AWS teams up with Hugging Face to counter ChatGPT boom

While Microsoft went all-in on OpenAI and Google poured millions into Anthropic, Amazon is taking a completely different route in the AI arms race. Instead of betting the house on a single proprietary lab, AWS is teaming up with the biggest open-source hub on the internet.

AWS and Hugging Face just announced an expanded partnership, making AWS the preferred cloud provider for the open-source AI platform. Hugging Face is essentially the GitHub of machine learning, currently hosting more than 100,000 models and pulling in over a million downloads a month.

Custom chips meet open-source models

The technical meat of the deal comes down to deployment and hardware. Developers will soon be able to deploy Hugging Face models straight into Amazon SageMaker with just a few clicks, cutting out days of messy infrastructure setup.

Even more interesting is the hardware play. Hugging Face plans to train its next-generation Bloom language model using AWS-designed Trainium chips. For running inference in production, models will tap into AWS Inferentia accelerators. That is a noticeable departure for Bloom, which had its previous iteration trained on the French public supercomputer Jean Zay.

The non-exclusive play

Unlike Microsoft reportedly pumping $10 billion into OpenAI or Google investing nearly $400 million into Anthropic, Amazon did not invest any equity into Hugging Face. The deal is also explicitly non-exclusive, meaning Hugging Face can still partner with whichever cloud provider it wants.

This reflects an intentional strategy. AWS already partners with Stability AI and AI21 Labs. As AWS VP Swami Sivasubramanian put it, the future should not be about one single model ruling the world. With AWS already counting over 100,000 AI customers and sharing roughly 1,000 clients with Hugging Face, Amazon is angling to be the universal computing layer rather than an exclusive publisher.

Why it matters

For engineers and startups, this removes massive operational headaches. Spinning up and fine-tuning open-source models usually requires significant DevOps muscle. Tying Hugging Face models directly to SageMaker and specialized custom silicon makes testing and deploying production workloads far more accessible and cost-effective. If your team is running open-source models, testing AWS custom instances is worth a look right now.

For the broader industry, this establishes a clear alternative to the Microsoft-OpenAI vertical silo. AWS is positioning itself as the Switzerland of AI infrastructure, betting that enterprise customers prefer choice over a single closed ecosystem.

We think Amazon's strategy is undeniably pragmatic. Selling pickaxes to the entire open-source world carries much less risk than picking a single frontrunner. However, there is an obvious vulnerability: because the partnership is not exclusive, AWS does not lock down proprietary model IP. If closed-source proprietary models outpace open-source alternatives by too wide a margin, simply offering compute for open models might not be enough to keep rivals at bay.