DeepInfra on Hugging Face Inference Providers 🔥

2026-04-27 · Source: Hugging Face - Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

DeepInfra, a serverless AI inference platform, is now a supported Inference Provider on the Hugging Face Hub as of April 29, 2026. This integration allows developers to access DeepInfra's catalog of over 100 models, including LLMs, text-to-image, text-to-video, and embeddings, directly from Hugging Face model pages and client SDKs. Initially, DeepInfra supports conversational and text-generation tasks, offering access to models like DeepSeek V4, Kimi-K2.6, and GLM-5.1. Users can configure API keys and provider preferences in their Hugging Face account settings, with options for direct billing via DeepInfra or routed billing through Hugging Face at standard provider rates. Hugging Face PRO users receive $2 in monthly inference credits applicable across providers.

Key takeaway

For AI Engineers building applications with diverse models, DeepInfra's integration into the Hugging Face Hub simplifies model access and billing. You can now seamlessly deploy models like DeepSeek V4 for conversational tasks, choosing between direct provider billing or consolidated billing through Hugging Face, potentially leveraging PRO plan credits to optimize costs and streamline your workflow.

Key insights

DeepInfra's integration with Hugging Face Hub streamlines serverless AI inference for diverse models.

Principles

• Cost-effective inference is crucial for developers.
• Unified access simplifies AI model integration.

Method

Users can set custom API keys or route requests via Hugging Face for serverless inference, with billing handled directly by the provider or through Hugging Face.

In practice

• Integrate DeepSeek V4 Pro via Hugging Face SDKs.
• Utilize Hugging Face PRO credits for inference.

Topics

• DeepInfra
• Hugging Face Hub
• Serverless AI Inference
• Large Language Models
• API Integration

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Hugging Face - Blog.