Is RunPod Down? How to Check RunPod Status in Real-Time
Is RunPod Down? How to Check RunPod Status in Real-Time
Quick Answer: To check if RunPod is down, visit apistatuscheck.com/api/runpod for real-time monitoring, or check the official RunPod status channels on Discord and Twitter. Common signs include pod creation failures, GPU unavailability errors, serverless endpoint timeouts, storage mount issues, and billing/credit processing problems.
When your ML training job suddenly stalls or your inference endpoint stops responding, every minute of downtime directly impacts your business. RunPod powers thousands of AI/ML workloads daily with affordable GPU rentals and serverless inference, making any service disruption a critical blocker for developers, researchers, and businesses. Whether you're experiencing pod creation failures, GPU allocation errors, or API timeouts, quickly verifying RunPod's status can save you valuable debugging time and help you make informed decisions about your ML infrastructure.
How to Check RunPod Status in Real-Time
1. API Status Check (Fastest Method)
The quickest way to verify RunPod's operational status is through apistatuscheck.com/api/runpod. This real-time monitoring service:
- Tests actual API endpoints every 60 seconds
- Monitors pod creation and serverless APIs for availability
- Shows response times and latency trends
- Tracks historical uptime over 30/60/90 days
- Provides instant alerts when issues are detected
- Tests multiple regions and GPU availability
Unlike social media updates that rely on community reports, API Status Check performs active health checks against RunPod's production endpoints, giving you the most accurate real-time picture of service availability before your workloads are affected.
2. RunPod Discord Community
RunPod's official Discord server is often the fastest place to get outage confirmations and updates from both the team and community:
- Join at discord.gg/runpod
- Check the #status or #announcements channels
- Search recent messages in #support for similar issues
- Real-time community feedback on regional availability
- Direct communication with RunPod staff during incidents
Pro tip: Enable notifications for the announcements channel to receive immediate updates when incidents are posted.
3. RunPod Twitter/X Account
Follow @runpodio for official status updates and incident communications. During major outages, the RunPod team typically posts updates on Twitter within minutes.
4. Check the RunPod Console
If the RunPod console at runpod.io is experiencing issues, this often indicates broader infrastructure problems:
- Login failures or authentication errors
- Pod list not loading or showing stale data
- GPU availability showing as 0 across all regions
- Storage volumes not appearing
- Billing/credit balance not updating
Dashboard issues often accompany API problems but can also occur independently due to frontend infrastructure issues.
5. Test RunPod API Endpoints Directly
For developers, making a test API call can quickly confirm connectivity and functionality:
import runpod
import os
# Set your API key
runpod.api_key = os.environ.get("RUNPOD_API_KEY")
try:
# Test basic API connectivity
pods = runpod.get_pods()
print(f"✅ RunPod API responding - {len(pods)} pods found")
# Test GPU availability
gpus = runpod.get_gpus()
available = [gpu for gpu in gpus if gpu['available'] > 0]
print(f"✅ {len(available)} GPU types available")
except runpod.error.AuthenticationError:
print("❌ Authentication failed - check your API key")
except runpod.error.APIError as e:
print(f"❌ API Error: {e}")
except Exception as e:
print(f"❌ Connection failed: {e}")
Look for connection timeouts, 500/502/503 HTTP errors, or unusual authentication failures across multiple API keys.
Common RunPod Issues and How to Identify Them
Pod Creation Failures
Symptoms:
- "No available GPUs" error when attempting to create pods
- Pod creation request hangs indefinitely
- Pods stuck in "PENDING" state for extended periods
- 500/503 errors during pod creation API calls
- Web UI showing infinite loading spinner on pod creation
What it means: RunPod's pod orchestration system may be experiencing high load, regional capacity issues, or infrastructure problems. This differs from normal capacity constraints—you'll see failures across multiple GPU types and regions simultaneously.
