Arcee AI has shifted its entire catalog of public models, private models, proprietary datasets and agent traces to Hugging Face as the exclusive storage and distribution platform, marking the first full migration of this scale by a major American AI lab away from AWS S3 under a multi-million-dollar commercial agreement.
Super excited to announce that @arcee_ai is the first major American AI lab to replace AWS S3 with Hugging Face for ALL their models and datasets, public AND private 🔥🔥🔥
Multi-million $ partnership to support American open-source AI, let’s go!
Enterprise customers now access a single supply chain on the Hugging Face Hub for both open and confidential artifacts, with private storage handled through Hugging Face Buckets that bundle per-TB pricing, egress and CDN; public releases continue alongside planned co-developed drops.
The exact dollar value, payment structure, data volume moved and migration completion timeline are not specified, leaving the full scope of the transition and any per-TB rates open for later clarification.
Many users praised Arcee AI's switch from AWS S3 to Hugging Face as a landmark step for open-source independence and cost efficiency.
Today we're announcing a multi-million-dollar strategic partnership with @huggingface.
The Hub is now the exclusive home for everything we build: every open model we release, every private run, every proprietary dataset, and every agent trace. All of it lives there.
HF has been an amazing partner since day one, so this was easy. As we’ve grown from a post-training shop into a full model lab, @huggingface is the obvious partner to scale the infra open models demand.
We’re all in, and we’re not slowing down. Get ready.
Super excited to announce that @arcee_ai is the first major American AI lab to replace AWS S3 with Hugging Face for ALL their models and datasets, public AND private 🔥🔥🔥
Multi-million $ partnership to support American open-source AI, let’s go!
🤝 @ClementDelangue 🤝 @MarkMcQuade
First major American AI lab to replace AWS S3 with Hugging Face 🔥 @arcee_ai is putting their entire catalog on the Hub — not just open releases, but private models & proprietary datasets too. Multi-million $ partnership. Buckets is now their research and production storage layer. Let's go 🚀
Super happy to work with @MarkMcQuade @latkins and team! More in 🧵
If people only knew how much OpenMed runs on HF Stack like Buckets, Datasets, and Spaces,
From datasets, to agent traces, to medical intelligent MCPs, to binary builds for OpenMed Agent, @huggingface has quietly become one of the easiest and best cloud platforms for AI builders.
Super excited to announce that @arcee_ai is the first major American AI lab to replace AWS S3 with Hugging Face for ALL their models and datasets, public AND private 🔥🔥🔥
Multi-million $ partnership to support American open-source AI, let’s go!
rc <3 hf
Today we're announcing a multi-million-dollar strategic partnership with @huggingface.
The Hub is now the exclusive home for everything we build: every open model we release, every private run, every proprietary dataset, and every agent trace. All of it lives there.
@arcee_ai @code_star @huggingface waow care to spare a million for a fella building stuff?
More details here: https://huggingface.co/blog/clem/arcee-hf
Super excited to announce that @arcee_ai is the first major American AI lab to replace AWS S3 with Hugging Face for ALL their models and datasets, public AND private 🔥🔥🔥
Multi-million $ partnership to support American open-source AI, let’s go!
With 14 researchers and about 30 people total across the company - Arcee is a lean operation.
To consistently ship world-class work at our size, infrastructure has to be invisible. The moment our team is spending energy on storage architecture and cost instead of model design, we’ve already lost.
Hugging Face Buckets solves this.
It gives us optimized, per-TB storage with egress and CDN included. It makes us completely compute agnostic. We can train wherever cluster capacity is cheapest and best, spin the compute down, and our data is right there waiting.
@ClementDelangue @arcee_ai let's go!
@raulizahi @arcee_ai @code_star @huggingface sure
we are not VC funded, only have funding from angels but gcp/aws/azure will only get you a reasonable amount of credits if u come with an accelerator
Read the full partnership announcement on the Arcee blog: https://www.arcee.ai/blog/why-we-made-hugging-face-the-home-for-everything-we-build
This builds on a foundation we've been laying for over two years.
With 200+ models and millions of downloads, Arcee is already one of the most active American labs on the Hub. Making it our official home allows us to meet 15 million ML engineers exactly where they already build.
We punch above our weight because we are ruthless about where we spend our focus.
This partnership removes an entire category of operational overhead. Every ounce of energy we get back goes straight into data quality, training runs, and product experience.
Follow arcee-ai on the Hub and stay tuned, we can’t wait to show you our next generation of open models when they’re ready.
@tekbog @arcee_ai @huggingface Couldn’t help myself.
@julien_c @ClementDelangue @MarkMcQuade wait that’s one too many handshakes
@ClementDelangue @arcee_ai wow exciting! Do they have opensource models?
i dont see the language performance a bottleneck on local hardware but rather than inference from models that can run it - unless u are referring to this?
the new meta is going to be with local smaller models then delegating into bigger models in sandboxes on the cloud, then communicating both through agents
im pretty sure this is what google and apple have decided together as well. so u have local inference then a small eval to determine the route/intent but working with local models and agents just puts you into the fun space of supporting edge cases for different machines running different software
as for the language question, yea everything can be optimized but as with all engineering is just evaluating if it’s worth spending the time on it, for us, as a company just starting we don’t have that privilege yet
@raulizahi @arcee_ai @code_star @huggingface im pretty sure we will be getting more and more low level stuff as people recognize the limits of frontier models and the cost
Arcee AI has shifted its entire catalog of public models, private models, proprietary datasets and agent traces to Hugging Face as the exclusive storage and distribution platform, marking the first full migration of this scale by a major American AI lab away from AWS S3 under a multi-million-dollar commercial agreement.
Super excited to announce that @arcee_ai is the first major American AI lab to replace AWS S3 with Hugging Face for ALL their models and datasets, public AND private 🔥🔥🔥
Multi-million $ partnership to support American open-source AI, let’s go!
