Is Weights & Biases Down? How to Check W&B Status in Real-Time

Quick Answer: To check if Weights & Biases (W&B) is down, visit apistatuscheck.com/api/wandb for real-time monitoring, or check the official status.wandb.ai page. Common signs include run sync failures, artifact upload timeouts, dashboard loading errors, API rate limiting, and team workspace access issues.

When your ML training runs stop syncing or your experiment dashboards won't load, every minute of lost visibility matters. Weights & Biases has become the backbone of modern MLOps, powering experiment tracking, model versioning, and collaborative ML development for thousands of teams worldwide. Whether you're facing failed run syncs, artifact upload errors, or dashboard timeouts, knowing how to quickly verify W&B's operational status can save hours of troubleshooting and help you make informed decisions about your ML workflows.

How to Check Weights & Biases Status in Real-Time

1. API Status Check (Fastest Method)

The quickest way to verify W&B's operational status is through apistatuscheck.com/api/wandb. This real-time monitoring service:

• Tests actual API endpoints every 60 seconds
• Shows response times and latency trends
• Tracks historical uptime over 30/60/90 days
• Provides instant alerts when issues are detected
• Monitors global availability across all regions

Unlike status pages that depend on manual updates from W&B's team, API Status Check performs continuous active health checks against Weights & Biases' production endpoints, giving you the most accurate real-time picture of service availability. This is especially critical when you have dozens of training runs attempting to sync simultaneously.

2. Official Weights & Biases Status Page

W&B maintains status.wandb.ai as their official communication channel for service incidents. The page displays:

• Current operational status for all services
• Active incidents and ongoing investigations
• Scheduled maintenance windows and upgrades
• Historical incident reports and postmortems
• Component-specific status (API, Dashboard, Artifact Storage, Webhooks)

Pro tip: Subscribe to status updates via email, SMS, or Slack on the status page to receive immediate notifications when incidents occur. For ML teams running continuous training pipelines, these alerts can prevent wasted compute hours.

3. Test W&B API Directly

For data scientists and ML engineers, making a test API call quickly confirms connectivity and authentication:

import wandb

try:
    # Initialize W&B with a test run
    run = wandb.init(
        project="status-check",
        name="health-check",
        mode="online"  # Force online mode
    )
    
    # Log a test metric
    run.log({"health_check": 1.0})
    
    # If we get here, W&B API is responding
    print("✓ W&B API is operational")
    run.finish()
    
except wandb.errors.CommError as e:
    print(f"✗ W&B API connection error: {e}")
except Exception as e:
    print(f"✗ W&B error: {e}")

Look for CommError, timeout exceptions, or HTTP 5xx errors which indicate service problems.

4. Check the W&B Dashboard

Navigate to wandb.ai and try to:

• Load your workspace and project list
• Open a recent run's dashboard
• View artifacts and model registry
• Check team settings and permissions

If the dashboard is loading slowly, showing infinite spinners, or returning error messages, this often indicates broader infrastructure issues beyond just the API.

5. Monitor Run Sync Status

If you have active training runs, check their sync status:

import wandb

# Check if runs are syncing successfully
run = wandb.init(project="my-project")

# Log with explicit sync check
for i in range(100):
    run.log({"loss": 1.0 / (i + 1)})
    
    # Check sync status
    if i % 10 == 0:
        sync_status = run._get_file_stream_api()
        if sync_status and sync_status._api:
            print(f"Sync healthy at step {i}")
        else:
            print(f"WARNING: Sync issue at step {i}")

Persistent sync failures across multiple runs and projects strongly suggest W&B service issues rather than local network problems.

Common Weights & Biases Issues and How to Identify Them

Run Sync Failures

Symptoms:

• Training runs complete locally but data doesn't appear in W&B dashboard
• wandb.init() hangs or times out
• Console shows repeated "Waiting for wandb.init()..." messages
• Runs stuck in "queued" or "syncing" status indefinitely
• Error messages like "Failed to sync run" or "Connection refused"

What it means: Run sync failures are often the first indicator of W&B service degradation. When the API service is healthy, runs sync within seconds. During outages, you'll see timeouts, connection errors, or runs that never appear in your workspace.

