Is Modal Down? How to Check Modal Status & Fix Common Issues

Quick Answer: To check if Modal is down, visit apistatuscheck.com/api/modal for real-time monitoring, or check status.modal.com for official updates. Common indicators include function cold start delays exceeding 60 seconds, GPU queue timeouts, container build failures with registry errors, and webhook delivery failures.

When your AI inference endpoint suddenly stops responding or your ML batch job hangs indefinitely, every minute of downtime impacts your business. Modal powers serverless GPU workloads for thousands of AI developers, from real-time inference APIs to large-scale training pipelines. Whether you're experiencing function deployment failures, GPU unavailability, or mysterious container errors, knowing how to quickly diagnose Modal's operational status can save hours of debugging and help you make informed decisions about your infrastructure.

How to Check Modal Status in Real-Time

1. API Status Check (Fastest Method)

The fastest way to verify Modal's operational status is through apistatuscheck.com/api/modal. This real-time monitoring service:

• Tests actual Modal API endpoints every 60 seconds
• Monitors function deployment times and cold start latency
• Tracks GPU availability across different instance types (A100, H100, T4)
• Shows response times and historical uptime trends
• Provides instant alerts when degraded performance is detected
• Monitors multiple regions (US East, US West, EU)

Unlike status pages that depend on manual updates from the Modal team, API Status Check performs active health checks against Modal's production infrastructure, giving you the most accurate real-time picture of service availability—often detecting issues before they're officially reported.

2. Official Modal Status Page

Modal maintains status.modal.com as their official communication channel for service incidents. The page displays:

• Current operational status for all services
• Active incidents and ongoing investigations
• GPU capacity availability by region
• Scheduled maintenance windows
• Historical incident reports with root cause analysis
• Component-specific status (API, GPU Workers, Container Registry, Volumes, Webhooks)

Pro tip: Subscribe to status updates via email or Slack on the status page to receive immediate notifications when incidents are declared or capacity issues arise.

3. Modal Dashboard Monitoring

If the Modal dashboard at modal.com/apps is showing errors or degraded performance, this often indicates broader infrastructure issues:

• Function deployment stuck in "Building" state for >5 minutes
• App list failing to load or showing stale data
• Logs not streaming in real-time
• Settings or secrets management timing out
• Billing page errors or API token generation failures

Navigate to your app's logs viewer—if logs from running functions aren't appearing or show significant delays (>30 seconds), this typically indicates event streaming issues on Modal's backend.

4. Test Modal Functions Directly

For developers, deploying a minimal test function can quickly confirm end-to-end functionality:

import modal

stub = modal.Stub("health-check")

@stub.function()
def test_function():
    """Minimal health check function"""
    return {"status": "ok", "timestamp": modal.functions.get_task_id()}

if __name__ == "__main__":
    with stub.run():
        result = test_function.remote()
        print(f"Modal health check: {result}")

Run this with modal run health_check.py. A healthy Modal deployment completes in 15-30 seconds (first run) or 2-5 seconds (warm function). Look for:

• Deployment timeouts (>2 minutes for cold start)
• Import errors not related to your code
• ConnectionError or TimeoutError exceptions
• HTTP 502/503/504 errors from Modal's API gateway

5. Check Modal Community Channels

The Modal community often reports issues before official status updates:

• Modal Slack workspace (modal-labs.slack.com) - #support channel
• Modal Discord server - Real-time community discussion
• Twitter/X @modal_labs - Official announcements and updates
• GitHub Issues (github.com/modal-labs/modal-client) - Known bugs and workarounds

If multiple users are reporting similar issues simultaneously, it's likely a platform-wide problem rather than a configuration issue in your code.