Quick diagnosis:
import runpod
import time
def test_pod_creation():
"""Test pod creation across multiple GPU types"""
gpu_types = ["NVIDIA RTX A4000", "NVIDIA A40", "NVIDIA RTX 3090"]
results = {}
for gpu_type in gpu_types:
try:
# Attempt to get availability
gpus = runpod.get_gpus()
gpu_info = next((g for g in gpus if g['name'] == gpu_type), None)
if gpu_info and gpu_info['available'] > 0:
results[gpu_type] = "✅ Available"
else:
results[gpu_type] = "⚠️ No capacity"
except Exception as e:
results[gpu_type] = f"❌ Error: {str(e)}"
return results
# Run the test
availability = test_pod_creation()
for gpu, status in availability.items():
print(f"{gpu}: {status}")
GPU Availability Issues
Common scenarios:
- All GPU types showing 0 availability across all regions
- Specific GPU types (e.g., H100, A100) consistently unavailable
- Regional outages (EU pods down, US pods working)
- GPU allocation succeeding but pod failing to initialize
What it indicates:
- Full outage: All GPUs unavailable = infrastructure issue
- Partial outage: Specific regions/types down = datacenter or capacity problem
- Capacity crunch: Gradual unavailability during peak hours = normal demand (not an outage)
Monitoring strategy:
import runpod
from datetime import datetime
def monitor_gpu_availability(interval_seconds=60):
"""Continuously monitor GPU availability"""
while True:
try:
gpus = runpod.get_gpus()
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
total_available = sum(gpu.get('available', 0) for gpu in gpus)
if total_available == 0:
print(f"[{timestamp}] ⚠️ ALERT: Zero GPUs available across all types")
# Send alert to your monitoring system
send_alert("RunPod GPU availability critical")
else:
print(f"[{timestamp}] ✅ {total_available} total GPUs available")
time.sleep(interval_seconds)
except Exception as e:
print(f"[{timestamp}] ❌ API Error: {e}")
time.sleep(interval_seconds)
# Run monitoring
monitor_gpu_availability()
Network and Storage Mount Problems
Symptoms:
- Pods starting but network volumes failing to mount
- SSH connectivity issues to running pods
- Persistent storage showing as unmounted or inaccessible
- File upload/download failures
- Container registry pull timeouts
Network-specific issues:
# Test from within a pod
curl -I https://huggingface.co # Test external connectivity
curl -I https://runpod.io # Test RunPod infrastructure
ping -c 4 8.8.8.8 # Test basic internet
# Check storage mounts
df -h # Show mounted volumes
ls -la /workspace # Check workspace accessibility
Common causes during outages:
- RunPod's network infrastructure experiencing routing issues
- Storage backend degradation or maintenance
- Regional internet connectivity problems
- Internal DNS resolution failures
Billing and Credit Issues
Symptoms:
- Credit balance not updating after purchase
- Pods terminating unexpectedly due to "insufficient credits"
- Payment processing failures
- Billing API endpoints returning errors
- Credit deductions not reflecting actual usage
What to check:
import runpod
try:
# Check account balance
user = runpod.get_user()
balance = user.get('credits', 'Unknown')
print(f"Current balance: ${balance}")
# Check recent spending
pods = runpod.get_pods()
for pod in pods:
print(f"Pod {pod['id']}: ${pod.get('cost_per_hour', 0)}/hr")
except runpod.error.APIError as e:
print(f"❌ Billing API error: {e}")
print("This may indicate RunPod billing system issues")
During billing outages:
- New pod creation may be blocked
- Existing pods should continue running (grace period)
- Credit purchases may fail or show pending indefinitely
- Usage tracking may be delayed
Serverless Endpoint Timeouts
Symptoms:
- RunPod Serverless endpoints returning 504 Gateway Timeout
- Requests taking 60+ seconds with no response
- Cold start times significantly longer than normal (10+ minutes)
- Worker scaling not responding to load
- Endpoint showing as "INITIALIZING" indefinitely
Diagnostic code:
import requests
import time
def test_serverless_endpoint(endpoint_id, api_key):
"""Test serverless endpoint health"""
url = f"https://api.runpod.ai/v2/{endpoint_id}/run"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
payload = {
"input": {
"prompt": "test",
"num_inference_steps": 1 # Minimal workload
}
}
start_time = time.time()
try:
response = requests.post(url, json=payload, headers=headers, timeout=30)
elapsed = time.time() - start_time
if response.status_code == 200:
print(f"✅ Endpoint responding ({elapsed:.2f}s)")
return True
else:
print(f"❌ HTTP {response.status_code}: {response.text}")
return False
except requests.exceptions.Timeout:
print(f"❌ Request timeout after 30s")
return False
except Exception as e:
print(f"❌ Connection error: {e}")
return False
# Test your endpoint
test_serverless_endpoint("your-endpoint-id", "your-api-key")
Common serverless issues during outages:
- Worker orchestration system degraded
- Container registry availability issues
- Auto-scaling not triggering properly
- Regional serverless capacity exhausted
The Real Impact When RunPod Goes Down
Halted ML Training Jobs
Every minute of RunPod downtime directly impacts active machine learning workloads:
- Research projects: Multi-day training runs interrupted, requiring checkpoints and restarts
- Production model development: Fine-tuning jobs for customer-facing models blocked
- Academic deadlines: PhD students and researchers facing paper submission deadlines
- Startup development: Small ML teams relying on RunPod for affordable GPU access
Cost calculation: A researcher training a large language model on 8×H100 GPUs at $13/GPU/hour faces $104/hour in lost compute time during an outage. A 4-hour outage equals $416 in wasted budget or delayed deliverables.
