Is Hugging Face Down? How to Check Hugging Face Status in Real-Time

Is Hugging Face Down? How to Check Hugging Face Status in Real-Time

Quick Answer: To check if Hugging Face is down, visit apistatuscheck.com/api/huggingface for real-time monitoring, or check the official status.huggingface.co page. Common signs include model download failures, Inference API timeouts, Spaces not loading, Hub authentication errors, dataset loading issues, and rate limiting errors.

When your ML pipeline suddenly fails or your production inference stops working, every minute of downtime compounds. Hugging Face hosts over 500,000 models, 100,000+ datasets, and thousands of Spaces, making it the backbone of modern AI development. Whether you're seeing model download failures, API timeouts, or authentication errors, quickly verifying Hugging Face's status can save hours of troubleshooting and help you make critical decisions about your ML infrastructure.

How to Check Hugging Face Status in Real-Time

1. API Status Check (Fastest Method)

The quickest way to verify Hugging Face's operational status is through apistatuscheck.com/api/huggingface. This real-time monitoring service:

  • Tests actual API endpoints every 60 seconds
  • Shows response times and latency trends across inference, model downloads, and Hub API
  • Tracks historical uptime over 30/60/90 days
  • Provides instant alerts when issues are detected
  • Monitors multiple services (Inference API, Model Hub, Spaces, Datasets)

Unlike status pages that rely on manual updates, API Status Check performs active health checks against Hugging Face's production endpoints, giving you the most accurate real-time picture of service availability.

2. Official Hugging Face Status Page

Hugging Face maintains status.huggingface.co as their official communication channel for service incidents. The page displays:

  • Current operational status for all services
  • Active incidents and investigations
  • Scheduled maintenance windows
  • Historical incident reports
  • Component-specific status (Inference API, Model Hub, Spaces, Datasets, Authentication)

Pro tip: Subscribe to status updates via email or RSS on the status page to receive immediate notifications when incidents occur.

3. Check the Hugging Face Hub

If the Hugging Face Hub at huggingface.co is loading slowly or showing errors, this often indicates broader infrastructure issues. Pay attention to:

  • Login failures or timeouts
  • Model page loading errors
  • File download failures (model weights, tokenizers)
  • Dataset preview not rendering
  • Space deployment failures

4. Test API Endpoints Directly

For developers, making a test API call can quickly confirm connectivity:

from huggingface_hub import InferenceClient

client = InferenceClient()

try:
    result = client.text_generation(
        "Hello world",
        model="gpt2"
    )
    print("API operational")
except Exception as e:
    print(f"API error: {e}")

Look for timeout errors, SSL/TLS handshake failures, or 500-series HTTP response codes.

5. Monitor Community Channels

Hugging Face's active community often reports issues quickly:

Common Hugging Face Issues and How to Identify Them

Model Download Failures

Symptoms:

  • OSError: Can't load model errors
  • Timeout errors during from_pretrained() calls
  • HTTP 500/502/503 errors from CDN
  • Incomplete model file downloads
  • Git LFS failures for large model files

What it means: Hugging Face stores model weights on a CDN and uses Git LFS for large files. During outages, these downloads may fail or timeout, especially for large models (>1GB). This differs from normal network issues—you'll see consistent failures across different models and regions.

Example error:

OSError: We couldn't connect to 'https://huggingface.co' to load this file

Inference API Errors

The Inference API is Hugging Face's hosted inference service. Common outage indicators:

Error types:

  • 503 Service Unavailable - Inference endpoints overloaded
  • 504 Gateway Timeout - Model inference took too long
  • 429 Rate Limit - Unusual if you're within your quota
  • Model loading error - Backend infrastructure issues
  • Connection timeout (no response within 60 seconds)

Example:

requests.exceptions.HTTPError: 503 Server Error: Service Unavailable

Affected models: Popular models like meta-llama/Llama-2-7b-chat-hf, mistralai/Mixtral-8x7B-Instruct-v0.1, and stabilityai/stable-diffusion-xl-base-1.0 see highest impact during outages.

