Hugging Face Inference API Free Tier Limits & Pricing 2026

Q: What is the difference between Serverless Inference API, Inference Endpoints, and Inference Providers?

Three different products under one Hugging Face umbrella. Serverless API: shared infrastructure, free tier, rate-limited, best for prototyping. Inference Endpoints: dedicated GPU you spin up for a single model, billed by the minute, scale-to-zero, best for production with predictable load. Inference Providers: a unified OpenAI-compatible gateway to 15+ third-party inference partners (Groq, Together AI, Fireworks, Replicate, Cerebras, Cohere) - you pick the model and Hugging Face routes to the underlying provider, pay per-token at provider pricing.

Q: Is Hugging Face PRO worth $9/month?

PRO is worth it if you (a) hit Serverless API rate limits in prototyping, (b) use ZeroGPU Spaces - PRO gives 25 minutes of daily H200 compute (vs ~3-5 min on free), (c) want 2M Inference Provider credits monthly. For a developer running multiple demos, prototyping with larger models, and using a few Spaces, $9 covers more than a single H200/hour rental would. For pure API use without ZeroGPU or Spaces, PRO is harder to justify - direct provider APIs (OpenRouter, Together) usually offer better per-token economics.

Q: How much does Hugging Face Inference Endpoints cost?

Inference Endpoints starts at $0.03 per CPU core/hour and $0.50 per GPU/hour, billed by the minute. Common GPU tiers: NVIDIA T4 (~$0.50/hr), L4 (~$0.80/hr), A10 (~$1.30/hr), A100 (~$4.50/hr), H100 (~$6/hr). Scale-to-zero is supported - paused endpoints incur no charges. For a model serving 100 requests/day in bursty traffic, scale-to-zero on an A10 endpoint typically lands at $20-40/month.

Q: What is Hugging Face Inference Providers?

Inference Providers is a unified OpenAI-compatible API that routes to 15+ third-party inference partners - Groq (LPU low-latency), Together AI (200+ open models, sub-100ms), Replicate (per-prediction billing, easy custom models), Fireworks (open-weight inference focus), Cerebras (CS-3 fast inference), Cohere, Nebius, and others. One auth token, one API format, switch providers by changing a string. Useful when you want provider choice without per-provider account setup. Pricing matches each underlying provider's published rate (no markup beyond the included PRO credits).

Last updated: June 2026

Hugging Face Inference API in 2026 is three products under one umbrella: the Serverless Inference API (free tier with rate limits, best for prototyping), Inference Endpoints (dedicated GPU you spin up per model, $0.50/hr+, scale-to-zero), and Inference Providers (a unified OpenAI-compatible gateway routing to Groq, Together AI, Fireworks, Replicate, Cerebras, and 10+ others). This guide unpacks each product, the real free-tier limits, where Hugging Face PRO ($9/month) is worth it, and how the stack compares to OpenRouter, Together AI direct, and Groq direct.

If you're still picking a model, see Best Open Source LLM 2026. For free tiers across all providers (not just Hugging Face), see Free LLM API Credits.

The three Hugging Face Inference products

Product	What it is	Pricing	Free tier
Serverless Inference API	Shared inference on HF infrastructure	Free / PRO $9/mo	A few hundred req/hour, <10B params
Inference Endpoints	Dedicated GPU per model, scale-to-zero	$0.03/CPU/hr, $0.50+/GPU/hr	None (paid only)
Inference Providers	Unified API to 15+ third-party providers	Pass-through provider rates	2M credits/mo on PRO

The naming is unfortunate - all three are "Hugging Face Inference" but solve different problems. Pick by workload: prototyping (Serverless), production with single model and steady load (Endpoints), production with multiple models or provider choice (Providers).

Hugging Face Inference API free tier: limits and rate limits

The Serverless Inference API is shared infrastructure that runs inference on a wide catalog of Hugging Face Hub models. Free-tier users get a few hundred requests per hour, limited to models under approximately 10 billion parameters. Cold starts on less popular models can take 10-30 seconds.

What works on the free tier:

Text classification, NER, summarization, embeddings - fast, generous limits.
Small-to-medium LLMs (Llama 3.2 8B, Qwen 2.5 7B, Mistral 7B) - fine for prototyping.
Image classification, object detection - straightforward.

What does not work well on the free tier:

70B+ LLMs - typically gated or rate-limited heavily, often unavailable on Serverless.
High-volume production traffic - rate limits hit fast.
Latency-critical workloads - cold starts will bite.