Debugging code:

import wandb
import time

def test_wandb_sync():
    """Test W&B sync with timeout detection"""
    start_time = time.time()
    
    try:
        run = wandb.init(
            project="sync-test",
            settings=wandb.Settings(
                _disable_stats=False,
                _disable_meta=False,
                timeout=30  # 30 second timeout
            )
        )
        
        # Log test data
        for i in range(10):
            run.log({"test_metric": i})
            
        elapsed = time.time() - start_time
        
        if elapsed > 10:
            print(f"⚠ Slow sync detected: {elapsed:.2f}s")
        else:
            print(f"✓ Sync successful: {elapsed:.2f}s")
            
        run.finish()
        return True
        
    except Exception as e:
        elapsed = time.time() - start_time
        print(f"✗ Sync failed after {elapsed:.2f}s: {e}")
        return False

# Run the test
test_wandb_sync()

Artifact Upload Timeouts

Symptoms:

• wandb.log_artifact() hangs indefinitely
• Large model checkpoints fail to upload
• Partial uploads that never complete
• Error: "Artifact upload timeout exceeded"
• HTTP 503 or 504 errors during artifact operations

Impact: Artifact upload failures are critical for ML teams because they break:

• Model checkpointing and versioning
• Dataset version tracking
• Reproducibility pipelines
• Model registry workflows

Detection and handling:

import wandb
from wandb.errors import CommError
import time

def upload_artifact_with_retry(artifact_path, artifact_name, max_retries=3):
    """Upload artifact with retry logic and timeout detection"""
    
    for attempt in range(max_retries):
        try:
            run = wandb.init(project="artifact-test")
            
            start_time = time.time()
            artifact = wandb.Artifact(artifact_name, type='model')
            artifact.add_file(artifact_path)
            
            # Attempt upload
            run.log_artifact(artifact)
            
            elapsed = time.time() - start_time
            print(f"✓ Artifact uploaded in {elapsed:.2f}s")
            
            run.finish()
            return True
            
        except CommError as e:
            elapsed = time.time() - start_time
            print(f"✗ Attempt {attempt + 1} failed after {elapsed:.2f}s: {e}")
            
            if attempt < max_retries - 1:
                wait_time = 2 ** attempt  # Exponential backoff
                print(f"Retrying in {wait_time}s...")
                time.sleep(wait_time)
            else:
                print("Max retries reached. W&B artifact service may be down.")
                return False
                
        except Exception as e:
            print(f"✗ Unexpected error: {e}")
            return False
    
    return False

# Usage
upload_artifact_with_retry("model.pkl", "my-model-v1")

Dashboard Loading Issues

Signs the dashboard is impacted:

• Workspace page shows "Loading..." indefinitely
• Individual run pages return 500/502/503 errors
• Charts and visualizations fail to render
• "Unable to fetch data" error messages
• Console errors showing failed API calls to api.wandb.ai

What to check:

import requests

def check_dashboard_api():
    """Check if W&B dashboard API is responding"""
    endpoints = [
        "https://api.wandb.ai/graphql",
        "https://api.wandb.ai/files",
        "https://api.wandb.ai/storage"
    ]
    
    for endpoint in endpoints:
        try:
            response = requests.get(
                endpoint,
                headers={"User-Agent": "wandb-health-check"},
                timeout=10
            )
            
            if response.status_code < 500:
                print(f"✓ {endpoint}: Status {response.status_code}")
            else:
                print(f"✗ {endpoint}: Status {response.status_code} (Service Error)")
                
        except requests.exceptions.Timeout:
            print(f"✗ {endpoint}: Timeout after 10s")
        except requests.exceptions.ConnectionError:
            print(f"✗ {endpoint}: Connection failed")
        except Exception as e:
            print(f"✗ {endpoint}: {e}")

check_dashboard_api()

Dashboard issues often accompany API problems but can also occur independently due to frontend infrastructure, CDN issues, or database query performance problems.