Broken Inference Pipelines
Production AI applications built on RunPod Serverless face immediate customer impact:
- AI SaaS products: Image generation, text-to-speech, LLM inference endpoints down
- API-first businesses: Customers' applications failing due to unavailable inference
- Mobile apps: In-app AI features returning errors to end users
- Real-time systems: Chatbots, content moderation, recommendation engines offline
Example: An AI image generation startup processing 10,000 API requests per hour at $0.50 average revenue per request loses $5,000 in hourly revenue during a complete RunPod outage.
Development and Testing Blocked
Even non-production workloads create significant delays:
- Model experimentation: Data scientists unable to test new architectures
- Integration testing: Developers blocked from testing RunPod-dependent features
- Demo preparation: Sales teams unable to prepare customer demonstrations
- Onboarding: New team members unable to provision development environments
Competitive Disadvantage
In the fast-moving AI space, infrastructure reliability directly impacts competitive position:
- Time-to-market delays: Product launches pushed back due to training delays
- Customer churn: Production users switching to competitors like Modal, Replicate, or Lambda Labs
- Reputation damage: Social media complaints about unreliable infrastructure
- Lost opportunities: Unable to capitalize on trending AI applications
Cascading Infrastructure Failures
For businesses deeply integrated with RunPod:
- Automated training pipelines stalled, backing up data processing queues
- Monitoring systems generating false alerts due to expected pods being unavailable
- Cost optimization workflows disrupted (can't scale down/up as planned)
- Multi-cloud strategies impacted if RunPod is primary provider
Incident Response Playbook for RunPod Outages
1. Implement Robust Health Checks and Retries
Connection retry with exponential backoff:
import runpod
import time
from functools import wraps
def retry_with_backoff(max_retries=5, base_delay=2, max_delay=60):
"""Decorator for retrying RunPod API calls with exponential backoff"""
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
for attempt in range(max_retries):
try:
return func(*args, **kwargs)
except (runpod.error.APIError,
runpod.error.APIConnectionError) as e:
if attempt == max_retries - 1:
raise
delay = min(base_delay * (2 ** attempt), max_delay)
print(f"⚠️ Attempt {attempt + 1} failed: {e}")
print(f"Retrying in {delay}s...")
time.sleep(delay)
return None
return wrapper
return decorator
@retry_with_backoff(max_retries=3)
def create_pod_with_retry(name, gpu_type, image):
"""Create a pod with automatic retry logic"""
pod = runpod.create_pod(
name=name,
image_name=image,
gpu_type_id=gpu_type,
cloud_type="SECURE",
volume_in_gb=50
)
return pod
# Usage
try:
pod = create_pod_with_retry(
name="training-job-001",
gpu_type="NVIDIA A40",
image="runpod/pytorch:2.0.0-py3.10-cuda11.8.0-devel"
)
print(f"✅ Pod created: {pod['id']}")
except Exception as e:
print(f"❌ Pod creation failed after retries: {e}")
# Implement fallback strategy
2. Save Training Checkpoints Aggressively
When RunPod availability is uncertain, checkpoint more frequently:
import torch
import os
from datetime import datetime
class CheckpointManager:
"""Aggressive checkpointing for RunPod environments"""
def __init__(self, checkpoint_dir="/workspace/checkpoints"):
self.checkpoint_dir = checkpoint_dir
os.makedirs(checkpoint_dir, exist_ok=True)
def save_checkpoint(self, model, optimizer, epoch, metrics):
"""Save model checkpoint with metadata"""
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
checkpoint_path = os.path.join(
self.checkpoint_dir,
f"checkpoint_epoch_{epoch}_{timestamp}.pt"
)
checkpoint = {
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'metrics': metrics,
'timestamp': timestamp
}
torch.save(checkpoint, checkpoint_path)
print(f"💾 Checkpoint saved: {checkpoint_path}")
# Also save to external storage (S3, HuggingFace Hub)
self.backup_to_cloud(checkpoint_path)
def backup_to_cloud(self, local_path):
"""Backup checkpoint to cloud storage"""
# Upload to S3, HuggingFace Hub, or other external storage
# This ensures checkpoints survive pod termination
pass
# Usage in training loop
checkpoint_mgr = CheckpointManager()
for epoch in range(num_epochs):
