Inference Endpoints by Hugging Face - AI Inference Platforms Tool

Overview

Inference Endpoints by Hugging Face is a fully managed deployment service for running models from the Hugging Face Hub in production. It enables teams to deploy Transformer and Diffuser models (and other model types hosted on the Hub) to secure, monitored, and autoscaling endpoints without managing infrastructure. Typical supported workloads include text generation, classification, speech recognition, and image generation pipelines such as Stable Diffusion. The product is built for both developer velocity and enterprise requirements: users can deploy public or private Hub models, push new model versions and roll back, and capture request logging and usage metrics. The service offers CPU and GPU-backed instances, autoscaling and concurrency controls, and options for custom containers or specialized inference backends (for example the Text Generation Inference backend) to reduce latency and cost for large-generation workloads. Inference Endpoints integrates with standard Hugging Face authentication tokens and supports secure, private deployments for organizations requiring VPC/private-network connectivity and compliance controls. Overall, it is designed to let teams move models from research to production quickly while retaining observability, security, and the ability to scale.

Key Features

• One-click or API deployment of models directly from the Hugging Face Hub (public or private).
• CPU and GPU-backed endpoints with autoscaling and concurrency controls for production traffic.
• Support for Transformers, Diffusers, and specialized backends like Text Generation Inference.
• Custom container and dependency support to run specialized inference code or libraries.
• Enterprise capabilities: private networking, access controls, audit logging, and compliance features.
• Built-in observability: request logs, usage metrics, model versioning, and easy rollbacks.

Example Usage

Example (python):

import requests
import os

# Replace with the HTTPS endpoint URL returned when you create an Inference Endpoint
ENDPOINT_URL = "https://YOUR_ENDPOINT_URL"
HF_API_TOKEN = os.environ.get("HF_API_TOKEN")  # set your Hugging Face API token in env

headers = {
    "Authorization": f"Bearer {HF_API_TOKEN}",
    "Content-Type": "application/json",
}

payload = {
    "inputs": "Write a short product description for a portable espresso maker.",
    # Many endpoints accept an optional 'parameters' dict to control generation
    "parameters": {"max_new_tokens": 120, "temperature": 0.7}
}

resp = requests.post(ENDPOINT_URL, headers=headers, json=payload)
resp.raise_for_status()
print(resp.json())

# For image-generation (Diffusers) endpoints, inputs may be a prompt string
# payload = {"inputs": "A photorealistic red bicycle leaning against a brick wall"}
# resp = requests.post(ENDPOINT_URL, headers=headers, json=payload)
# print(resp.content)  # may be raw bytes depending on endpoint configuration

Key Information

• Category: Inference Platforms
• Type: AI Inference Platforms Tool