API Rate Limiting

Symptoms:

• HTTP 429 "Too Many Requests" errors
• Sudden RateLimitError exceptions during normal operations
• API calls that were working suddenly start failing
• Error message: "Rate limit exceeded for organization"

Normal vs Abnormal:

• Normal: You exceed your plan's documented rate limits
• Abnormal: Rate limiting occurs well below documented limits, suggesting W&B is throttling traffic due to infrastructure stress

Detection code:

import wandb
from wandb.errors import CommError
import time

def detect_rate_limiting():
    """Test for unexpected rate limiting"""
    api = wandb.Api()
    
    requests_made = 0
    rate_limit_errors = 0
    
    try:
        # Make several API calls in quick succession
        for i in range(20):
            try:
                runs = api.runs("my-entity/my-project", per_page=10)
                list(runs)  # Force evaluation
                requests_made += 1
                time.sleep(0.5)  # Normal throttling
                
            except CommError as e:
                if "429" in str(e) or "rate limit" in str(e).lower():
                    rate_limit_errors += 1
                    print(f"✗ Rate limit hit at request {requests_made}")
                else:
                    raise
                    
    except Exception as e:
        print(f"Error during rate limit test: {e}")
    
    if rate_limit_errors > 0:
        print(f"⚠ Hit rate limiting after {requests_made} requests")
        print(f"This may indicate W&B is under load")
    else:
        print(f"✓ Completed {requests_made} requests without rate limiting")

detect_rate_limiting()

During service degradation, W&B may implement aggressive rate limiting as a protective measure, affecting users who are normally well within their limits.

Team/Organization Access Errors

Symptoms:

• "Unauthorized" or "Forbidden" errors when accessing team workspaces
• Inability to view or create runs in organization projects
• SSO authentication failures
• Permission errors for previously accessible resources
• Team members unable to join or access shared projects

Identifying service vs permission issues:

import wandb

def diagnose_access_issue(entity, project):
    """Determine if access issues are service-related"""
    api = wandb.Api()
    
    try:
        # Test 1: Check API authentication
        user = api.viewer
        print(f"✓ Authenticated as: {user.username}")
        
        # Test 2: Try to access the project
        runs = api.runs(f"{entity}/{project}", per_page=1)
        run_list = list(runs)
        print(f"✓ Successfully accessed {entity}/{project}")
        print(f"  Found {len(run_list)} runs")
        
        # Test 3: Try to create a test run
        test_run = wandb.init(
            entity=entity,
            project=project,
            name="access-test",
            mode="online"
        )
        test_run.finish()
        print(f"✓ Successfully created test run in {entity}/{project}")
        
        return "access_ok"
        
    except wandb.errors.CommError as e:
        error_msg = str(e).lower()
        
        if "unauthorized" in error_msg or "forbidden" in error_msg:
            if "500" in str(e) or "503" in str(e):
                print(f"✗ Access denied with server error: {e}")
                return "service_issue"
            else:
                print(f"✗ Permission denied: {e}")
                return "permission_issue"
                
        elif "timeout" in error_msg or "connection" in error_msg:
            print(f"✗ Connection error: {e}")
            return "service_issue"
            
        else:
            print(f"✗ Unknown error: {e}")
            return "unknown"
            
    except Exception as e:
        print(f"✗ Unexpected error: {e}")
        return "unknown"

# Usage
status = diagnose_access_issue("my-team", "my-project")
if status == "service_issue":
    print("\n⚠ This appears to be a W&B service issue, not a permission problem")

If multiple team members suddenly lose access simultaneously, or if access errors come with HTTP 5xx status codes, the issue is likely on W&B's side rather than a permission configuration problem.

The Real Impact When Weights & Biases Goes Down

Lost ML Training Visibility

The most immediate impact of W&B downtime is losing real-time visibility into training runs:

• No live metrics: Can't monitor training loss, validation accuracy, or other critical metrics
• Blind hyperparameter tuning: Sweeps become impossible without metric feedback
• Resource waste: Training runs may be diverging or stuck, but you can't tell without W&B dashboards
• Late detection of failures: Can't catch NaN losses, memory leaks, or data loading issues early

Cost impact: For teams running dozens of GPU instances (monitored via Lambda Labs, RunPod, or other compute providers), a few hours without monitoring can waste thousands of dollars on failed experiments.