Common Modal Issues and How to Identify Them

Function Cold Start Delays

Symptoms:

• Function deployment stuck on "Building image..." for >3 minutes
• First invocation after idle period takes >60 seconds
• Intermittent 504 Gateway Timeout errors on function calls
• Function containers starting then immediately dying

What it means: Modal's cold start process involves pulling container images, allocating GPU resources, and initializing your Python environment. Normal cold starts take 15-45 seconds depending on image size and dependencies. Extended cold start times (>2 minutes) usually indicate:

• Container registry issues (Docker image pull failures)
• GPU resource exhaustion (no available A100/H100 instances)
• Image layer download bottlenecks
• Python package installation timeouts during environment setup

How to diagnose:

import modal
import time

stub = modal.Stub("cold-start-test")

@stub.function(gpu="T4", timeout=300)
def measure_cold_start():
    """Measure actual cold start time"""
    start = time.time()
    import torch  # Heavy import for timing
    elapsed = time.time() - start
    return {
        "cold_start_seconds": elapsed,
        "gpu_available": torch.cuda.is_available(),
        "gpu_name": torch.cuda.get_device_name(0) if torch.cuda.is_available() else None
    }

If this function consistently takes >90 seconds or fails with timeout errors, Modal's GPU provisioning is likely degraded.

GPU Queue Delays

Symptoms:

• Functions with gpu="A100" or gpu="H100" stuck in queue for >5 minutes
• "Waiting for GPU resources..." message appearing in logs
• Successful function runs followed by immediate queue delays
• GPU-enabled functions working in one region but failing in another

What it means: Modal dynamically allocates GPU instances from cloud providers. During high-demand periods or capacity shortages:

• Popular GPU types (A100-80GB, H100) may have limited availability
• Specific regions may run out of GPU capacity
• Bulk job submissions can exhaust available GPU pools
• Provider-side capacity issues (AWS, GCP spot instance availability)

Workaround strategies:

import modal

stub = modal.Stub("gpu-fallback")

# Try A100 first, fall back to A10G if unavailable
@stub.function(
    gpu=modal.gpu.A100(),  # Preferred GPU
    timeout=600,
    retries=2
)
def inference_primary(input_data):
    return run_model(input_data)

@stub.function(
    gpu=modal.gpu.A10G(),  # Fallback GPU
    timeout=600
)
def inference_fallback(input_data):
    return run_model(input_data)

def smart_inference(input_data):
    """Try A100, fall back to A10G if queue is long"""
    try:
        return inference_primary.remote(input_data)
    except modal.exception.TimeoutError:
        print("A100 queue timeout, falling back to A10G")
        return inference_fallback.remote(input_data)

Container Build Failures

Symptoms:

• modal deploy failing with "Failed to build image" errors
• ImageBuildError exceptions during function deployment
• Docker layer push failures: "error writing blob" or "unexpected EOF"
• pip install timeouts during image build process
• Missing system dependencies in deployed container vs. local environment

What it means: Modal builds container images from your Python requirements and Dockerfile definitions. Build failures typically indicate:

• Modal's container registry having capacity or connectivity issues
• PyPI/package registry timeouts when installing dependencies
• Large image layers (>5GB) timing out during push
• Incompatible dependency versions for Modal's base images
• System package installation failures (apt-get, yum errors)

Diagnostic approach:

import modal

stub = modal.Stub("build-test")

# Minimal image to test registry connectivity
minimal_image = modal.Image.debian_slim().pip_install("numpy")

# Full image with all dependencies
production_image = (
    modal.Image.debian_slim()
    .pip_install("torch", "transformers", "accelerate")
    .apt_install("ffmpeg", "libsm6", "libxext6")
)

@stub.function(image=minimal_image)
def test_minimal():
    """Test if basic image builds work"""
    import numpy as np
    return f"Minimal image OK: numpy {np.__version__}"

@stub.function(image=production_image)
def test_full():
    """Test if full image builds work"""
    import torch
    return f"Full image OK: torch {torch.__version__}"

Run both functions. If test_minimal() succeeds but test_full() fails, the issue is likely with specific package downloads rather than Modal's core registry infrastructure.