Spaces Not Loading

Hugging Face Spaces host ML demos and applications. During outages:

  • Spaces show "Building" status indefinitely
  • Runtime errors: "Error: Space didn't start in time"
  • Blank pages or loading spinners
  • WebSocket connection failures
  • Gradio/Streamlit interfaces not responding

What to check:

  1. Is the issue affecting multiple Spaces or just yours?
  2. Check Space logs in the settings (if accessible)
  3. Try other popular Spaces as a benchmark

Hub Authentication Failures

Symptoms:

  • Login redirects failing
  • Token authentication errors: Invalid token
  • Git operations failing with 401/403 errors
  • huggingface-cli login timeouts
  • OAuth flow breaking for third-party integrations

Example:

$ huggingface-cli login
Error: Cannot reach huggingface.co

Authentication issues prevent you from:

  • Accessing private models/datasets
  • Pushing model updates
  • Using gated models that require access approval
  • Deploying to Spaces

Dataset Loading Issues

Common problems:

  • datasets.load_dataset() hanging or timing out
  • Stream dataset failures
  • Dataset viewer not loading on Hub
  • Parquet file download errors
  • Arrow format conversion failures

Example:

from datasets import load_dataset

# May hang or fail during outages
dataset = load_dataset("squad", split="train")
# ConnectionError or TimeoutError

Large datasets (>10GB) are especially vulnerable to incomplete downloads during partial outages.

Rate Limiting Errors

While rate limiting is normal, unusual rate limit errors can indicate backend issues:

Unusual patterns:

  • Rate limits much lower than documented
  • Rate limit errors immediately on first request
  • Inconsistent rate limits across similar API calls
  • No rate limit headers returned

This may indicate Hugging Face's load balancers are protecting overloaded backend systems.

The Real Impact When Hugging Face Goes Down

ML Pipeline Disruption

Modern ML workflows depend on Hugging Face at multiple stages:

  • Training: Can't download pre-trained models for fine-tuning
  • Evaluation: Test datasets unavailable
  • Deployment: Model artifacts unreachable
  • Continuous training: Scheduled jobs fail

For a data science team running experiments, a 4-hour outage can mean:

  • 20+ developers blocked
  • Scheduled model training jobs failed
  • Research deadlines missed
  • Compute resources idling (wasted cloud costs)

Production Inference Failures

Businesses running production AI on Hugging Face's Inference API face immediate impact:

SaaS products:

  • AI features completely non-functional
  • Chatbots returning error messages
  • Content generation failing
  • Image/video processing blocked

Example scenario: A customer support chatbot powered by Llama-2 suddenly returns errors to all users. Support tickets spike, customers are frustrated, and your team scrambles to diagnose whether it's your code or Hugging Face's infrastructure.

Broken Model Training

Failed fine-tuning runs:

  • Training job fails 2 hours in when loading validation dataset
  • GPU compute costs wasted ($2-10/hour per GPU)
  • Need to restart from checkpoint (if available)
  • Research timelines delayed

Scheduled training:

  • Nightly model retraining jobs fail
  • Production models become stale
  • A/B tests invalidated
  • Model performance degrades over time

Deployment Pipeline Failures

Modern ML deployment pipelines integrate Hugging Face deeply:

CI/CD breaks:

  • Docker builds fail (can't download model weights)
  • Model registry sync interrupted
  • Automated testing blocked
  • Rollback procedures compromised

Example Dockerfile:

FROM python:3.11
RUN pip install transformers
# This will fail during Hugging Face outages:
RUN python -c "from transformers import AutoModel; AutoModel.from_pretrained('bert-base-uncased')"

API Integration Downtime

Third-party applications integrating Hugging Face APIs experience cascading failures:

  • OpenAI alternatives using Hugging Face models break
  • Replicate models that depend on Hugging Face affected
  • Custom AI products built on Inference API stop working
  • Research tools and platforms disrupted

Community Impact

Hugging Face's massive community feels broad impact:

  • Kaggle competitions blocked (can't access datasets)
  • University research projects stalled
  • Tutorial code examples fail (breaking learning experiences)
  • Open-source projects can't run CI tests
  • Conference demo prep disrupted

Incident Response Playbook: What to Do When Hugging Face Goes Down

1. Implement Local Model Caching

Always cache models locally in production:

from transformers import AutoModel, AutoTokenizer

# Set local cache directory
cache_dir = "/app/model_cache"

model = AutoModel.from_pretrained(
    "bert-base-uncased",
    cache_dir=cache_dir,
    local_files_only=False  # Falls back to local if download fails
)

Docker best practices:

# Pre-download models during build (not runtime)
FROM python:3.11
COPY requirements.txt .
RUN pip install -r requirements.txt

# Cache models in image
RUN python -c "from transformers import AutoModel; \
    AutoModel.from_pretrained('bert-base-uncased')"

This ensures your application can start even when Hugging Face is down.