Broken Experiment Reproducibility

W&B is mission-critical for reproducible ML research:

• Lost experiment records: Runs that complete during outages may never sync, losing all hyperparameters, code versions, and results
• Incomplete artifact chains: Model versioning breaks when artifacts can't upload
• Broken dataset versioning: Can't track which data version was used for training
• Missing code snapshots: Git commits and code diffs won't be captured

Long-term impact: Months later when trying to reproduce a successful experiment, missing W&B data makes it nearly impossible to recreate the exact training conditions.

Team Collaboration Breakdown

Modern ML teams rely on W&B for coordination:

• Can't share results: Team members can't review each other's experiments
• Blocked code reviews: PRs that reference W&B runs become unverifiable
• Stalled model selection: Can't compare models across team members
• Delayed production deployments: Model registry downtime blocks promotion workflows

Productivity cost: A 4-hour outage affecting a 10-person ML team represents 40 person-hours of reduced productivity, beyond just the technical impact.

Failed CI/CD Pipelines

Many organizations integrate W&B into automated ML pipelines:

• Training pipelines fail: Scheduled retraining jobs error out when W&B is unavailable
• Model validation blocked: Automated evaluation scripts that log to W&B hang or fail
• Deployment gates broken: Systems that check W&B metrics before deploying models can't proceed
• Data pipeline failures: ETL jobs that log data quality metrics to W&B time out

Example of pipeline failure:

# Automated training pipeline that fails during W&B outage
def automated_training_pipeline():
    """CI/CD pipeline that depends on W&B"""
    
    # Load data
    train_data, val_data = load_datasets()
    
    # Initialize W&B - THIS HANGS DURING OUTAGE
    run = wandb.init(
        project="production-models",
        job_type="training",
        tags=["automated", "ci-cd"]
    )
    
    # Train model
    model = train_model(train_data, val_data, run)
    
    # Log artifacts - THIS ALSO FAILS
    artifact = wandb.Artifact("prod-model", type="model")
    artifact.add_file("model.pkl")
    run.log_artifact(artifact)
    
    # Entire pipeline blocked - deployment can't proceed
    run.finish()

Integration Cascade Failures

W&B often integrates with other tools in the ML stack:

• HuggingFace integrations break: Models pushed to HuggingFace Hub with W&B tracking fail
• Slack/Discord bot notifications stop: Teams lose automated experiment alerts
• Model serving platforms: Systems that pull models from W&B registry can't deploy updates
• Experiment management tools: Third-party tools that sync with W&B lose data

The ripple effects extend far beyond just W&B itself when it's deeply integrated into your infrastructure.

Incident Response Playbook for W&B Outages

1. Immediately Switch to Offline Mode

When W&B is down, prevent training runs from hanging by switching to offline mode:

import wandb
import os

# Method 1: Environment variable (set before training)
os.environ["WANDB_MODE"] = "offline"

# Method 2: In code
run = wandb.init(
    project="my-project",
    mode="offline"  # Run syncs locally, uploads when service returns
)

# Train as normal
for epoch in range(100):
    loss = train_epoch()
    run.log({"loss": loss, "epoch": epoch})

run.finish()

# Later, when W&B is back online:
# Run: wandb sync <run_directory>

Critical: Offline mode captures all metrics, artifacts, and code locally. When W&B recovers, you can sync historical runs without losing data.

2. Implement Automatic Failover Detection

Add health checks to your training scripts:

import wandb
import time
import requests

def is_wandb_available(timeout=5):
    """Check if W&B API is responding"""
    try:
        response = requests.get(
            "https://api.wandb.ai/",
            timeout=timeout
        )
        return response.status_code < 500
    except:
        return False

def init_wandb_with_fallback(project, **kwargs):
    """Initialize W&B with automatic offline fallback"""
    
    if is_wandb_available():
        print("✓ W&B available - using online mode")
        return wandb.init(project=project, mode="online", **kwargs)
    else:
        print("⚠ W&B unavailable - switching to offline mode")
        print("  Run will sync automatically when service recovers")
        return wandb.init(project=project, mode="offline", **kwargs)

# Usage in training script
run = init_wandb_with_fallback(
    project="my-project",
    name="training-run",
    config={"lr": 0.001, "batch_size": 32}
)

# Continue training normally...

This ensures your training runs never hang waiting for W&B, while still capturing all data for later sync.

