While they all involve running predictions on models, they represent different levels of a project—ranging from quick testing to programmatic integration to enterprise production.

1. Inference Widget (The "Tester")

The widget is the interactive UI found directly on a model's repository page. It is a browser-based tool that lets you manually type a prompt to see if the model's output meets your needs.

• How it works: It uses "Inference Providers" (serverless partners like Together AI or SambaNova) to run the model on-demand for free in the UI.
• Best for: Absolute beginners or developers who want to "try before they buy" (or download).

2. Inference API (The "Developer Tool")

This is a serverless way to use a model inside your own code without managing servers. You send a standard HTTP request (using Python or JavaScript) to Hugging Face, and it returns the model’s prediction.

• How it works: It is powered by the same shared, serverless infrastructure as the widget. It’s essentially a public "managed service" for thousands of models.
• Best for: Prototyping, small applications, or any project that doesn't require guaranteed sub-millisecond latency.

# pip install huggingface_hub
# <https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english>
from huggingface_hub import InferenceClient

# Initialize with the correct full Model ID
# DistilBERT is now under the 'distilbert/' namespace
client = InferenceClient(
    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english",
    token="<token here>"
)

try:
    # Use the text_classification task specifically
    result = client.text_classification("That was the first thing that sprang to mind as I watched the closing credits to Europa make there was across the screen, The performances are good, they may not be on par with performances in later von Trier films, but that's just because the images are sometimes so distracting that you don't really pick up on them the first time round. But I would like to point out the fantastic performance of Jean-Marc Barr in the lead role, whose blind idealism is slowly warn down by the two opposing sides, until he erupts in the films final act. Again, muck like The Element of Crime, the film ends with our hero unable to wake up from his nightmare state, left in this terrible place, with only the continuing narration of von Sydow to seal his fate. Europa is a tremendous film, and I cant help thinking what a shame that von Trier has abandoned this way of filming, since he was clearly one of the most talented visual directors working at that time, Europa, much like the rest of his cinematic cannon is filled with a wealth of iconic scenes. His dedication to composition and mise-en-scene is unrivalled, not to mention his use of sound and production design. But since . 10/10")
    print(result)
except Exception as e:
    print(f"Error: {e}")

3. Deploy / Inference Endpoints (The "Production System")

When you click Deploy and choose Inference Endpoints, you are spinning up dedicated private infrastructure. Unlike the shared Inference API, this hardware is yours alone.

• How it works: You choose the specific cloud (AWS or Azure) and hardware (CPU, GPU, or AI accelerators). It handles autoscaling, provides ultra-low latency, and offers enterprise-grade security.
• Best for: High-traffic production apps that need 24/7 reliability, custom security settings, and fast response times.

Summary Comparison Table