Volume Mount Issues

Symptoms:

• Functions failing with VolumeNotFound or VolumeNotMounted errors
• File I/O operations timing out when reading/writing to volumes
• Volume data appearing empty or stale (not reflecting recent writes)
• Concurrent writes to shared volumes causing corruption or locks
• Volume quota exceeded errors despite deleting files

What it means: Modal Volumes provide persistent storage across function invocations. Issues typically stem from:

• Volume service degradation (network file system performance)
• Synchronization delays between function instances and volume backend
• Concurrent access conflicts when multiple functions write simultaneously
• Volume snapshot/backup operations blocking I/O
• Quota enforcement issues or filesystem corruption

Best practices for resilient volume usage:

import modal
from pathlib import Path
import time

stub = modal.Stub("volume-resilient")
volume = modal.Volume.from_name("my-data-volume", create_if_missing=True)

@stub.function(volumes={"/data": volume}, timeout=600)
def safe_volume_write(filename, content):
    """Write to volume with retry logic"""
    volume_path = Path("/data") / filename
    
    for attempt in range(3):
        try:
            # Ensure parent directory exists
            volume_path.parent.mkdir(parents=True, exist_ok=True)
            
            # Write with atomic rename pattern
            temp_path = volume_path.with_suffix('.tmp')
            temp_path.write_text(content)
            temp_path.rename(volume_path)
            
            # Explicit commit to volume
            volume.commit()
            
            return {"status": "success", "path": str(volume_path)}
        
        except (IOError, OSError) as e:
            if attempt == 2:
                raise
            print(f"Volume write failed (attempt {attempt + 1}), retrying...")
            time.sleep(2 ** attempt)  # Exponential backoff

Webhook Delivery Failures

Symptoms:

• Webhook endpoints not receiving Modal function completion events
• Significant delays (minutes to hours) in webhook delivery
• Missing webhook signatures or authentication headers
• Duplicate webhook deliveries for the same event
• Webhooks failing silently with no retry attempts

What it means: Modal can send webhooks when functions complete, fail, or reach specific states. Delivery issues suggest:

• Modal's webhook dispatch service experiencing backlogs
• Network connectivity issues between Modal and your webhook endpoint
• Webhook retry queue exhaustion during prolonged outages
• Webhook validation failures on Modal's side

Webhook reliability patterns:

import modal
import hmac
import hashlib

stub = modal.Stub("webhook-handler")

@stub.webhook(method="POST")
def handle_webhook(request_body: dict):
    """Receive webhooks from Modal with validation"""
    
    # Validate webhook signature (if Modal provides one)
    signature = request_body.get('signature')
    expected = hmac.new(
        key=modal.Secret.from_name("webhook-secret").value.encode(),
        msg=request_body.get('payload').encode(),
        digestmod=hashlib.sha256
    ).hexdigest()
    
    if signature != expected:
        return {"error": "Invalid signature"}, 401
    
    # Process webhook with idempotency
    event_id = request_body.get('event_id')
    if already_processed(event_id):
        return {"status": "duplicate", "event_id": event_id}, 200
    
    # Handle the event
    process_event(request_body)
    mark_processed(event_id)
    
    return {"status": "success"}, 200

Alternative pattern if webhooks are unreliable:

import modal

stub = modal.Stub("polling-pattern")

@stub.function(schedule=modal.Period(seconds=60))
def poll_completed_jobs():
    """Poll for completed jobs instead of relying on webhooks"""
    # Query Modal API for recently completed functions
    # Process results that haven't been handled yet
    pass

The Real Impact When Modal Goes Down

ML Pipeline Failures

Every minute of Modal downtime cascades through your ML infrastructure:

• Training pipelines: Multi-hour training jobs interrupted mid-batch, losing GPU hours
• Data processing: ETL pipelines for embedding generation or dataset preprocessing stalled
• Model fine-tuning: Hyperparameter sweeps stuck waiting for GPU availability
• Evaluation workflows: Batch inference jobs for model benchmarking blocked

For a team running continuous training pipelines at $50/hour in GPU costs, a 4-hour outage means $200 in wasted compute plus the opportunity cost of delayed model iterations.

