Is Replicate Down? How to Check Replicate AI Status in Real-Time

Is Replicate Down? How to Check Replicate AI Status in Real-Time

Quick Answer: To check if Replicate is down, visit apistatuscheck.com/api/replicate for real-time monitoring, or check the official status.replicate.com page. Common signs include model prediction failures, cold start timeouts, GPU queue delays exceeding 5+ minutes, webhook delivery failures, and "model version not found" errors.

When your AI-powered features suddenly stop generating images, running inference, or processing predictions, every second of downtime means frustrated users and broken product experiences. Replicate powers thousands of AI applications with access to Stable Diffusion, LLaMA, SDXL, and hundreds of other open-source models—making any service disruption a critical blocker for ML-powered products. Whether you're seeing prediction timeouts, GPU queue issues, or model loading failures, knowing how to quickly verify Replicate's status can save valuable troubleshooting time and help you maintain reliable AI features.

How to Check Replicate Status in Real-Time

1. API Status Check (Fastest Method)

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

  • Tests actual prediction endpoints every 60 seconds
  • Shows GPU queue times and inference latency trends
  • Tracks historical uptime over 30/60/90 days
  • Provides instant alerts when issues are detected
  • Monitors model availability across popular models

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

2. Official Replicate Status Page

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

  • Current operational status for all services
  • Active incidents and investigations
  • GPU queue health metrics
  • Scheduled maintenance windows
  • Historical incident reports
  • Component-specific status (API, Predictions, Webhooks, Models)

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

3. Check the Replicate Dashboard

If the Replicate Dashboard at replicate.com is loading slowly or showing errors, this often indicates broader infrastructure issues. Pay attention to:

  • Login failures or timeouts
  • Model browsing errors
  • Prediction history not loading
  • API token management access issues
  • Documentation site availability

4. Test API Endpoints Directly

For developers, running a quick test prediction can confirm connectivity:

curl -s -X POST \
  -H "Authorization: Token $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"version": "stability-ai/sdxl:latest", "input": {"prompt": "test"}}' \
  https://api.replicate.com/v1/predictions

Look for:

  • HTTP response codes outside the 2xx range
  • Timeout errors (no response within 30+ seconds)
  • SSL/TLS handshake failures
  • Rate limit errors when you're within normal usage

Common Replicate Issues and How to Identify Them

Model Prediction Failures

Symptoms:

  • Predictions consistently failing with generic error messages
  • "status": "failed" responses across multiple models
  • Predictions stuck in "starting" state indefinitely
  • 500/502/503 HTTP errors from Replicate API
  • Models returning empty outputs or malformed results

What it means: When prediction processing is degraded, inference requests that should succeed start failing. This differs from normal model errors (like invalid inputs)—you'll see a pattern of failures across different models and valid input parameters.

Cold Start Delays and GPU Queue Timeouts

Replicate uses a warm pool of GPUs for popular models, but during outages or high load:

Normal cold start: 10-30 seconds for unpopular models
Problem indicators:

  • Cold starts exceeding 2-5 minutes
  • Queue times showing "10+ minutes" for popular models (Stable Diffusion, SDXL)
  • Predictions timing out before GPU allocation
  • "error": "Prediction timed out" messages

GPU queue metrics to watch:

{
  "status": "starting",
  "started_at": null,
  "metrics": {
    "predict_time": null
  },
  "logs": "Waiting in queue... (position: 47)"
}

If queue positions aren't moving or predictions remain in "starting" for 5+ minutes on popular models, there's likely an infrastructure issue.

Webhook Delivery Failures

Webhooks are critical for async prediction workflows, and they're often the first system affected during partial outages:

  • Webhook POST requests not arriving at your endpoint
  • Significant delays (minutes to hours instead of seconds)
  • Missing or invalid webhook signatures
  • Duplicate webhook deliveries (retry storms)

Check your webhook logs. If delivery attempts are failing or delayed, and your endpoint is confirmed working (returns 200 OK to test requests), the issue is likely on Replicate's side.

API Rate Limiting Issues

Normal rate limits:

  • Free tier: 50 predictions/day
  • Paid: Based on usage and model

Outage indicators:

  • 429 Too Many Requests errors when you're well within limits
  • Sudden rate limit reductions
  • Inconsistent rate limit headers (X-RateLimit-Remaining)

Model Version Not Found Errors

Common error:

{
  "detail": "Model version not found",
  "status": 404
}

When this indicates an outage:

  • Happens with stable, well-known model versions
  • Affects multiple different models simultaneously
  • Previously working code starts failing
  • Model still visible on replicate.com but API returns 404

This often indicates model registry synchronization issues during infrastructure problems.