Hugging Face PRO ($9/month) raises Serverless rate limits substantially and adds three perks: 25 minutes of daily H200 ZeroGPU compute (vs ~3-5 minutes on free), 1TB private storage + 10TB public storage, and 2M monthly Inference Provider credits. For a developer running multiple demos, prototyping with larger models, or building ZeroGPU Spaces, PRO is the cheapest H200 access available.

Inference Endpoints - dedicated GPU

Inference Endpoints lets you spin up a dedicated server (CPU or GPU) for a single model from the Hub. You pick the model and the hardware tier; Hugging Face handles provisioning, autoscaling, and the OpenAI-compatible API in front of it.

Pricing (May 2026, billed by the minute, not the hour):

Tier	Hardware	Hourly	Typical use
CPU small	2 vCPU	$0.03	Embeddings, small classification
CPU large	8 vCPU	$0.12	Heavier CPU workloads
GPU T4	16GB	$0.50	7B-14B models, image classification
GPU L4	24GB	$0.80	13B models, Stable Diffusion
GPU A10G	24GB	$1.30	30B at 4-bit, faster SD
GPU A100	80GB	$4.50	70B at 4-bit, large vision models
GPU H100	80GB	$6.00	Flagship inference, low latency

Scale-to-zero is the killer feature: paused endpoints incur no charges. For a model serving 100-1000 requests/day in bursty traffic, scale-to-zero with autoscaling typically lands at $20-60/month - competitive with hosted APIs at this volume.

When Endpoints make sense: you have a custom model (fine-tuned variant, in-house model) or a specific pinned version that's not exposed via Inference Providers, and your traffic is steady enough to justify dedicated hardware.

When Endpoints don't make sense: bursty workloads with very long idle periods (use Serverless or Providers), or extremely steady workloads at high QPS (renting a GPU directly on RunPod or Lambda is often cheaper for sustained load).

Inference Providers - the unified gateway

Launched in late 2024 and matured through 2025-2026, Inference Providers is Hugging Face's answer to the "which inference partner should I use" question. It exposes a single OpenAI-compatible API that routes to 15+ third-party providers:

Groq - LPU hardware, sub-100ms time-to-first-token, fastest on Llama / Qwen variants.
Together AI - 200+ open-source LLMs, automated tuning, broad catalog.
Fireworks - high-performance, low-latency open-weight inference focus.
Replicate - per-prediction billing, easy custom-model deployment from Hub.
Cerebras - CS-3 wafer-scale chips, ultra-fast Llama inference.
Cohere - Cohere's Command models, strong RAG and tool use.
Nebius, SambaNova, Novita AI, Hyperbolic, Featherless - and others.

Pricing is pass-through - you pay the underlying provider's published rate. The 2M monthly Inference Provider credits included with PRO ($9) cover prototyping. Above the included credits, charges go to your HF account at provider rates.

Switching providers is a string change: model meta-llama/Llama-3.3-70B-Instruct routes to whichever provider you select (or the cheapest available). One auth token, one billing relationship, no per-provider account setup.

When to use which Hugging Face Inference product

Prototyping a new model on the Hub: Serverless Inference API (free or PRO). Cold start is fine; rate limits are generous for development.

Production, single fine-tuned model, steady traffic: Inference Endpoints with scale-to-zero on the right GPU tier.

Production, multiple models or provider choice: Inference Providers. Lower operational overhead than running Endpoints; pay per-token instead of per-hour.

High-volume single-model production (>100K req/day): Skip HF for hot path. Direct provider integration (Groq, Together, Fireworks) or self-hosted GPU on RunPod / Lambda usually wins on per-token cost. Use HF Inference Providers for failover.

ZeroGPU Spaces / interactive demos: HF PRO. 25 minutes of daily H200 for $9/month is the cheapest H200 access for short interactive workloads.

Hugging Face vs OpenRouter

Both Inference Providers and OpenRouter expose multiple LLM backends through one OpenAI-compatible API. The differences:

Catalog: OpenRouter has the broadest catalog (300+ models from 60+ providers). HF Inference Providers covers 15+ partners with focus on open-weight models. For Anthropic Claude, Google Gemini, OpenAI GPT-5, OpenRouter aggregates them; HF focuses on open models served by inference partners.

Hub integration: HF wins by a lot if you use the Hub for models, datasets, Spaces. Models on the Hub are one click away from inference. OpenRouter is inference-only.