Inference Endpoint Downtime

Production inference APIs built on Modal face immediate customer impact:

• Real-time inference APIs: Customer-facing features (chatbots, image generation, voice synthesis) go offline
• Synchronous predictions: Timeout errors propagate to user-facing applications
• Batch inference jobs: Overnight processing jobs for business intelligence fail to complete
• A/B testing: Experiment traffic cannot reach treatment model variants

For a product serving 1,000 inference requests per minute at $0.01 each, a 1-hour outage means $600 in lost revenue plus potential SLA breach penalties.

Serverless Job Queue Backlog

Modal's queueing system buffers work during normal operation, but outages create cascading problems:

• Queue buildup: Thousands of pending jobs accumulate during downtime
• Thundering herd: All queued jobs attempt to execute simultaneously when service resumes
• Resource exhaustion: Sudden spike overwhelms downstream services (databases, APIs)
• Failed retries: Jobs exceeding maximum retry counts fail permanently
• Data staleness: Time-sensitive predictions become irrelevant if delayed hours

Recovery can take 2-3x the outage duration as the system processes backlogged work.

GPU Cost Implications

Modal's billing model means outages have complex cost implications:

• Billed for queued time: Functions waiting in GPU queue may still incur minimal charges
• Failed job costs: Functions that start but fail due to infra issues still consume billable seconds
• Retry amplification: Automatic retries multiply costs during intermittent failures
• Idle GPU charges: Functions stuck in "running" state but not actually executing

During outages, closely monitor your Modal usage dashboard to ensure you're not being charged for failed infrastructure.

Developer Productivity Loss

Beyond direct financial impact, outages disrupt your team:

• Engineers spend hours debugging what appear to be code issues but are actually platform problems
• Deployment pipelines blocked, preventing hotfixes or feature releases
• On-call engineers paged unnecessarily for infrastructure issues outside their control
• Context switching as team pivots to incident response mode

A 2-hour outage can easily consume 10-20 engineer-hours across a team when factoring in investigation time.

Customer Trust and SLA Violations

For businesses offering AI products powered by Modal:

• Customer-facing APIs returning 5xx errors damage reliability reputation
• SLA credits owed to enterprise customers for downtime
• Support ticket floods as customers report issues
• Potential churn if reliability becomes a pattern
• Competitive disadvantage if rivals offer more reliable AI infrastructure

While Modal's overall reliability is strong, even rare outages can trigger customer contract reviews and vendor diversification discussions.

Incident Response Playbook for Modal Outages

1. Implement Smart Retry Logic with Backoff

Modal functions should handle transient failures gracefully:

import modal
import time
from functools import wraps

def retry_with_backoff(max_retries=3, initial_delay=1, backoff_factor=2):
    """Decorator for retrying Modal function calls"""
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            delay = initial_delay
            last_exception = None
            
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except (modal.exception.TimeoutError, 
                        modal.exception.FunctionTimeoutError,
                        modal.exception.ExecutionError) as e:
                    last_exception = e
                    if attempt == max_retries - 1:
                        raise
                    
                    print(f"Attempt {attempt + 1} failed: {e}. Retrying in {delay}s...")
                    time.sleep(delay)
                    delay *= backoff_factor
            
            raise last_exception
        return wrapper
    return decorator

stub = modal.Stub("resilient-inference")

@stub.function(gpu="A100", timeout=300, retries=0)  # Handle retries manually
def run_inference(input_data):
    """Run ML inference with built-in error handling"""
    import torch
    # Your inference code here
    return result

@retry_with_backoff(max_retries=3, initial_delay=2, backoff_factor=3)
def resilient_inference(input_data):
    """Wrapper with retry logic"""
    return run_inference.remote(input_data)