Training Job Failures

If you're using Replicate for fine-tuning:

  • Training jobs stuck in "starting" indefinitely
  • Unexpected training failures with infrastructure errors
  • Dataset upload timeouts
  • Training logs not streaming

The Real Impact When Replicate Goes Down

AI Features Completely Broken

For products built on Replicate, outages create immediate user-facing failures:

  • AI image generators: Users can't create images
  • Content moderation tools: NSFW detection fails, unsafe content slips through
  • Text-to-speech apps: Voice generation stops working
  • AI avatars: Profile picture generation blocked
  • Document processing: OCR and text extraction fails
  • Video tools: Frame interpolation, upscaling, and editing breaks

Every minute of downtime means users hitting error messages instead of experiencing your AI features.

Image Generation and Creative Tools Down

Replicate powers many creative AI tools through Stable Diffusion, SDXL, and other image models:

User impact:

  • Artists and designers can't generate imagery
  • Marketing teams blocked from creating ad assets
  • Social media tools unable to generate content
  • Game development workflows halted
  • E-commerce product visualization broken

For businesses charging per image generation or operating on freemium models, this translates to direct revenue loss and user churn.

Production Inference Infrastructure Failing

Enterprise teams running critical ML workloads face:

  • Real-time AI features down: Chatbots, recommendations, predictions
  • Batch processing halted: Overnight data processing jobs fail
  • Model serving disrupted: API-dependent services offline
  • Integration tests failing: CI/CD pipelines blocked

Cost implications: Teams may need to spin up emergency infrastructure on other providers (AWS SageMaker, Hugging Face, Modal) or delay product releases.

Webhook Processing Backlog

After outage resolution, you may receive:

  • Thousands of delayed prediction results simultaneously
  • Webhooks arriving out of order (prediction B completes before A)
  • Duplicate webhook deliveries (Replicate retries failed sends)
  • Results for predictions your system already marked as "failed"

This can overwhelm your processing infrastructure and create data consistency issues if not handled properly with idempotency checks.

Customer Trust and Revenue Loss

For AI-powered SaaS products:

  • Users perceive your product as unreliable (even if the issue is Replicate's)
  • Social media complaints spike: "App is broken!"
  • Competitors may gain advantage if they use different infrastructure
  • Subscription cancellations increase
  • Support ticket volume explodes

Compounding effect: If your product offers free credits or trials, users may burn credits retrying failed predictions, leading to increased costs and customer service issues.

What to Do When Replicate Goes Down

1. Implement Robust Retry Logic with Exponential Backoff

Intelligent retries for prediction creation:

async function createPredictionWithRetry(input, maxRetries = 3) {
  for (let attempt = 0; attempt < maxRetries; attempt++) {
    try {
      const prediction = await replicate.predictions.create({
        version: "stability-ai/sdxl:latest",
        input: input
      });
      return prediction;
    } catch (error) {
      // Don't retry client errors (bad input)
      if (error.response?.status >= 400 && error.response?.status < 500) {
        throw error;
      }
      
      // Don't retry on last attempt
      if (attempt === maxRetries - 1) throw error;
      
      // Exponential backoff: 2s, 4s, 8s
      const delayMs = 1000 * Math.pow(2, attempt + 1);
      await new Promise(resolve => setTimeout(resolve, delayMs));
    }
  }
}

Polling with timeout handling:

async function waitForPrediction(predictionId, timeoutMinutes = 10) {
  const startTime = Date.now();
  const timeoutMs = timeoutMinutes * 60 * 1000;
  
  while (true) {
    const prediction = await replicate.predictions.get(predictionId);
    
    if (prediction.status === "succeeded") {
      return prediction.output;
    }
    
    if (prediction.status === "failed") {
      throw new Error(`Prediction failed: ${prediction.error}`);
    }
    
    if (Date.now() - startTime > timeoutMs) {
      throw new Error(`Prediction timed out after ${timeoutMinutes} minutes`);
    }
    
    // Adaptive polling: faster when starting, slower when processing
    const pollInterval = prediction.status === "starting" ? 2000 : 500;
    await new Promise(resolve => setTimeout(resolve, pollInterval));
  }
}

2. Queue Predictions for Later Processing

When Replicate is experiencing issues, implement a fallback queue:

// Redis-backed prediction queue
async function queuePrediction(userId, modelVersion, input) {
  const job = {
    id: uuidv4(),
    userId,
    modelVersion,
    input,
    createdAt: Date.now(),
    attempts: 0
  };
  
  await redis.lpush('prediction_queue', JSON.stringify(job));
  
