I’m excited to share that Featherless AI is a supported reasoning provider for Hug Face Hub! Featherless AI joins the growing ecosystem and directly enhances the breadth and capabilities of serverless inference on the hub’s model page. Inference providers are seamlessly integrated into the client SDK (both JS and Python), making it easy to use different models using preferred providers.
Featherless AI supports a variety of text and conversation models, including the latest open source models such as Deepseek, Meta, Google, Qwen, and more.
Featherless AI is a serverless AI inference provider with unique model loading and GPU orchestration capabilities that create a very large catalog of models available to users. Providers often offer an unlimited range of models with low cost of access to a limited set of models, or with the operational costs associated with the user managing the server. Featherless offers an unparalleled range of models and varieties, but offers serverless pricing, offering the best world of both worlds. Find the complete list of supported models on the model page.
I’m so excited to see what you build with this new provider!
Learn more about using Featherless as an inference provider on our dedicated documentation page.
How it works
In the website UI
In User Account Settings, you set your own API key for the provider you signed up for. If no custom key is configured, the request is routed through HF. See more details about the document request types. Order a provider if you like. This applies to model page widgets and code snippets.
As mentioned before, when calling an inference provider there are two modes: a custom key (the call goes directly to the inference provider, using the corresponding inference provider’s own API key) (in that case no tokens are required from the provider.
The model page introduces third-party inference providers (compatible with current models sorted by user preferences)
From the client SDK
I’m using Huggingface_hub from Python
The following example shows how to use DeepSeek-R1 using featherless AI as an inference provider: Automatic routing through a hugging face can be used with a hugging face token, or your own wingless AI API key if you have one.
Make sure you have installed or upgrade huggingface_hub version v0.33.0 or higher: pip install – Upgrade Huggingface-hub
Import OS
from huggingface_hub Import Inference client=Inference client(provider=“Featherless”,api_key = os.environ(“HF_TOKEN”)) Message = ({
“role”: “user”,
“content”: “What is the capital of France?”
}) complete = client.chat.completions.create(model =“deepseek-ai/deepseek-r1-0528”message = message,)
printing(complete.choices)0). message)
From JS using @huggingface/Incerence
Import { inference } from “@Huggingface/Inference”;
const Client= new inference(process.Env.hf_token);
const chatcompletion = wait client.ChatCompletion({
Model: “deepseek-ai/deepseek-r1-0528”,
message:({
role: “user”,
content: “What is the capital of France?”
}),
Provider: “Featherless”,});
console.log(ChatCompletion.Choices(0).message);
Request
For direct requests, i.e. when using keys from inference providers, the corresponding provider will be billed. For example, if you use a featherless AI API key, you will be billed for a featherless AI account.
For routed requests, i.e. when authenticating through a facehub that hugs, you only pay the standard provider API rate. There is no additional markup. Pass the provider’s costs directly. (In the future, we may establish a revenue sharing agreement with our provider partners.)
Important Memopia users get $2 worth of inference credits each month. You can use them between providers. š„
Subscribe to our Hugging Face Pro plan for access to inference credits, Zerogpu, Spaces Dev Mode, 20x high limits and more.
We also infer small allocations for sign-in free users for free, but upgrade to Pro if possible!
Feedback and next steps
We want to get your feedback! Share your thoughts and comments here: https://huggingface.co/spaces/huggingface/huggingdiscussions/discussions/49
