AI INFERENCE

Run AI Models in Production

Discover inference providers, deployment strategies, and practical tradeoffs to serve AI models at scale, from free APIs to enterprise-grade solutions. This page is built to help you choose a serving path based on workload, privacy, latency, and operational complexity rather than provider hype alone.

Prototype fast

Serverless APIs are usually the fastest path when you want to test product ideas without managing GPUs.

Stabilize latency

Dedicated endpoints become more attractive once request volume and user expectations are predictable.

Keep control

Self-hosted or Triton-style deployments make more sense when privacy, custom runtimes, or cost control dominate the decision.

Inference Providers

Hugging Face Inference API

Free tier serverless inference for thousands of models. Great for prototyping and light workloads.

Free TierServerless100k+ Models

Hugging Face Inference Endpoints

Dedicated infrastructure for production inference. Choose your GPU, region, and scaling options.

Dedicated GPUAuto-scalingProduction

AWS SageMaker

Deploy Hugging Face models on SageMaker with optimized containers and enterprise-grade reliability.

EnterpriseAWSManaged

NVIDIA Triton

High-performance inference server supporting multiple frameworks. Ideal for maximum throughput.

High ThroughputMulti-frameworkOn-Premise

What teams often miss

The biggest mistake is optimizing only for first-day setup speed. Real inference decisions also depend on retry behavior, scaling predictability, prompt size, observability, and whether your data can leave your environment at all.

Best next step

After choosing a serving path, validate the actual model with the comparison workspace and recommender so infrastructure and model quality stay aligned.

Quick Start by Task

Text Generation

NLP

Run LLMs like Llama, Mistral, and GPT-style models for text generation tasks.

meta-llama/Llama-3-8B mistralai/Mistral-7B-v0.1

Image Generation

Vision

Generate images using Stable Diffusion and other diffusion models.

stabilityai/stable-diffusion-xl-base-1.0

Speech & Audio

Audio

Transcribe audio with Whisper or generate speech with text-to-speech models.

openai/whisper-large-v3

Embeddings

Retrieval

Create vector embeddings for semantic search, RAG pipelines, and clustering.

sentence-transformers/all-MiniLM-L6-v2

Get Started in Seconds

Use the Hugging Face Inference API with just a few lines of code. No GPU required.

import requests

API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-3-8B"
headers = {"Authorization": "Bearer hf_YOUR_TOKEN"}

response = requests.post(API_URL, headers=headers, json={
    "inputs": "What is AI inference?",
    "parameters": {"max_new_tokens": 200}
})

print(response.json())

Ready to learn more?

Dive into our chapter-by-chapter AI inference tutorial to learn about model serving, hardware selection, throughput optimization, and production rollout patterns.

Start the Tutorial