Key principles:

• Use exponential backoff to avoid overwhelming recovering systems
• Set reasonable max retries (3-5) to prevent infinite loops
• Log each retry attempt for debugging
• Distinguish between retryable errors (timeouts, 5xx) and permanent failures (4xx, config errors)

2. Queue Work for Asynchronous Processing

When Modal is experiencing degraded performance, queue work instead of failing synchronously:

import modal
from redis import Redis
import json

stub = modal.Stub("queued-inference")
redis_client = Redis(host='your-redis-host', port=6379)

@stub.function()
def process_inference_queue():
    """Background worker to process queued inference jobs"""
    while True:
        # Pop job from Redis queue
        job_data = redis_client.lpop('modal_inference_queue')
        if not job_data:
            time.sleep(5)
            continue
        
        job = json.loads(job_data)
        
        try:
            result = run_model.remote(job['input'])
            store_result(job['job_id'], result)
            notify_completion(job['callback_url'], result)
        except Exception as e:
            # Re-queue if Modal is still down
            if is_modal_outage(e):
                redis_client.rpush('modal_inference_queue', job_data)
                time.sleep(30)  # Back off during outage
            else:
                handle_permanent_failure(job, e)

def submit_inference_job(input_data, callback_url):
    """Public API endpoint - queues work if Modal is down"""
    job_id = generate_unique_id()
    
    job = {
        'job_id': job_id,
        'input': input_data,
        'callback_url': callback_url,
        'submitted_at': time.time()
    }
    
    # Try immediate processing first
    try:
        result = run_model.remote(input_data, timeout=10)
        return {'status': 'completed', 'result': result}
    except modal.exception.TimeoutError:
        # Queue for later if Modal is slow/down
        redis_client.rpush('modal_inference_queue', json.dumps(job))
        return {'status': 'queued', 'job_id': job_id}

This pattern ensures you don't lose inference requests during outages—they're processed once Modal recovers.

3. Implement Multi-Region Fallback

For critical workloads, deploy functions across multiple Modal regions:

import modal

# Deploy identical functions in multiple regions
stub_us_east = modal.Stub("inference-us-east")
stub_us_west = modal.Stub("inference-us-west")
stub_eu = modal.Stub("inference-eu")

@stub_us_east.function(gpu="A100")
def inference_us_east(data):
    return run_model(data)

@stub_us_west.function(gpu="A100")
def inference_us_west(data):
    return run_model(data)

@stub_eu.function(gpu="A100")
def inference_eu(data):
    return run_model(data)

def multi_region_inference(data):
    """Try regions in order until one succeeds"""
    regions = [
        ('us-east', inference_us_east),
        ('us-west', inference_us_west),
        ('eu', inference_eu)
    ]
    
    for region_name, func in regions:
        try:
            return func.remote(data, timeout=30)
        except Exception as e:
            print(f"Region {region_name} failed: {e}")
            continue
    
    raise Exception("All regions failed")

Note: This increases cost (multiple deployments) but provides resilience against regional outages.

4. Build Health Checks and Circuit Breakers

Prevent cascading failures by detecting Modal degradation early:

import modal
from datetime import datetime, timedelta

class ModalHealthMonitor:
    def __init__(self, failure_threshold=3, recovery_time=300):
        self.failure_count = 0
        self.failure_threshold = failure_threshold
        self.recovery_time = recovery_time
        self.circuit_open_time = None
    
    def is_circuit_open(self):
        """Check if circuit breaker is open (Modal marked as down)"""
        if self.circuit_open_time is None:
            return False
        