2. Fallback to Mirror or Alternative Sources

Use Hugging Face mirrors:

  • Mirror sites hosted by institutions (check status.huggingface.co for official mirrors)
  • Self-hosted model registries
  • Company-internal model caches

Alternative model sources:

  • Direct PyTorch Hub downloads
  • GitHub model releases
  • S3/GCS buckets with model backups
  • TorchServe model store

Example fallback logic:

def load_model_with_fallback(model_name):
    sources = [
        lambda: AutoModel.from_pretrained(model_name),  # Hugging Face
        lambda: torch.hub.load('pytorch/vision', model_name),  # PyTorch Hub
        lambda: load_from_s3(f"models/{model_name}"),  # Your backup
    ]
    
    for source in sources:
        try:
            return source()
        except Exception as e:
            logger.warning(f"Source failed: {e}")
    
    raise RuntimeError("All model sources unavailable")

3. Switch to Local Inference

If using Hugging Face's Inference API, have a local inference fallback:

Quick local server with FastAPI:

from fastapi import FastAPI
from transformers import pipeline

app = FastAPI()
generator = pipeline('text-generation', model='gpt2')

@app.post("/generate")
async def generate(prompt: str):
    return generator(prompt, max_length=100)

Costs vs. benefits:

  • Higher infrastructure costs (GPU instances)
  • Full control and reliability
  • No rate limits
  • Lower latency for some use cases

4. Implement Robust Retry Logic

Exponential backoff for downloads:

import time
from transformers import AutoModel

def download_model_with_retry(model_name, max_retries=5):
    for attempt in range(max_retries):
        try:
            return AutoModel.from_pretrained(model_name)
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            wait_time = 2 ** attempt  # 1, 2, 4, 8, 16 seconds
            logger.warning(f"Download failed, retrying in {wait_time}s: {e}")
            time.sleep(wait_time)

For Inference API:

import requests
from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(5),
    wait=wait_exponential(multiplier=1, min=2, max=60)
)
def call_inference_api(model, payload):
    response = requests.post(
        f"https://api-inference.huggingface.co/models/{model}",
        headers={"Authorization": f"Bearer {API_TOKEN}"},
        json=payload
    )
    response.raise_for_status()
    return response.json()

5. Queue and Defer Non-Critical Operations

When Hugging Face is down, queue non-critical ML operations:

from celery import Celery

app = Celery('ml_tasks')

@app.task(bind=True, max_retries=10)
def process_with_model(self, data):
    try:
        model = load_huggingface_model()
        return model.predict(data)
    except HuggingFaceDownError:
        # Retry in 5 minutes
        raise self.retry(countdown=300)

What to defer:

  • Batch inference jobs
  • Model evaluation
  • Dataset preprocessing
  • Non-user-facing predictions

What NOT to defer:

  • User-facing features
  • Real-time inference requests
  • Critical business operations

6. Set Up Comprehensive Monitoring

Monitor multiple signals:

import requests
import time

def check_huggingface_health():
    checks = {
        "hub": "https://huggingface.co",
        "inference": "https://api-inference.huggingface.co",
        "cdn": "https://cdn-lfs.huggingface.co",
    }
    
    for name, url in checks.items():
        try:
            start = time.time()
            response = requests.get(url, timeout=10)
            latency = time.time() - start
            
            if response.status_code != 200 or latency > 5:
                alert(f"Hugging Face {name} degraded")
        except Exception as e:
            alert(f"Hugging Face {name} down: {e}")

Subscribe to alerts:

  • API Status Check alerts - automated monitoring
  • Hugging Face status page notifications
  • Your own synthetic monitoring
  • Error rate tracking in application logs

7. Communicate with Stakeholders

Internal communication:

  • Alert ML engineering team immediately
  • Notify product managers of affected features
  • Update incident channel with status
  • Prepare customer support with FAQs

External communication (if customer-facing):

  • Status banner: "AI features experiencing intermittent issues"
  • Email to affected users (if significant)
  • Social media update (if widespread)
  • Graceful error messages in UI

Example user-facing message:

Our AI assistant is temporarily unavailable due to a third-party service disruption. 
We're monitoring the situation and expect normal service to resume shortly.

