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Running

Love that I can call DeepSeek R1 directly from the Hub 🔥

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="together",
    api_key="xxxxxxxxxxxxxxxxxxxxxxxx"
)

messages = [
    {
        "role": "user",
        "content": "What is the capital of France?"
    }
]

completion = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1", 
    messages=messages, 
    max_tokens=500
)

print(completion.choices[0].message)

Is it possible to set a monthly payment budget or rate limits for all the external providers? I don't see such options in billings tab. In case a key is or session token is stolen, it can be quite dangerous to my thin wallet:(

@benhaotang you already get spending notifications when crossing important thresholds ($10, $100, $1,000) but we'll add spending limits in the future

@benhaotang you already get spending notifications when crossing important thresholds ($10, $100, $1,000) but we'll add spending limits in the future

Thanks for your quick reply, good to know!

Would be great if you could add Nebius AI Studio to the list :) New inference provider on the market, with the absolute cheapest prices and the highest rate limits...

Could be good to add featherless.ai

TitanML !!

Prima Era: request to join Inference Providers (GLM-5.3, Kimi-K3)

Hi Hugging Face team,

We're Prima Era (https://primaera.xyz, org https://huggingface.co/primaera on the Team plan) and would like to be registered as an Inference Provider for the conversational task, with an OpenAI-compatible API.

• Endpoint: https://api.primaera.xyz/v1
• Hub model: zai-org/GLM-5.3, our model id glm-5.3 (FP8, 1,048,576-token context, streaming, tool calls, reasoning_content)
• List price per million tokens: $0.55 input, $0.18 cached input, $1.05 output
• We can also serve moonshotai/Kimi-K3 on the same GPUs and would like to map it too.
• NVIDIA B300 GPUs we operate in the US; we don't log prompts or completions.

Already in place for your requirements:

• pricing (USD per million input and output tokens) and context_length in /v1/models
• a unique X-Request-Id header on every response, streaming included
• billing API: POST https://api.primaera.xyz/v1/request-costs with {"requestIds": [...]}, returning costNanoUsd per request, with the same bearer auth as inference

Next we'll open the huggingface.js PR. Who should we coordinate with? Contact: hf@primaera.xyz

Thanks!

Hi Hugging Face provider-onboarding team — following up on PyAI’s existing Hear/Speak request. Could you help us identify the current blocker and the next concrete qualification step?

Before we implement a native provider adapter, we need confirmation of API-only ASR/TTS eligibility, the required Hub model mappings, the ASR/TTS request and response contract, request-cost billing requirements, and any organization-plan prerequisites. Our working direct integrations are linked above; we understand they do not establish native Inference Providers approval or routing.

Is there a technical owner who can confirm the checklist and tell us what evidence or corrections are needed from PyAI? If audio-provider onboarding is currently unavailable or the request is simply queued, please let us know so we can plan appropriately. Thank you!

Runcrate: request to join Inference Providers when onboarding reopens

Hey Hugging Face team,

Jish here, founder of Runcrate (runcrate.ai). We run an OpenAI-compatible inference API for open-weight models, and we've built the provider side to your spec: the billing endpoint answers cost per request id in nano-USD, and our models list carries live pricing.

Tasks we'd bring: chat (LLM and VLM), feature extraction, text to image and speech to text.

We saw on huggingface.js#2698 that new-provider onboarding is paused for now. Could you keep us on the list for when it opens again? Happy to open the JS client PR whenever suits you.

Best,
Jish

Montagea — Request to join Hugging Face Inference Providers (FastH3 Omni)

Hi Hugging Face team, @Wauplin @SBrandeis ,

We are building Montagea, an AI video creation platform. Our API is deployed, and we'd like to join Inference Providers, initially with FastH3 Omni.

Website: https://montagea.com
API docs: https://montagea.com/docs/api
HF organization: https://huggingface.co/Montagea-com
Contact: tim@montagea.com

Initial model:
FastVideo/FastVideo-FastH3-Omni-8-Step-V1
Our API model ID: minimax-h3

Infrastructure:
We operate GPU instances on Google Cloud using NVIDIA RTX PRO 6000 Blackwell GPUs and UniServe.

Capabilities:

• Text-to-video and image-to-video
• Reference image, video and audio inputs
• Asynchronous video submission, polling and retrieval

Our implementation includes:

• Provider API: https://api.montagea.com/api/partners/huggingface/v1
• Inference-Id response headers
• Per-request billing lookup in integer nano-USD
• Local JavaScript/Python adapters; upstream SDK registration is pending

Could you please confirm:

1. Are you currently onboarding new video inference providers?
2. How should FastH3 Omni's reference inputs map to HF tasks and SDK parameters?
3. What timeout and validation rules apply to asynchronous video generation?
4. What are the current organization-plan, settlement and server-side registration requirements?

We can arrange a scoped evaluation key privately and prepare the required SDK PRs, model mappings and documentation.

Thank you!

Hi Hugging Face team,

I'm Olumide Ola, founder of Olaverse Lab. We publish open-weight models on the Hub (huggingface.co/olaverse), including MIST-Extract-1.0, a 4B document-to-JSON extractor with calibrated per-field confidence, and MIST-QG, multilingual question generation. Both are built for English and African-language documents.

We run a production API for these models (api.olaverselab.com) with an OpenAI-compatible /v1/chat/completions endpoint, on dedicated GPUs with failover.

We'd like to be listed as an Inference Provider for our own models, so users on our model pages can call them through the router. We're happy to do the huggingface_hub and huggingface.js pull requests, the model mappings and the docs.

Two questions before we start:

Do you onboard providers that serve only their own models?
What do you need from us on billing and scale?
Thanks,
Olumide Ola, Olaverse Ltd

Hello Hugging Face Team,

I'm Trista Tsai from IntelliTrain, a Taiwan-based AI technology company.

We are interested in joining Hugging Face Inference Providers as an AI API provider.

We have reviewed the official provider registration documentation and would like to connect with the team responsible for provider onboarding and commercial partnerships.

Could you please direct us to the appropriate contact person or email address?

We would also appreciate information about the onboarding process, expected launch timeline, commercial terms, and potential API traffic.

Thank you for your assistance!

Best regards,
Trista Tsai
IntelliTrain
https://intelli-train.ai/

Alfred – a voice agent for everyday users, built on Inference Providers + HF sign-in

Hi HF team and everyone!

I'd like to share an app built entirely around Inference Providers and say thanks for making it possible.

Alfred is a friendly, animated voice assistant for Android. Users sign in with their Hugging Face account (OAuth + PKCE). From then on, every model call goes through the Inference Providers router with their own token, so they need no API keys and no extra accounts.

What it does with the Hub:

• Chat with any Hub model, with a focus on regional models. For Polish, it automatically recommends the newest official Bielik (SpeakLeash) available on the router
• Image generation & editing: Z-Image-Turbo, Qwen-Image, Qwen-Image-Edit (fal-ai queue mode)
• Voice in/out with Whisper + Piper (sherpa-onnx), 10 UI languages, a voice translator, web search, PDF/Word documents and MCP servers

Alfred is in closed testing on Google Play. If anyone from the team wants to try it, send me a DM with the Google account email to add to the tester list.

We'd love to talk about collaboration too: a showcase or case study, feedback on the integration, or early access to new regional models. Who would be the right person to contact?

Thanks!
Marcin Wawrzyniak, DevPartner (Poland)