# Training code...
# Checkpoint every N steps (not just epochs)
if step % 100 == 0:
checkpoint_mgr.save_checkpoint(
model=model,
optimizer=optimizer,
epoch=epoch,
metrics={'loss': loss.item(), 'step': step}
)
Why this matters: If RunPod experiences an outage and your pod terminates unexpectedly, you'll lose minimal progress. Frequent checkpoints to external storage (not just pod volumes) ensure recoverability.
3. Implement Multi-Cloud Fallback Strategy
Don't put all your GPUs in one basket:
from enum import Enum
import os
class GPUProvider(Enum):
RUNPOD = "runpod"
MODAL = "modal"
REPLICATE = "replicate"
LAMBDALABS = "lambdalabs"
class MultiCloudGPUManager:
"""Automatically failover between GPU providers"""
def __init__(self, preferred_provider=GPUProvider.RUNPOD):
self.preferred_provider = preferred_provider
self.providers = {
GPUProvider.RUNPOD: self._create_runpod_job,
GPUProvider.MODAL: self._create_modal_job,
# Add other providers
}
def create_training_job(self, config):
"""Create training job with automatic failover"""
providers_to_try = [self.preferred_provider] + [
p for p in GPUProvider if p != self.preferred_provider
]
for provider in providers_to_try:
try:
print(f"Attempting to create job on {provider.value}...")
result = self.providers[provider](config)
print(f"✅ Job created on {provider.value}")
return result
except Exception as e:
print(f"❌ {provider.value} failed: {e}")
continue
raise Exception("All GPU providers failed")
def _create_runpod_job(self, config):
"""Create RunPod training job"""
import runpod
return runpod.create_pod(**config)
def _create_modal_job(self, config):
"""Create Modal training job"""
# Modal implementation
pass
# Usage
gpu_manager = MultiCloudGPUManager(preferred_provider=GPUProvider.RUNPOD)
try:
job = gpu_manager.create_training_job({
'name': 'training-job',
'gpu_type': 'A100',
'image': 'pytorch:latest'
})
except Exception as e:
print(f"Failed to create job on any provider: {e}")
Integration suggestions:
- Use Modal for serverless GPU jobs
- Deploy to Replicate for inference endpoints
- Leverage HuggingFace Spaces for demos and smaller models
4. Queue Jobs Instead of Failing Immediately
When RunPod is temporarily unavailable, queue jobs for later execution:
import redis
import json
from datetime import datetime
class JobQueue:
"""Queue ML jobs during RunPod outages"""
def __init__(self, redis_url="redis://localhost:6379"):
self.redis_client = redis.from_url(redis_url)
self.queue_key = "runpod_job_queue"
def enqueue_job(self, job_config):
"""Add job to queue"""
job = {
'id': f"job_{datetime.now().timestamp()}",
'config': job_config,
'queued_at': datetime.now().isoformat(),
'status': 'pending'
}
self.redis_client.lpush(self.queue_key, json.dumps(job))
print(f"📋 Job queued: {job['id']}")
return job['id']
def process_queue(self):
"""Process queued jobs when RunPod is back"""
while True:
job_json = self.redis_client.rpop(self.queue_key)
if not job_json:
break
job = json.loads(job_json)
try:
# Attempt to create RunPod pod
pod = runpod.create_pod(**job['config'])
print(f"✅ Queued job {job['id']} started: {pod['id']}")
except Exception as e:
print(f"❌ Job {job['id']} failed: {e}")
# Re-queue or handle error
self.redis_client.lpush(self.queue_key, job_json)
break
# Usage
queue = JobQueue()
# When RunPod is down
if not is_runpod_available():
job_id = queue.enqueue_job({
'name': 'deferred-training',
'gpu_type': 'A100',
'image': 'pytorch:latest'
})
notify_user(f"Training job queued (ID: {job_id}). Will start when GPUs become available.")
else:
# Create pod immediately
pod = runpod.create_pod(...)