3. Queue Artifacts for Delayed Upload

For large model checkpoints and datasets:

import wandb
import os
import json
from pathlib import Path

class ArtifactQueue:
    """Queue artifacts for upload when W&B is available"""
    
    def __init__(self, queue_dir="./wandb_artifact_queue"):
        self.queue_dir = Path(queue_dir)
        self.queue_dir.mkdir(exist_ok=True)
        
    def add_to_queue(self, artifact_path, artifact_name, artifact_type, metadata=None):
        """Add artifact to upload queue"""
        queue_item = {
            "artifact_path": str(artifact_path),
            "artifact_name": artifact_name,
            "artifact_type": artifact_type,
            "metadata": metadata or {},
            "queued_at": time.time()
        }
        
        queue_file = self.queue_dir / f"{artifact_name}_{int(time.time())}.json"
        with open(queue_file, "w") as f:
            json.dump(queue_item, f)
        
        print(f"✓ Queued artifact: {artifact_name}")
        
    def process_queue(self, project, entity=None):
        """Process queued artifacts when W&B is available"""
        if not is_wandb_available():
            print("W&B still unavailable - queue not processed")
            return
        
        queue_files = list(self.queue_dir.glob("*.json"))
        
        if not queue_files:
            print("No artifacts in queue")
            return
        
        print(f"Processing {len(queue_files)} queued artifacts...")
        
        run = wandb.init(project=project, entity=entity, job_type="artifact-upload")
        
        for queue_file in queue_files:
            try:
                with open(queue_file) as f:
                    item = json.load(f)
                
                # Upload artifact
                artifact = wandb.Artifact(
                    item["artifact_name"],
                    type=item["artifact_type"],
                    metadata=item["metadata"]
                )
                artifact.add_file(item["artifact_path"])
                run.log_artifact(artifact)
                
                # Remove from queue
                queue_file.unlink()
                print(f"✓ Uploaded: {item['artifact_name']}")
                
            except Exception as e:
                print(f"✗ Failed to upload {queue_file.name}: {e}")
        
        run.finish()

# Usage during outage
queue = ArtifactQueue()

# Training loop
for epoch in range(100):
    model = train_epoch()
    
    # Save checkpoint
    checkpoint_path = f"model_epoch_{epoch}.pt"
    torch.save(model.state_dict(), checkpoint_path)
    
    # Try to upload, queue if W&B is down
    if is_wandb_available():
        artifact = wandb.Artifact(f"model-epoch-{epoch}", type="model")
        artifact.add_file(checkpoint_path)
        run.log_artifact(artifact)
    else:
        queue.add_to_queue(
            checkpoint_path,
            f"model-epoch-{epoch}",
            "model",
            {"epoch": epoch, "loss": current_loss}
        )

# Later, when W&B recovers
queue.process_queue(project="my-project")

This ensures no model checkpoints are lost during outages, even for large files that can't sync immediately.

4. Set Up Multi-Region Monitoring

For enterprise teams, monitor W&B from multiple locations:

import requests
import concurrent.futures

def check_wandb_from_region(region_name, api_endpoint="https://api.wandb.ai"):
    """Check W&B availability from specific region"""
    try:
        start_time = time.time()
        response = requests.get(api_endpoint, timeout=10)
        latency = time.time() - start_time
        
        return {
            "region": region_name,
            "status": response.status_code,
            "latency_ms": latency * 1000,
            "available": response.status_code < 500
        }
    except Exception as e:
        return {
            "region": region_name,
            "status": None,
            "latency_ms": None,
            "available": False,
            "error": str(e)
        }

def check_global_wandb_status():
    """Check W&B from multiple geographic regions"""
    regions = {
        "us-east": "https://api.wandb.ai",
        "us-west": "https://api.wandb.ai",
        "europe": "https://api.wandb.ai",
        "asia": "https://api.wandb.ai"
    }
    
    with concurrent.futures.ThreadPoolExecutor() as executor:
        futures = {
            executor.submit(check_wandb_from_region, name, endpoint): name
            for name, endpoint in regions.items()
        }
        
        results = []
        for future in concurrent.futures.as_completed(futures):
            results.append(future.result())
    
