DeepInfra on Hugging Face Inference Providers 🔥

Hugging Face Blog / 4/29/2026

📰 NewsDeveloper Stack & InfrastructureIndustry & Market Moves

Key Points

  • DeepInfra has been added as an inference provider option within Hugging Face’s Inference Providers ecosystem.
  • The update focuses on enabling users to route inference requests to DeepInfra through the Hugging Face platform.
  • This integration is intended to broaden model serving choices and improve flexibility for developers using Hugging Face.
  • The post is published on April 29, 2026 and includes a link to the corresponding update documentation on GitHub.

DeepInfra on Hugging Face Inference Providers 🔥

Published April 29, 2026
Update on GitHub

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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

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