past two years, I have been using it on a daily basis for work-related activities, using Hugging Face Spaces, models, and many more features.
What do I think about the stability of the solution?
Hugging Face is pretty stable, and we have not seen any downtime.
What do I think about the scalability of the solution?
Scalability of Hugging Face is good, and there are no limitations.
How are customer service and support?
We have not had a chance to interact with customer support, but I feel it would be very good.
How would you rate customer service and support?
Neutral
Which solution did I use previously and why did I switch?
We actually started with Hugging Face itself and did not use a different solution.
What was our ROI?
We have seen a return on investment, as the number of employees has been reduced because most of the things are getting automated using Hugging Face and AWS .
Which other solutions did I evaluate?
Before choosing Hugging Face, we have not actually evaluated other options; we found out it was good, so we are using it.
What other advice do I have?
We have seen improved productivity and time saved from using Hugging Face; for a task that would have taken six hours, it saved us five hours, and we completed it in one hour with the plug-and-play integration of inference and everything, using the few lines of code that Hugging Face provides.
I would recommend Hugging Face to others looking into using it because it is easy to integrate and plug-and-play.
My company does not have a business relationship with this vendor other than being a customer.
Hugging Face is good overall, and I give this review a rating of eight out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
KhasimMirza
Extensive documentation and diverse models support AI-driven projects
Reviewed on Apr 18, 2025
Review provided by PeerSpot
What is our primary use case?
I am working on AI with various large language models for different purposes such as medicine and law, where they are fine-tuned with specific requirements. I download LLMs from Hugging Face for these environments. I use it to support AI-driven projects and deploy AI applications for local use, focusing on local LLMs with real-world applications.
What is most valuable?
Hugging Face is valuable because it provides a single, comprehensive repository with thorough documentation and extensive datasets. It hosts nearly 400,000 open-source LLMs that cover a wide variety of tasks, including text classification, token classification, text generation, and more. It serves as a foundational platform offering updated resources, making it essential in the AI community.
What needs improvement?
It is challenging to suggest specific improvements for Hugging Face, as their platform is already very well-organized and efficient. However, they could focus on cleaning up outdated models if they seem unnecessary and continue organizing more LLMs.
For how long have I used the solution?
I have been working with Hugging Face for about one and a half years.
What do I think about the stability of the solution?
Hugging Face is stable, provided the environment is controlled, and the user base is limited. The stability relies on the specific models and the data they're fed, which minimizes issues like hallucination.
What do I think about the scalability of the solution?
Hugging Face is quite scalable, especially in terms of upgrading models for better performance. There is flexibility in using models of varying sizes while keeping the application environment consistent.
How are customer service and support?
I have not needed to communicate with Hugging Face's technical support because they have extensive documentation available.
How would you rate customer service and support?
Neutral
Which solution did I use previously and why did I switch?
Before Hugging Face, I used Ollama due to its ease of use, but Hugging Face offers a wider range of models.
How was the initial setup?
The initial setup can be rated as a seven out of ten due to occasional issues during model deployment, which might require adjustments. Recent developments have made the process easier though.
What's my experience with pricing, setup cost, and licensing?
The pricing is reasonable. I use a pro account, which costs about $9 a month. This positions it in the middle of the cost scale.
Which other solutions did I evaluate?
Before choosing Hugging Face, I used Ollama for its ease of use, but it lacked the variety offered by Hugging Face.
What other advice do I have?
Overall, the platform is excellent. For any AI enthusiast, Hugging Face provides a broad array of open-source models and a solid foundation for building AI applications. Using an on-premises model helps manage errors in critical environments. I rate Hugging Face as an eight out of ten.
SwaminathanSubramanian
Versatility empowers AI concept development despite the multi-GPU challenge
Reviewed on Feb 05, 2025
Review provided by PeerSpot
What is our primary use case?