5. Monitor Proactively and Alert Early
Set up comprehensive monitoring before outages occur:
import requests
import time
from datetime import datetime
class RunPodMonitor:
"""Proactive RunPod availability monitoring"""
def __init__(self, api_key, alert_webhook):
self.api_key = api_key
self.alert_webhook = alert_webhook
self.consecutive_failures = 0
self.alert_threshold = 3
def check_health(self):
"""Perform health check"""
checks = {
'api_reachable': self._test_api_connectivity(),
'gpus_available': self._test_gpu_availability(),
'pod_creation': self._test_pod_creation(),
}
all_healthy = all(checks.values())
if not all_healthy:
self.consecutive_failures += 1
if self.consecutive_failures >= self.alert_threshold:
self._send_alert(checks)
else:
self.consecutive_failures = 0
return checks
def _test_api_connectivity(self):
"""Test if RunPod API is reachable"""
try:
response = requests.get(
"https://api.runpod.io/graphql",
timeout=10
)
return response.status_code == 200
except:
return False
def _test_gpu_availability(self):
"""Test if any GPUs are available"""
try:
import runpod
runpod.api_key = self.api_key
gpus = runpod.get_gpus()
total_available = sum(g.get('available', 0) for g in gpus)
return total_available > 0
except:
return False
def _test_pod_creation(self):
"""Test pod creation API (without actually creating)"""
# Implement validation-only check
return True
def _send_alert(self, failed_checks):
"""Send alert to monitoring system"""
alert = {
'timestamp': datetime.now().isoformat(),
'service': 'RunPod',
'status': 'degraded',
'failed_checks': [k for k, v in failed_checks.items() if not v],
'consecutive_failures': self.consecutive_failures
}
requests.post(self.alert_webhook, json=alert)
print(f"🚨 ALERT SENT: RunPod health check failed")
# Run monitoring loop
monitor = RunPodMonitor(
api_key=os.environ['RUNPOD_API_KEY'],
alert_webhook="https://your-monitoring-system.com/webhook"
)
while True:
health = monitor.check_health()
print(f"[{datetime.now()}] Health: {health}")
time.sleep(60) # Check every minute
Subscribe to monitoring services:
- API Status Check for RunPod - automated 24/7 monitoring
- RunPod Discord notifications
- Your own synthetic monitoring
- Application error rate tracking
6. Communicate with Stakeholders
Internal communication template:
🚨 RunPod Service Disruption Detected
Status: [Investigating / Confirmed Outage / Recovering]
Impact: [Pod creation failing / GPU allocation blocked / Serverless endpoints timeout]
Affected: [All regions / EU only / Specific GPU types]
Actions Taken:
- Switched to job queuing mode
- Activated backup provider (Modal)
- Notified customers of potential delays
ETA: [Monitoring for resolution / No ETA available]
Updates: This channel + #engineering
Customer communication (for B2B AI services):
We're currently experiencing delays in our AI processing pipeline due to infrastructure provider issues. Your requests are queued and will be processed automatically once service is restored (typically within 1-2 hours). No action needed on your part.
Frequently Asked Questions
How often does RunPod experience outages?
RunPod maintains strong overall reliability, but as a rapidly growing GPU cloud platform, occasional capacity constraints and infrastructure issues do occur. Major outages affecting all customers are relatively rare (3-6 times per year), but regional or GPU-specific availability issues are more common during peak demand periods. Most users experience 99%+ effective uptime.
What's the difference between "out of capacity" and "RunPod down"?
Out of capacity (not an outage): Specific GPU types unavailable in certain regions due to high demand. The platform is functioning normally, but popular GPUs (H100, A100) may be fully allocated. This is expected behavior during peak hours.
RunPod down (outage): API errors, pod creation failures across all GPU types, existing pods terminating unexpectedly, or complete service unavailability. This indicates infrastructure problems requiring RunPod team intervention.
Should I use RunPod for production ML inference?
RunPod Serverless is increasingly used for production inference, but consider these factors:
Pros:
- Extremely cost-effective compared to AWS/GCP
- Auto-scaling for variable loads
- Wide GPU selection for different model sizes
Cons:
- Less mature than enterprise providers
- Smaller team = potentially slower incident response
- Less comprehensive SLA and support options
Best practice: Use RunPod for cost-sensitive production workloads with fallback to more expensive but more reliable providers (Modal, Replicate, or AWS Sagemaker) for critical requests.
How do I get notified of RunPod outages?
Multiple notification options:
- API Status Check: Monitor RunPod 24/7 with instant alerts via email, Slack, Discord, or webhook
- RunPod Discord: Join official Discord and enable notifications for #announcements
- Twitter/X: Follow @runpodio with tweet notifications enabled
- Custom monitoring: Implement health checks in your application (see Incident Response section)
Can I get a refund for time lost during RunPod outages?