        # Try to close circuit after recovery_time
        if datetime.now() - self.circuit_open_time > timedelta(seconds=self.recovery_time):
            self.reset()
            return False
        
        return True
    
    def record_failure(self):
        """Record a Modal failure"""
        self.failure_count += 1
        if self.failure_count >= self.failure_threshold:
            self.circuit_open_time = datetime.now()
            alert_team("Modal circuit breaker opened - marking as down")
    
    def record_success(self):
        """Record a successful Modal call"""
        self.failure_count = 0
        if self.circuit_open_time:
            alert_team("Modal circuit breaker closed - service recovered")
        self.circuit_open_time = None
    
    def reset(self):
        """Reset circuit breaker"""
        self.failure_count = 0
        self.circuit_open_time = None

health_monitor = ModalHealthMonitor()

def protected_modal_call(func, *args, **kwargs):
    """Wrap Modal calls with circuit breaker"""
    if health_monitor.is_circuit_open():
        raise Exception("Modal circuit breaker open - service marked as down")
    
    try:
        result = func(*args, **kwargs)
        health_monitor.record_success()
        return result
    except Exception as e:
        health_monitor.record_failure()
        raise

This prevents your application from repeatedly calling a down Modal service, reducing wasted time and costs.

5. Monitor GPU Availability Before Submission

Check GPU queue depth before submitting expensive jobs:

import modal

stub = modal.Stub("gpu-aware-submission")

def estimate_gpu_wait_time(gpu_type="A100"):
    """Estimate current GPU queue wait time"""
    try:
        # Submit a minimal test function
        start = time.time()
        
        @stub.function(gpu=gpu_type, timeout=60)
        def gpu_ping():
            return "ok"
        
        result = gpu_ping.remote(timeout=30)
        elapsed = time.time() - start
        
        # If it took >20s for a trivial function, queue is backed up
        return elapsed
    except modal.exception.TimeoutError:
        return float('inf')  # Queue is severely backed up

def smart_job_submission(job_data, gpu_type="A100"):
    """Only submit jobs if GPU queue is reasonable"""
    wait_time = estimate_gpu_wait_time(gpu_type)
    
    if wait_time > 120:  # >2 minute wait
        print(f"GPU queue wait time: {wait_time}s - deferring submission")
        return {'status': 'deferred', 'reason': 'high_gpu_queue'}
    
    # Queue is healthy, submit the job
    return submit_job.remote(job_data)

6. Set Up Comprehensive Alerting

Don't wait for users to report issues:

import modal
import requests
from datetime import datetime

stub = modal.Stub("modal-monitoring")

@stub.function(schedule=modal.Period(minutes=5))
def health_check():
    """Run every 5 minutes to check Modal health"""
    
    checks = {
        'api_reachable': check_api_health(),
        'gpu_available': check_gpu_availability(),
        'container_builds': check_build_system(),
        'volume_io': check_volume_performance()
    }
    
    failures = [k for k, v in checks.items() if not v]
    
    if failures:
        send_alert({
            'service': 'Modal',
            'status': 'degraded',
            'failing_components': failures,
            'timestamp': datetime.utcnow().isoformat(),
            'dashboard': 'https://apistatuscheck.com/api/modal'
        })
    
    # Log results for trending
    log_health_metrics(checks)

def check_api_health():
    """Test basic API connectivity"""
    try:
        # Make a simple API call
        stub.lookup("health-check", "test_function").remote()
        return True
    except:
        return False

def check_gpu_availability():
    """Test GPU provisioning"""
    try:
        @stub.function(gpu="T4", timeout=60)
        def gpu_test():
            return True
        
        result = gpu_test.remote(timeout=30)
        return result
    except:
        return False

def send_alert(alert_data):
    """Send to Slack, PagerDuty, or other alerting system"""
    requests.post('YOUR_WEBHOOK_URL', json=alert_data)

Subscribe to external monitoring:

• API Status Check alerts for Modal
• Modal's status page notifications
• Community Slack/Discord monitoring channels

7. Document Your Incident Response Process

Create a runbook for your team:

# Modal Outage Response Runbook

## Detection
1. Check [apistatuscheck.com/api/modal](https://apistatuscheck.com/api/modal)
2. Verify on [status.modal.com](https://status.modal.com)
3. Test with minimal health check function
4. Check #modal-support Slack for reports