I have been using Hugging Face for proof of concepts (POC) and a generative AI project. Currently, I'm trying to use it with Tala and Olaama, along with some other AI tools as I build up my knowledge of AI and generative AI.
What is most valuable?
I like that Hugging Face is versatile in the way it has been developed. I appreciate the versatility and the fact that it has generalized many models. I'm exploring other solutions as well, however, I find Hugging Face very user-friendly.
I am still building my knowledge of it. From my perspective, it's very easy to use, and as you ramp up, you discover new aspects about it.
What needs improvement?
Regarding scalability, I'm finding the multi-GPU aspect of it challenging. Training the model is another hurdle, although I'm only getting into that aspect currently. Organizations are apprehensive about investing in multi-GPU setups.
Additionally, data cleanup is a challenge that needs to be resolved, as data must be mature and pristine.
For how long have I used the solution?
I have been using it for a total of around six months.
What do I think about the stability of the solution?
I have not really faced any stability issues, however, the scale has been small. I'm unsure how it would perform on a larger scale.
What do I think about the scalability of the solution?
I have not had production-type deployments for a client yet. Organizations are not mature enough to invest significantly in multi-GPU setups, which presents a scalability challenge. Also, organizations are apprehensive about the multi-GPU route.
How are customer service and support?
I have not contacted their support team yet.
How would you rate customer service and support?
Neutral
What's my experience with pricing, setup cost, and licensing?
I am just a user at this point and do not have information about their pricing.
Which other solutions did I evaluate?
I'm exploring Langchain and Agentic AI as part of my current learning and development.
What other advice do I have?
Joining the Hugging Face community can provide additional support. It allows for collaboration on models and datasets, offering quick insights on how the community is using it.
I rate the solution a seven out of ten.
Melek Ghouma
Accessible inference APIs drive personal project success for students
Reviewed on Jan 24, 2025
Review provided by PeerSpot
What is our primary use case?
This is a simple personal project, non-commercial. As a student, that's all I do.
What is most valuable?
The most valuable features are the inference APIs as it takes me a long time to run inferences on my local machine.
What needs improvement?
Access to the models and datasets could be improved. Many interesting ones are restricted. It would be great if they provided access for students or non-professionals who just want to test things.
For how long have I used the solution?
I have been using this solution for about the last three or four months.
Which solution did I use previously and why did I switch?
I have used just TensorFlow and PyTorch . Nothing else.
What's my experience with pricing, setup cost, and licensing?
What other advice do I have?
I've been trying to implement some chatbots, and having free access to Hugging Face helped me a lot.
I use PyTorch and TensorFlow to implement other deep-learning models and access LLMs. Each one of these tools has its own purpose. Python is used for deep learning projects to train and fine-tune models at the deep learning level, while for Hugging Face, it's mainly for the transformers library and LLM APIs. I cannot compare them directly. For me, it's about access to datasets and models.
I would rate this product nine out of ten.
Vikas_Gupta
Easy to use, but initial configuration can be a bit challenging
Reviewed on Sep 04, 2024
Review provided by PeerSpot
What is our primary use case?
We use the tool to extract data from a PDF file, give the text data to any Hugging Face model like Meta or Llama, and get the results from those models according to the prompt. It's basically like having a chat with the PDF file.
What is most valuable?
The solution is easy to use compared to other frameworks like PyTorch and TensorFlow.
What needs improvement?
Initially, I faced issues with the solution's configuration.
For how long have I used the solution?
I have been using Hugging Face for almost two years.
What do I think about the stability of the solution?
Hugging Face is a stable solution.
What do I think about the scalability of the solution?
Hugging Face is a scalable solution.
What other advice do I have?
To use Hugging Face, you need to have basic knowledge of how to feed the data, how to speed data, how to train the model, and how to evaluate the model. Compared to other frameworks like PyTorch and TensorFlow, I'm more comfortable with using Hugging Face. I would recommend the solution to other users.
Overall, I rate the solution seven and a half out of ten.