  // Notify user
  await notifyUser(userId, {
    message: "Your AI generation is queued due to high demand. You'll receive an email when it's ready.",
    estimatedWaitMinutes: 15
  });
  
  return job.id;
}

// Background worker processes queue
async function processQueue() {
  while (true) {
    const jobData = await redis.rpop('prediction_queue');
    if (!jobData) {
      await new Promise(resolve => setTimeout(resolve, 5000));
      continue;
    }
    
    const job = JSON.parse(jobData);
    
    try {
      const prediction = await createPredictionWithRetry(job.input);
      await saveResult(job.userId, prediction);
      await sendEmail(job.userId, "Your AI generation is ready!");
    } catch (error) {
      job.attempts++;
      if (job.attempts < 5) {
        // Requeue with exponential backoff
        await redis.lpush('prediction_queue', JSON.stringify(job));
      } else {
        await handleFailedJob(job, error);
      }
    }
  }
}

This prevents complete feature failure while maintaining user experience.

3. Implement Multi-Provider Failover

Enterprise applications should consider multi-cloud AI inference strategies:

Primary: Replicate for cost-effective, diverse model access
Fallback options:

  • Hugging Face Inference API: For open-source models (similar catalog)
  • Modal: For custom model deployments
  • AWS SageMaker: For enterprise workloads
  • OpenAI DALL-E: For image generation (different cost structure)
  • Stability AI API: Direct access to Stable Diffusion

Implementation example:

async function generateImageWithFallback(prompt) {
  try {
    // Try Replicate first (most cost-effective)
    return await replicate.run("stability-ai/sdxl:latest", {
      input: { prompt }
    });
  } catch (error) {
    if (isReplicateOutage(error)) {
      logger.warn('Replicate down, failing over to Hugging Face');
      
      try {
        return await huggingface.imageGeneration({
          model: "stabilityai/stable-diffusion-xl-base-1.0",
          inputs: prompt
        });
      } catch (hfError) {
        logger.error('Hugging Face also failed, trying OpenAI DALL-E');
        return await openai.images.generate({
          model: "dall-e-3",
          prompt: prompt
        });
      }
    }
    throw error;
  }
}

Trade-offs:

  • Additional integration complexity
  • Different pricing models (may be more expensive)
  • Inconsistent outputs across different model implementations
  • Requires abstraction layer for provider-agnostic code

For more on alternative providers, see our guides:

4. Communicate Proactively with Users

In-app status banners:

// Check Replicate health before showing AI features
const replicateStatus = await fetch('https://apistatuscheck.com/api/replicate/status');
const status = await replicateStatus.json();

if (status.degraded || status.down) {
  showBanner({
    type: 'warning',
    message: 'AI generation features are experiencing delays. Your requests are queued and will process automatically.',
    link: 'https://status.replicate.com'
  });
}

User notifications:

  • Email users with queued predictions
  • Show estimated wait times
  • Offer credit refunds for failed generations
  • Update help docs with current status

Support team preparation:

  • Brief support staff on the outage
  • Prepare templated responses
  • Monitor social media for user complaints
  • Update status page or blog

5. Monitor and Alert Aggressively

Health check implementation:

// Monitor Replicate API health every 60 seconds
setInterval(async () => {
  const startTime = Date.now();
  
  try {
    // Test lightweight endpoint
    const response = await fetch('https://api.replicate.com/v1/models', {
      headers: {
        'Authorization': `Token ${REPLICATE_API_TOKEN}`
      }
    });
    
    const latencyMs = Date.now() - startTime;
    
    if (!response.ok || latencyMs > 5000) {
      await sendAlert({
        severity: 'warning',
        message: `Replicate API health check degraded: ${response.status}, ${latencyMs}ms`,
        channel: '#ai-alerts'
      });
    }
  } catch (error) {
    await sendAlert({
      severity: 'critical',
      message: `Replicate API health check failed: ${error.message}`,
      channel: '#ai-alerts',
      pagerduty: true
    });
  }
}, 60000);

GPU queue monitoring:

async function monitorGPUQueue(modelVersion) {
  const prediction = await replicate.predictions.create({
    version: modelVersion,
    input: { prompt: "health check" }
  });
  
  // Track time to GPU allocation
  const startTime = Date.now();
  while (prediction.status === "starting") {
    await new Promise(resolve => setTimeout(resolve, 1000));
    await prediction.reload();
    
    const queueTimeSeconds = (Date.now() - startTime) / 1000;
    if (queueTimeSeconds > 300) { // 5 minutes
      await sendAlert({
        severity: 'warning',
        message: `GPU queue time exceeds 5 minutes for ${modelVersion}`,
        queueTime: queueTimeSeconds
      });
      break;
    }
  }
  
  // Cancel health check prediction
  await replicate.predictions.cancel(prediction.id);
}

Subscribe to alerts:

  • API Status Check alerts - automated monitoring
  • Replicate status page notifications
  • Your own synthetic monitoring
  • Error rate monitoring in application logs
  • GPU cost tracking (spikes may indicate retry storms)