Pricing: OpenRouter adds ~5% markup at most levels. HF Inference Providers is pass-through (no markup beyond the PRO-included credits). For pure cost minimization on open models, HF Providers wins; for proprietary frontier models (GPT-5, Claude), OpenRouter is competitive.

UX: OpenRouter has the more polished playground and analytics. HF is improving but still feels like an extension of the Hub UX.

Pick Hugging Face Inference Providers if Hub integration matters (most ML teams). Pick OpenRouter for breadth and frontier-model coverage.

Hugging Face vs direct provider APIs

If you only call one or two providers (e.g., always Groq or always Together), going direct to the provider is usually cheapest and lowest-latency. The HF Inference Providers layer adds a few milliseconds of routing overhead and a per-provider relationship through HF instead of with the provider.

The trade-off: with HF you switch providers with a string change. Direct, you maintain N integrations.

For 90% of teams: start with Inference Providers, migrate to direct when one provider becomes 70%+ of your spend.

Common mistakes with Hugging Face Inference

Hitting Serverless rate limits in production. Serverless is for prototyping - graduate to Endpoints or Providers before you ship.
Running Endpoints 24/7 for bursty traffic. Enable scale-to-zero. A model used 2 hours/day costs 1/12 of a 24/7 endpoint.
Paying for ZeroGPU on the free tier. ZeroGPU has very tight time limits on free; PRO is the practical minimum for ZeroGPU work.
Using Inference Providers for one stable provider. If 95% of your traffic goes to Groq, integrate Groq directly. The HF layer is for choice, not for single-provider deployments.
Ignoring cold starts on Serverless. For latency-sensitive prototyping, pre-warm the model with periodic requests or move to Endpoints.

How to start

Get a free Hugging Face account. Generate an Inference API token in account settings.
Test Serverless first. Point the OpenAI SDK at https://api-inference.huggingface.co/v1 (or use the huggingface_hub Python library directly) and call a small model.
Upgrade to PRO ($9) if rate-limited or if you use Spaces / ZeroGPU.
Move to Inference Providers when you need provider choice or higher reliability.
Move to Inference Endpoints when you have a custom model or specific GPU tier requirements.
Direct provider integration when one provider dominates your spend.

Frequently asked questions

What are the Hugging Face Inference API free tier limits in 2026? The Serverless Inference API has a free tier with rate limits: a few hundred requests per hour, limited to models under ~10B parameters, with cold starts on less popular models. Hugging Face PRO ($9/month) raises rate limits significantly and adds 25 minutes of daily H200 ZeroGPU compute plus 2M monthly Inference Provider credits. Inference Endpoints and Inference Providers are paid by usage.

What is the difference between Serverless Inference API, Inference Endpoints, and Inference Providers? Three products under one Hugging Face umbrella. Serverless API: shared infrastructure, free tier, rate-limited, best for prototyping. Inference Endpoints: dedicated GPU you spin up for a single model, billed by the minute, scale-to-zero, best for production with predictable load. Inference Providers: a unified OpenAI-compatible gateway to 15+ third-party partners (Groq, Together AI, Fireworks, Replicate, Cerebras, Cohere), pay per-token at provider pricing.

Is Hugging Face PRO worth $9/month? Worth it if you hit Serverless rate limits in prototyping, use ZeroGPU Spaces (PRO gives 25 minutes of daily H200 vs ~3-5 min free), or want 2M monthly Inference Provider credits. For pure API use without ZeroGPU or Spaces it is harder to justify - direct provider APIs usually offer better per-token economics.

How much does Hugging Face Inference Endpoints cost? Endpoints start at $0.03 per CPU core/hour and $0.50 per GPU/hour, billed by the minute. Common GPU tiers: T4 (~$0.50/hr), L4 (~$0.80/hr), A10 (~$1.30/hr), A100 (~$4.50/hr), H100 (~$6/hr). Scale-to-zero means paused endpoints incur no charges; a bursty 100 req/day model often lands at $20-40/month.

What is Hugging Face Inference Providers? A unified OpenAI-compatible API that routes to 15+ third-party inference partners - Groq, Together AI, Replicate, Fireworks, Cerebras, Cohere, Nebius and others. One auth token, switch providers by changing a string, pricing matches each underlying provider's published rate.

Hugging Face Inference API vs OpenRouter - which should I use? Both expose multiple providers behind one OpenAI-compatible API. Hugging Face integrates with the Hub (models, datasets, ZeroGPU Spaces); OpenRouter has a larger catalog (300+ models) and a more polished pure-inference UX. Pick Hugging Face if you use the Hub, OpenRouter for breadth and frontier-model coverage.