    # Analyze results
    available_regions = [r for r in results if r["available"]]
    unavailable_regions = [r for r in results if not r["available"]]
    
    print(f"W&B Status Check Results:")
    print(f"  Available regions: {len(available_regions)}/{len(results)}")
    
    for result in results:
        status_icon = "✓" if result["available"] else "✗"
        latency = f"{result['latency_ms']:.0f}ms" if result["latency_ms"] else "timeout"
        print(f"  {status_icon} {result['region']}: {latency}")
    
    return len(unavailable_regions) == 0

# Run global check
is_fully_operational = check_global_wandb_status()

5. Communicate with Your Team

Create automated alerts for your ML team:

import requests

def notify_team_wandb_down(channel="ml-team"):
    """Send Slack notification about W&B outage"""
    
    message = {
        "channel": channel,
        "blocks": [
            {
                "type": "header",
                "text": {
                    "type": "plain_text",
                    "text": "⚠️ Weights & Biases Service Issue Detected"
                }
            },
            {
                "type": "section",
                "text": {
                    "type": "mrkdwn",
                    "text": "*All team members:* W&B API is currently experiencing issues.\n\n*Action required:*\n• Switch training runs to `offline` mode\n• Model uploads will be queued automatically\n• Check <https://apistatuscheck.com/api/wandb|real-time status>\n• View official updates at <https://status.wandb.ai|status.wandb.ai>"
                }
            },
            {
                "type": "section",
                "fields": [
                    {
                        "type": "mrkdwn",
                        "text": "*Status:* Degraded"
                    },
                    {
                        "type": "mrkdwn",
                        "text": f"*Detected:* {time.strftime('%H:%M:%S')}"
                    }
                ]
            }
        ]
    }
    
    # Send to your Slack webhook
    requests.post(os.environ["SLACK_WEBHOOK_URL"], json=message)

# Automated monitoring
def monitor_wandb_health(check_interval=60):
    """Continuously monitor W&B and alert on issues"""
    was_down = False
    
    while True:
        is_up = is_wandb_available()
        
        if not is_up and not was_down:
            # Status changed from up to down
            notify_team_wandb_down()
            was_down = True
        elif is_up and was_down:
            # Status recovered
            notify_team_wandb_recovered()
            was_down = False
        
        time.sleep(check_interval)

6. Post-Outage Recovery

Once W&B service is restored:

# 1. Sync all offline runs
find . -type d -name "offline-run-*" -exec wandb sync {} \;

# 2. Process queued artifacts
python process_artifact_queue.py

# 3. Verify all runs synced successfully
wandb verify

# 4. Check for data consistency issues
python validate_experiment_data.py

# 5. Update team on recovery
python notify_wandb_recovered.py

Post-outage checklist:

• All offline runs synced to W&B
• Queued artifacts uploaded successfully
• Verify run metrics match local logs
• Confirm team members can access dashboards
• Document lessons learned
• Review and improve monitoring/failover systems

Related ML Infrastructure Monitoring

W&B is often part of a larger ML infrastructure stack. Monitor your entire pipeline:

• HuggingFace Status - Model hosting and transformers library
• Lambda Labs Status - GPU compute infrastructure
• RunPod Status - Cloud GPU platform
• Replicate Status - ML model deployment
• Together AI Status - LLM inference platform

A holistic monitoring approach ensures you can quickly identify whether issues are with W&B, your compute provider, or other dependencies in your ML stack.

Frequently Asked Questions

How often does Weights & Biases go down?

Weights & Biases maintains strong uptime, typically exceeding 99.9% availability. Major outages affecting all users are rare (2-4 times per year), though regional issues or specific component degradation (like artifact storage or webhooks) may occur more frequently. Most ML teams experience minimal disruption from W&B outages in a typical year.

What's the difference between W&B status page and API Status Check?

The official W&B status page (status.wandb.ai) is manually updated by W&B's engineering team during incidents, which can sometimes lag 5-15 minutes behind actual issues. API Status Check performs automated health checks every 60 seconds against live W&B API endpoints, often detecting problems before they're officially reported. Use both for comprehensive monitoring—API Status Check for early detection, and the official page for detailed incident communications.

Can I use W&B offline permanently?