RunPod's standard terms don't explicitly guarantee SLA credits for downtime. However:
- You're not charged for pods that fail to start
- If a running pod becomes inaccessible due to RunPod infrastructure issues, you can request credit via support
- Enterprise customers may have custom SLA agreements with guaranteed uptime
Contact RunPod support (support@runpod.io or Discord) with specific outage details and usage logs for case-by-case review.
What GPU types are most reliable on RunPod?
Reliability generally correlates with availability:
Most reliable:
- RTX A4000, A5000 (consumer/prosumer GPUs, good availability)
- RTX 3090, 4090 (good stock, popular for development)
Moderate availability:
- A40, A6000 (enterprise GPUs, moderate capacity)
- L40, L4 (newer, growing availability)
Often capacity-constrained:
- H100, A100 (highest demand, lowest availability)
- MI300X (limited supply)
For production workloads, design your infrastructure to work across multiple GPU types rather than depending on a specific model.
How does RunPod compare to Modal, Replicate, and Lambda Labs?
RunPod:
- ✅ Most cost-effective
- ✅ Widest GPU selection
- ✅ Flexible (pods + serverless)
- ⚠️ Capacity can be tight
- ⚠️ Less mature than enterprise providers
- ✅ Best developer experience
- ✅ Excellent for serverless Python
- ✅ Strong reliability
- ❌ More expensive
- ❌ Less GPU variety
- ✅ Best for inference APIs
- ✅ Pre-built model library
- ✅ Simple usage-based pricing
- ❌ Not ideal for training
- ❌ Limited customization
Lambda Labs:
- ✅ Good availability of high-end GPUs
- ✅ Bare metal performance
- ❌ More expensive than RunPod
- ❌ Less flexible (longer commitments)
Should I checkpoint my model during every RunPod training run?
Absolutely yes. Best practices:
- Checkpoint frequently: Every 100-500 training steps (not just per epoch)
- Save to external storage: Don't rely solely on pod volumes (upload to S3, HuggingFace Hub, etc.)
- Keep multiple checkpoints: Last 3-5 checkpoints + best performing checkpoint
- Include optimizer state: Full checkpoint should allow seamless resumption
- Test recovery: Periodically verify you can actually resume from checkpoints
RunPod pods can be interrupted by capacity constraints, spot instance preemption (if using Community Cloud), or infrastructure issues. Aggressive checkpointing is cheap insurance.
What should I do if RunPod has been down for hours?
Immediate actions:
- Switch providers: Move critical workloads to Modal, Replicate, or Lambda Labs
- Process your queue: If you implemented job queuing, run queued jobs on alternative infrastructure
- Communicate: Update stakeholders/customers about delays and mitigation plans
- Document: Log the incident, impact, and your response for future improvement
Recovery actions:
- Resume training: Restore from checkpoints once RunPod is back
- Review architecture: Consider if you need better multi-cloud resilience
- Update monitoring: Add alerts you wish you'd had during the incident
- Provide feedback: Share your experience in RunPod Discord to help improve service
Long-term considerations:
- Evaluate whether RunPod's price savings justify the reliability risk for your use case
- Implement the multi-cloud patterns described in the Incident Response section
- Build relationships with RunPod team (especially if you're a large customer)
Is there an official RunPod status page?
As of early 2026, RunPod does not maintain a traditional status.runpod.io page. Status updates are primarily communicated through:
- Discord #announcements and #status channels
- Twitter/X (@runpodio)
- Direct support communications
Third-party monitoring like API Status Check fills this gap by providing automated, real-time status information independent of RunPod's internal communications.
Stay Ahead of RunPod Outages
Don't let GPU infrastructure issues derail your ML projects. Subscribe to real-time RunPod monitoring and get notified instantly when issues are detected—before your training jobs fail or inference endpoints timeout.
API Status Check monitors RunPod 24/7 with:
- 60-second health checks for pod creation and serverless APIs
- GPU availability tracking across all regions
- Instant alerts via email, Slack, Discord, or webhook
- Historical uptime tracking and incident reports
- Multi-platform monitoring for your entire ML infrastructure stack
Build ML infrastructure resilience: Also monitor Modal, Replicate, HuggingFace, and other critical AI/ML services with one monitoring dashboard.
Last updated: February 4, 2026. RunPod status information is provided based on real-time monitoring and community reports. For official incident communications, refer to RunPod's Discord server and Twitter/X account.
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