8. Post-Outage Recovery Steps

Once Hugging Face is back online:

  1. Clear failed job queues - Process backlogged requests
  2. Re-validate cached models - Ensure local caches are up-to-date
  3. Review logs - Identify which services were affected
  4. Test critical paths - Verify model loading, inference, datasets
  5. Update documentation - Document learnings and improved procedures
  6. Review costs - Calculate compute waste from failed jobs
  7. Improve resilience - Implement additional safeguards based on lessons learned

Frequently Asked Questions

How often does Hugging Face go down?

Hugging Face maintains high availability, typically exceeding 99.9% uptime. Major outages affecting all services are rare (2-4 times per year), though specific components like Spaces or Inference API may experience more frequent brief disruptions. Most enterprise teams experience minimal impact due to proper caching and fallback strategies.

What's the difference between Hugging Face status page and API Status Check?

The official Hugging Face status page (status.huggingface.co) is manually updated by Hugging Face's team during incidents, which can sometimes lag behind actual issues by several minutes. API Status Check performs automated health checks every 60 seconds against live Hugging Face endpoints (Model Hub, Inference API, Spaces), often detecting issues before they're officially reported. Use both for comprehensive monitoring.

Can I use Hugging Face models offline?

Yes! Download models to a local cache and use local_files_only=True to prevent network calls. For complete offline usage, pre-download all required files (model weights, tokenizers, configs) and set the TRANSFORMERS_OFFLINE=1 environment variable. This is essential for air-gapped environments and outage resilience.

Should I self-host models or use Hugging Face Inference API?

Use Inference API when: You're prototyping, have variable load, want zero infrastructure management, or need access to expensive models (70B+ parameters). Cost: ~$0.06-0.60 per 1,000 requests.

Self-host when: You have consistent high load, need guaranteed uptime, require sub-100ms latency, or process sensitive data. Cost: ~$0.50-2.00/hour for GPU instances.

Best approach: Hybrid - use Inference API for development and variable workloads, self-host for production critical paths.

How do I prevent model download failures during deployments?

Best practices:

  1. Build-time downloads: Download models during Docker image build, not container startup
  2. Model registry: Upload models to your own S3/GCS/Azure storage
  3. Private mirrors: Set up internal Hugging Face mirrors for large organizations
  4. Dependency lock: Pin exact model commits (not branches) to ensure consistency
  5. Health checks: Verify model availability before deploying new code

What should I do if my Space isn't loading?

First, check if it's a widespread issue:

  1. Visit apistatuscheck.com/api/huggingface
  2. Try loading other popular Spaces
  3. Check status.huggingface.co

If only your Space is affected:

  • Check Space logs (Settings → Logs)
  • Verify your dependencies aren't broken
  • Restart the Space manually
  • Check for memory/storage limits exceeded
  • Review recent commits for issues

Are there alternatives to Hugging Face for model hosting?

Yes, several alternatives exist for different use cases:

  • Replicate - Easy API for ML models, pay-per-use pricing
  • Modal - Serverless compute for ML workloads
  • AWS SageMaker - Fully managed ML platform
  • GitHub + PyTorch Hub - Open source model distribution
  • Self-hosted: TorchServe, TensorFlow Serving, or custom FastAPI

Most production teams use a combination: Hugging Face for discovery and development, with production inference on dedicated infrastructure.

How can I get notified immediately when Hugging Face has issues?

Multiple notification options:

  • Subscribe to API Status Check alerts for instant notifications via email, Slack, Discord, or webhook
  • Enable notifications on status.huggingface.co
  • Set up custom monitoring with PagerDuty, Datadog, or New Relic
  • Implement application-level error tracking with Sentry or Rollbar
  • Monitor error rates and latency in your observability platform

Best practice: Use multiple layers—external monitoring (API Status Check) + internal application monitoring.

Stay Ahead of Hugging Face Outages

Don't let ML infrastructure issues derail your AI development and production systems. Subscribe to real-time Hugging Face alerts and get notified instantly when issues are detected—before your team notices.

API Status Check monitors Hugging Face 24/7 with:

  • 60-second health checks across Model Hub, Inference API, Spaces, and Datasets
  • Instant alerts via email, Slack, Discord, or webhook
  • Historical uptime tracking and incident reports
  • Multi-API monitoring for your entire AI stack (including OpenAI, Replicate, Modal, and more)

Start monitoring Hugging Face now →

Last updated: February 4, 2026. Hugging Face status information is provided in real-time based on active monitoring. For official incident reports, always refer to status.huggingface.co.

Monitor Your APIs

Check the real-time status of 100+ popular APIs used by developers.

View API Status →