Yes, W&B supports fully offline mode where all data is stored locally. However, you lose key benefits like real-time collaboration, centralized dashboards, and the model registry. Offline mode is best used as a temporary fallback during outages, then synced when connectivity returns. For on-premise deployments, W&B offers self-hosted options that provide cloud-like features without internet dependency.

How do I prevent data loss during W&B outages?

Always use offline mode during outages: wandb.init(mode="offline"). This captures all metrics, artifacts, and metadata locally in your project directory. When W&B service recovers, run wandb sync <directory> to upload historical data. Additionally, implement local logging alongside W&B (save metrics to CSV/JSON) as a backup for critical experiments.

Will my training runs fail if W&B is down?

It depends on your code. If you use wandb.init() without timeouts or offline fallback, your script may hang indefinitely waiting for W&B. Implement the automatic failover patterns shown in this guide to gracefully degrade to offline mode. The key is: wandb.init(mode="offline") allows training to continue without interruption, then syncs data later.

Does W&B have regional outages or is it global?

W&B primarily operates on a unified global infrastructure, so major outages typically affect all regions simultaneously. However, specific components like artifact storage (which uses cloud object storage) may experience regional degradation. Network routing issues can also cause regional accessibility problems even when W&B's core services are healthy.

How do I get notified immediately when W&B goes down?

Several notification options:

• Subscribe to official updates at status.wandb.ai (email/SMS/Slack)
• Use API Status Check for automated alerts via email, Slack, Discord, PagerDuty, or webhook (60-second monitoring intervals)
• Implement your own health checks in CI/CD pipelines
• Set up Datadog/New Relic synthetic monitoring of W&B endpoints

For mission-critical ML pipelines, redundant monitoring (official + third-party) is recommended.

Can I switch to an alternative during W&B outages?

While there are alternatives (MLflow, Neptune, ClearML), switching mid-experiment is impractical. A better approach:

1. Short outages (< 1 hour): Use offline mode and sync later
2. Extended outages: Consider self-hosted MLflow as emergency backup
3. Long-term: Evaluate W&B Server (self-hosted) for critical production workloads

Most teams find W&B's reliability sufficient with proper offline mode implementation rather than maintaining parallel tracking systems.

What SLA does Weights & Biases provide?

W&B's SLA varies by plan:

• Free/Academic: Best effort, no SLA guarantees
• Team plans: Typically 99.5% uptime SLA
• Enterprise: Custom SLAs up to 99.9% with priority support and incident response

Enterprise customers may receive SLA credits for downtime exceeding guaranteed thresholds. Review your specific contract or contact W&B sales for detailed SLA terms.

How do I check if my specific W&B workspace is affected?

Use the W&B API to test your specific entity/project:

import wandb

api = wandb.Api()

try:
    # Test access to your specific workspace
    runs = api.runs("your-entity/your-project", per_page=1)
    list(runs)  # Force evaluation
    print("✓ Your workspace is accessible")
except Exception as e:
    print(f"✗ Workspace access failed: {e}")

If the general W&B service is operational but your workspace shows errors, the issue may be account-specific (billing, permissions, or team settings) rather than a platform-wide outage.

Stay Ahead of W&B Outages

Don't let MLOps infrastructure issues derail your ML experiments. Subscribe to real-time W&B alerts and get notified instantly when issues are detected—before your training runs fail.

API Status Check monitors Weights & Biases 24/7 with:

• 60-second health checks of W&B API endpoints
• Instant alerts via email, Slack, Discord, or webhook
• Historical uptime tracking and incident timeline
• Multi-component monitoring (API, dashboard, artifacts, webhooks)
• Integration with your entire ML stack monitoring

Start monitoring Weights & Biases now →

For ML teams running production pipelines:

Monitor your complete infrastructure stack:

• Weights & Biases - Experiment tracking
• HuggingFace - Model hub
• Lambda Labs - GPU compute
• RunPod - Cloud GPUs
• OpenAI - LLM APIs

Get comprehensive ML infrastructure monitoring with a single dashboard.

---

Last updated: February 4, 2026. Weights & Biases status information is provided in real-time based on active monitoring. For official incident reports, always refer to status.wandb.ai.