## Immediate Actions
1. Enable circuit breaker to stop new submissions
2. Switch inference traffic to fallback provider (if available)
3. Queue pending jobs in Redis/SQS
4. Update status page for your customers
5. Notify engineering and support teams

## Communication
- Post in #incidents Slack channel
- Update customer status page within 15 minutes
- Prepare customer email template if outage >1 hour
- Monitor customer support tickets for spike

## Recovery
1. Monitor Modal status page for "Resolved" status
2. Test with health check function before re-enabling
3. Gradually ramp traffic back to Modal (10% → 50% → 100%)
4. Process queued jobs in batches to avoid thundering herd
5. Monitor error rates and GPU queue times
6. Review Modal bill for unexpected charges from failures

## Post-Mortem
- Calculate downtime duration and revenue impact
- Review retry/fallback effectiveness
- Document lessons learned
- Update monitoring and alerting based on detection gaps
- Consider architectural changes to improve resilience

Frequently Asked Questions

How often does Modal go down?

Modal maintains strong uptime, typically exceeding 99.9% availability. Major platform-wide outages are rare (1-2 times per year), though regional capacity constraints for specific GPU types (especially A100 and H100) occur more frequently during peak demand. Most developers experience minimal disruption in typical usage. However, GPU-dependent workloads can face intermittent queue delays during high-demand periods even when the core platform is operational.

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

Modal's official status page (status.modal.com) is manually updated by Modal's engineering team during incidents, which typically provides updates within 5-15 minutes of issue detection. API Status Check performs automated health checks every 60 seconds against live Modal API endpoints and GPU provisioning, often detecting degraded performance or capacity issues before they're officially reported. Use API Status Check for proactive monitoring and the official status page for confirmed incident details and estimated resolution times.

Can I get refunded or credits for Modal outages?

Modal's Service Level Agreement (SLA) provides uptime commitments and service credits for Enterprise customers when availability falls below specified thresholds. Standard pay-as-you-go customers typically do not receive automatic refunds for platform outages, though Modal may issue credits on a case-by-case basis for extended incidents. You should not be charged for failed functions due to infrastructure issues—contact Modal support if you notice charges for failed executions. Enterprise customers should review their specific contract for SLA credit terms.

Should I use Modal webhooks or polling for critical operations?

For production-critical workflows, implement a hybrid approach: use webhooks as the primary mechanism but include scheduled polling as a backup. During Modal outages or webhook service degradation, webhook deliveries may be delayed or lost. A polling function running every 1-5 minutes (depending on latency requirements) that checks for completed jobs ensures you don't miss important state changes. This redundancy adds minimal cost but significantly improves reliability.

How do I prevent wasting GPU costs during Modal outages?

Implement these cost-protection strategies:

1. Timeouts: Set aggressive timeout values on GPU functions (e.g., timeout=600 for 10 minutes max)
2. Circuit breakers: Stop submitting new GPU jobs when failure rate exceeds thresholds
3. Health checks: Test GPU availability with minimal functions before submitting expensive workloads
4. Billing alerts: Configure Modal dashboard alerts when daily spend exceeds expected amounts
5. Retry limits: Cap retry attempts to prevent cost amplification during intermittent failures

Always review your Modal usage dashboard after incidents to identify any unexpected charges from failed infrastructure.

What's the best GPU fallback strategy if Modal's A100s are unavailable?

Implement a tiered fallback approach:

1. Try A100 first (optimal performance)
2. Fall back to A10G (80% of A100 performance, better availability)
3. Use T4 for simpler models (widely available, lower cost)
4. Queue for later if all GPU types are unavailable

You can also implement multi-region fallback, trying US-East → US-West → EU until a region has capacity. For critical workloads, consider a multi-cloud strategy with a secondary provider like Replicate or Hugging Face Inference as the ultimate fallback.