6. Post-Outage Recovery Checklist

Once Replicate service is restored:

  1. Process queued predictions from your prediction queue
  2. Review failed predictions and offer retry credits to affected users
  3. Process webhook backlog with idempotency checks to avoid duplicate processing
  4. Audit prediction logs for inconsistencies (missing outputs, duplicate charges)
  5. Analyze financial impact (wasted GPU time, lost revenue, refund costs)
  6. Review GPU spend for anomalies (retry storms can cause unexpected charges)
  7. Update incident documentation with learnings and improved runbooks
  8. Improve resilience based on failure patterns observed

Idempotent webhook processing:

async function handleWebhook(webhook) {
  // Use prediction ID as idempotency key
  const alreadyProcessed = await redis.get(`webhook:${webhook.id}`);
  
  if (alreadyProcessed) {
    console.log(`Webhook ${webhook.id} already processed, skipping`);
    return;
  }
  
  // Process the prediction result
  await savePredictionResult(webhook.id, webhook.output);
  
  // Mark as processed (expire after 7 days)
  await redis.setex(`webhook:${webhook.id}`, 604800, 'true');
}

Frequently Asked Questions

How often does Replicate go down?

Replicate maintains strong uptime, typically exceeding 99.9% availability. Major outages affecting all models are rare (2-4 times per year), though specific model availability or GPU queue issues may occur more frequently during high-demand periods. Most production applications experience minimal disruption, though cold start delays can spike during peak usage.

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

The official Replicate status page (status.replicate.com) is manually updated by Replicate's team during incidents, which can sometimes lag behind actual issues by several minutes. API Status Check performs automated health checks every 60 seconds against live prediction endpoints and popular models (Stable Diffusion, SDXL, LLaMA), often detecting issues before they're officially reported. Use both for comprehensive monitoring.

Can I get credits or refunds for failed predictions during outages?

Replicate's pricing is based on compute time used—if a prediction fails before significant GPU time is consumed, you typically aren't charged. However, predictions that fail mid-inference may still incur costs. For widespread outages affecting many users, Replicate may issue service credits proactively. Contact support with specific prediction IDs if you believe you were incorrectly charged during an outage.

Should I use webhooks or polling to get prediction results?

For production applications, implement a hybrid approach: use webhooks for normal operation (most efficient) but include polling as a fallback. During outages, webhooks may be delayed or lost, so having polling logic (checking every 30-60 seconds with exponential backoff) ensures you don't miss prediction results. Always implement idempotency checks when processing webhooks.

How can I prevent duplicate prediction costs during retries?

Replicate doesn't currently support idempotency keys like payment processors do. To prevent duplicate predictions:

  1. Store prediction IDs in your database before creating them
  2. Check for existing predictions before creating new ones
  3. Use prediction status checks instead of creating duplicate requests
  4. Implement queue-based retry logic with deduplication
  5. Set reasonable timeouts before marking predictions as "failed"

What models are most affected during Replicate outages?

Popular models like Stable Diffusion XL, LLaMA, and Whisper typically have warm GPU pools and are prioritized during incidents. Less popular or custom models may experience longer cold start times or reduced availability. During partial outages, Replicate may prioritize high-demand models to serve the most users. Check model-specific status on the status page during incidents.

How do Replicate outages compare to other AI platforms?

Replicate's shared-GPU model means outages often manifest as queue delays rather than complete failures. Compared to alternatives:

  • OpenAI: Higher uptime but more expensive, closed models
  • Hugging Face Inference: Similar open-source models, comparable reliability
  • AWS SageMaker: Better SLAs but requires infrastructure management
  • Modal, Banana, Beam: Similar architectures with different pricing

Diversification across providers is wise for mission-critical AI features. Learn more:

Is there a Replicate downtime notification service?

Yes, several options exist:

  • Subscribe to official updates at status.replicate.com
  • Enable webhook notifications for status changes
  • Use API Status Check for automated alerts via email, Slack, Discord, or webhook
  • Set up custom monitoring with tools like Datadog, New Relic, or Prometheus
  • Join Replicate's Discord community for real-time incident discussions

Stay Ahead of Replicate Outages

Don't let AI infrastructure issues catch you off guard. Subscribe to real-time Replicate alerts and get notified instantly when prediction failures spike—before your users start complaining.

API Status Check monitors Replicate 24/7 with:

  • 60-second health checks across popular models
  • GPU queue time tracking
  • Instant alerts via email, Slack, Discord, or webhook
  • Historical uptime tracking and incident reports
  • Multi-API monitoring for your entire AI stack (OpenAI, Hugging Face, Anthropic, etc.)

Start monitoring Replicate now →

Related monitoring guides:

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

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