惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

aimingoo的专栏
aimingoo的专栏
B
Blog RSS Feed
Recent Announcements
Recent Announcements
Vercel News
Vercel News
M
MIT News - Artificial intelligence
阮一峰的网络日志
阮一峰的网络日志
L
LangChain Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Security Blog
Microsoft Security Blog
H
Help Net Security
T
The Blog of Author Tim Ferriss
Y
Y Combinator Blog
G
Google Developers Blog
罗磊的独立博客
爱范儿
爱范儿
宝玉的分享
宝玉的分享
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园_首页
S
SegmentFault 最新的问题
WordPress大学
WordPress大学
月光博客
月光博客
人人都是产品经理
人人都是产品经理
Apple Machine Learning Research
Apple Machine Learning Research

Hugging Face - Blog

Waypoint-1.5: Higher-Fidelity Interactive Worlds for Everyday GPUs ALTK‑Evolve: On‑the‑Job Learning for AI Agents Safetensors is Joining the PyTorch Foundation Holo3: Breaking the Computer Use Frontier Any Custom Frontend with Gradio's Backend A New Framework for Evaluating Voice Agents (EVA) Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations One-Shot Any Web App with Gradio's gr.HTML CUGA on Hugging Face: Democratizing Configurable AI Agents New in llama.cpp: Model Management Building Deep Research: How we Achieved State of the Art OVHcloud on Hugging Face Inference Providers 🔥 20x Faster TRL Fine-tuning with RapidFire AI Building for an Open Future - our new partnership with Google Cloud Aligning to What? Rethinking Agent Generalization in MiniMax M2 Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac Sentence Transformers is joining Hugging Face! Unlock the power of images with AI Sheets Supercharge your OCR Pipelines with Open Models Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face Get your VLM running in 3 simple steps on Intel CPUs Nemotron-Personas-India: Synthesized Data for Sovereign AI Introducing RTEB: A New Standard for Retrieval Evaluation Accelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models VibeGame: Exploring Vibe Coding Games Nemotron-Personas-Japan: ソブリン AI のための合成データセット Swift Transformers Reaches 1.0 – and Looks to the Future Smol2Operator: Post-Training GUI Agents for Computer Use SyGra: The One-Stop Framework for Building Data for LLMs and SLMs Gaia2 and ARE: Empowering the community to study agents
DeepInfra on Hugging Face Inference Providers 🔥
Aray Sultanbekova, Shang-Pin, Utemuratov, Yessen K, Oguz Vuruska · 2026-04-29 · via Hugging Face - Blog

Back to Articles

banner image

We're thrilled to share that DeepInfra is now a supported Inference Provider on the Hugging Face Hub!

DeepInfra joins our growing ecosystem, enhancing the breadth and capabilities of serverless inference directly on the Hub's model pages. Inference Providers are also seamlessly integrated into our client SDKs (for both JS and Python), making it super easy to use a wide variety of models with your preferred providers.

DeepInfra is a serverless AI inference platform offering one of the most cost-effective pricing per token in the industry. With a catalog of over 100 models, DeepInfra makes it easy for developers to integrate a wide range of AI capabilities into their applications with minimal setup.

DeepInfra supports a broad spectrum of model types - from LLMs to text-to-image, text-to-video, embeddings, and more. As part of this initial integration, DeepInfra is launching support for conversational and text-generation tasks on Hugging Face, enabling access to popular open-weight LLMs such as DeepSeek V4, Kimi-K2.6, GLM-5.1, and many more. Support for additional tasks (text-to-image, text-to-video, embeddings, and more) will roll out soon!

Read more about how to use DeepInfra as an Inference Provider in its dedicated documentation page.

See the full list of models supported by DeepInfra here.

Follow DeepInfra on Hugging Face: https://huggingface.co/DeepInfra.

How it works

In the website UI

  1. In your user account settings, you are able to:
  • Set your own API keys for the providers you've signed up with. If no custom key is set, your requests will be routed through HF.
  • Order providers by preference. This applies to the widget and code snippets in the model pages.

Inference Providers

  1. As mentioned, there are two modes when calling Inference Providers:
  • Custom key (calls go directly to the inference provider, using your own API key of the corresponding inference provider)
  • Routed by HF (in that case, you don't need a token from the provider, and the charges are applied directly to your HF account rather than the provider's account)

Inference Providers

  1. Model pages showcase third-party inference providers (the ones that are compatible with the current model, sorted by user preference)

Inference Providers

From the client SDKs

DeepInfra is available through the Hugging Face SDKs - huggingface_hub (>= 1.11.2) for Python and @huggingface/inference for JavaScript.

The following examples show how to use DeepSeek V4 Pro through DeepInfra. Use a Hugging Face token to authenticate - the request will be routed to DeepInfra automatically.

From your favorite Agent Harness

Hugging Face Inference Providers are integrated in most Agent Harnesses - including Pi, OpenCode, Hermes Agents, OpenClaw, and more. This means you can plug DeepInfra-hosted models straight into your favorite tools without any extra glue code. Browse the full list of integrations here.

from Python

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://router.huggingface.co/v1",
    api_key=os.environ["HF_TOKEN"],
)

completion = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Pro:deepinfra",
    messages=[
        {
            "role": "user",
            "content": "Write a Python function that returns the nth Fibonacci number using memoization."
        }
    ],
)

print(completion.choices[0].message)

from JS

import { OpenAI } from "openai";

const client = new OpenAI({
    baseURL: "https://router.huggingface.co/v1",
    apiKey: process.env.HF_TOKEN,
});

const chatCompletion = await client.chat.completions.create({
    model: "deepseek-ai/DeepSeek-V4-Pro:deepinfra",
    messages: [
        {
            role: "user",
            content: "Write a Python function that returns the nth Fibonacci number using memoization.",
        },
    ],
});

console.log(chatCompletion.choices[0].message);

Billing

For direct requests, i.e. when you use the key from an inference provider, you are billed by the corresponding provider. For instance, if you use a DeepInfra API key you're billed on your DeepInfra account.

For routed requests, i.e. when you authenticate via the Hugging Face Hub, you'll only pay the standard provider API rates. There's no additional markup from us; we just pass through the provider costs directly. (In the future, we may establish revenue-sharing agreements with our provider partners.)

Important Note ‼️ PRO users get $2 worth of Inference credits every month. You can use them across providers. 🔥

Subscribe to the Hugging Face PRO plan to get access to Inference credits, ZeroGPU, Spaces Dev Mode, 20x higher limits, and more.

We also provide free inference with a small quota for our signed-in free users, but please upgrade to PRO if you can!

Feedback and next steps

We would love to get your feedback! Share your thoughts and/or comments here: https://huggingface.co/spaces/huggingface/HuggingDiscussions/discussions/49