How do I debug whether a failure is my code or Modal's infrastructure?

Follow this diagnostic checklist:

1. Check status pages: API Status Check and status.modal.com
2. Test minimal function: Deploy a "hello world" function with identical GPU/region settings
3. Check Modal community: Search Slack/Discord for recent reports of similar issues
4. Review error patterns: Infrastructure issues affect multiple functions simultaneously; code bugs are usually function-specific
5. Compare timings: If cold start times are 3x+ normal, it's likely infrastructure
6. Test locally: Run your code outside Modal to isolate Modal-specific issues

If minimal test functions fail with the same error, it's almost certainly a Modal infrastructure issue.

Does Modal support running AI workloads in specific geographic regions?

Yes, Modal supports multiple regions including US-East, US-West, and EU. You can specify regions when deploying functions to comply with data residency requirements or optimize latency for specific geographies. During outages or capacity constraints, regional availability can vary—one region may have GPU capacity while others are exhausted. For global AI applications, consider deploying identical functions across multiple regions with intelligent routing based on health checks and queue times.

What AI/ML platforms should I monitor alongside Modal?

For comprehensive AI infrastructure monitoring, track these services:

• Hugging Face - Model hosting and inference APIs
• Replicate - Serverless model deployment (Modal alternative)
• OpenAI - GPT/DALL-E APIs for LLM workloads
• AWS SageMaker - Enterprise ML infrastructure
• Anthropic Claude - Alternative LLM provider

Monitoring your entire AI stack helps you distinguish between Modal-specific issues and broader provider outages affecting your dependencies.

How can I test Modal's GPU queue times before submitting large batch jobs?

Implement a queue depth estimation function:

import modal
import time

stub = modal.Stub("queue-test")

@stub.function(gpu="A100", timeout=120)
def minimal_gpu_task():
    """Tiny task to measure queue wait time"""
    import torch
    return torch.cuda.is_available()

def estimate_queue_time(samples=3):
    """Estimate GPU queue wait time by submitting test functions"""
    wait_times = []
    
    for _ in range(samples):
        start = time.time()
        try:
            result = minimal_gpu_task.remote(timeout=60)
            elapsed = time.time() - start
            wait_times.append(elapsed)
        except modal.exception.TimeoutError:
            return float('inf')  # Queue is severely backed up
    
    avg_wait = sum(wait_times) / len(wait_times)
    return avg_wait

# Before submitting 1000 batch jobs:
queue_time = estimate_queue_time()
if queue_time < 30:  # Less than 30s wait
    print("Queue healthy, submitting batch jobs")
    submit_batch()
else:
    print(f"Queue backed up ({queue_time}s), deferring submission")
    schedule_for_later()

This prevents you from submitting large job batches into an already saturated queue, saving both time and costs.

Stay Ahead of Modal Outages

Don't let GPU infrastructure issues derail your AI development. Subscribe to real-time Modal alerts and get notified instantly when issues are detected—before your CI/CD pipeline fails or your customers notice degraded inference performance.

API Status Check monitors Modal 24/7 with:

• 60-second health checks for API, GPU provisioning, and container builds
• GPU availability tracking across A100, H100, T4, and A10G instance types
• Multi-region monitoring (US-East, US-West, EU)
• Instant alerts via email, Slack, Discord, or webhook
• Historical uptime tracking and incident timeline
• Cold start latency monitoring and trends

Also monitor your complete AI infrastructure:

• Hugging Face Status - Model hub and inference endpoints
• Replicate Status - Alternative serverless GPU platform
• OpenAI Status - GPT API for LLM workloads
• AWS Status - SageMaker and EC2 GPU instances

Start monitoring Modal now →

Build more resilient AI infrastructure with proactive monitoring across your entire stack. Get visibility into outages, GPU capacity issues, and performance degradation before they impact your business.

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Last updated: February 4, 2026. Modal status information is provided in real-time based on active monitoring. For official incident reports, always refer to status.modal.com.