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

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

Quick Answer: To check if Leonardo AI is down, visit apistatuscheck.com/api/leonardo-ai for real-time monitoring, or check the official Leonardo AI status page. Common signs include image generation queue delays, model loading failures, credit/token deduction issues, canvas real-time lag, and API rate limiting errors.

When your AI image generation workflow suddenly stops working, creative deadlines are at stake. Leonardo AI powers thousands of creators, game developers, marketers, and designers worldwide with its fine-tuned models and real-time canvas capabilities. Whether you're experiencing failed generations, API timeouts, or credit system glitches, knowing how to quickly verify Leonardo AI's status can save you hours of troubleshooting and help you make informed decisions about your creative workflow.

How to Check Leonardo AI Status in Real-Time

1. API Status Check (Fastest Method)

The quickest way to verify Leonardo AI's operational status is through apistatuscheck.com/api/leonardo-ai. This real-time monitoring service:

  • Tests actual API endpoints every 60 seconds
  • Shows response times and generation latency trends
  • Tracks historical uptime over 30/60/90 days
  • Provides instant alerts when issues are detected
  • Monitors generation queue status and model availability

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

Start monitoring Leonardo AI now →

2. Official Leonardo AI Status Channels

Leonardo AI communicates service status through their official channels:

  • Discord community - Real-time incident reports and team responses
  • Twitter/X (@LeonardoAi_) - Official status updates and announcements
  • In-app notifications - Dashboard alerts for active incidents
  • Status page - If available, shows component-specific health

Pro tip: Join the Leonardo AI Discord server and enable notifications for the #status or #announcements channel to receive immediate updates when incidents occur.

3. Check Your Leonardo AI Dashboard

If the Leonardo AI Dashboard at app.leonardo.ai is showing errors, this often indicates broader infrastructure issues. Pay attention to:

  • Dashboard loading failures or infinite spinners
  • Model library not loading
  • Generation history showing as empty
  • Credit balance display errors
  • Canvas features unresponsive

4. Test API Endpoints Directly

For developers integrating Leonardo AI's API, making a test call can quickly confirm connectivity:

curl -X POST https://cloud.leonardo.ai/api/rest/v1/generations \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "test generation",
    "modelId": "6bef9f1b-29cb-40c7-b9df-32b51c1f67d3",
    "width": 512,
    "height": 512,
    "num_images": 1
  }'

Look for HTTP response codes outside the 2xx range, timeout errors (504), or rate limiting errors (429).

5. Monitor Community Reports

Real-time user reports often surface issues before official announcements:

  • Discord #support channel - Users reporting similar issues
  • Reddit r/LeonardoAI - Community discussion of outages
  • Twitter - Search for "Leonardo AI down" or "Leonardo AI not working"
  • DownDetector - Crowd-sourced outage reports

If multiple users are reporting the same issue simultaneously, it's likely a platform-wide problem rather than an isolated account issue.

Common Leonardo AI Issues and How to Identify Them

Image Generation Queue Delays

Symptoms:

  • Generations stuck in "pending" status for 5+ minutes
  • Queue position not advancing
  • Completed generations not appearing in gallery
  • "Initializing generation" message persisting indefinitely

What it means: Leonardo AI uses a queuing system for image generation. During high load or infrastructure issues, the queue can become backlogged. Normal generation time is 10-60 seconds depending on model and settings; anything beyond 5 minutes indicates potential problems.

How to diagnose:

// Check generation status via API
const checkGeneration = async (generationId) => {
  const response = await fetch(
    `https://cloud.leonardo.ai/api/rest/v1/generations/${generationId}`,
    {
      headers: {
        'Authorization': `Bearer ${API_KEY}`
      }
    }
  );
  
  const data = await response.json();
  console.log('Generation status:', data.generations_by_pk.status);
  console.log('Created at:', data.generations_by_pk.createdAt);
  
  // If status is PENDING for >5 minutes, likely queue issue
  const createdTime = new Date(data.generations_by_pk.createdAt);
  const waitTime = Date.now() - createdTime.getTime();
  
  if (data.generations_by_pk.status === 'PENDING' && waitTime > 300000) {
    console.warn('Generation stuck in queue for', waitTime / 1000, 'seconds');
  }
};

Model Loading Failures

Common error messages:

  • "Failed to load model"
  • "Model temporarily unavailable"
  • "Unable to initialize model weights"
  • Model dropdown showing empty or not loading

What it means: Leonardo AI offers dozens of fine-tuned models (Leonardo Diffusion, DreamShaper, RPG 4.0, etc.). Model loading failures indicate issues with the model serving infrastructure, which can affect specific models or all models simultaneously.

Impact by model type:

  • Platform models (Leonardo Vision XL, Phoenix) - Core service impact
  • Community models - May have separate infrastructure
  • Custom fine-tuned models - Dependent on model storage availability

Credit/Token System Issues

Symptoms:

  • Credits not deducting after successful generations
  • Credits deducting but generation failing
  • Token balance showing incorrect amounts
  • Subscription tier not reflecting after payment
  • "Insufficient credits" error despite having balance

What it means: Leonardo AI uses a credit-based system where each generation consumes credits based on resolution, model, and settings. Credit system issues can cause billing inconsistencies and access problems.

Diagnostic check:

// Verify user credit balance via API
const checkCredits = async () => {
  const response = await fetch(
    'https://cloud.leonardo.ai/api/rest/v1/me',
    {
      headers: {
        'Authorization': `Bearer ${API_KEY}`
      }
    }
  );
  
  const data = await response.json();
  console.log('API Paid Tokens:', data.user_details[0].apiPaidTokens);
  console.log('Subscription Tokens:', data.user_details[0].subscriptionTokens);
  console.log('Token Renewal Date:', data.user_details[0].tokenRenewalDate);
  
  // Compare with dashboard display to identify discrepancies
};

Canvas Real-Time Lag

Canvas-specific issues:

  • Real-time generation preview freezing
  • Brush strokes not registering
  • Undo/redo not working
  • Layer management unresponsive
  • WebSocket connection errors in console

What it means: Leonardo AI Canvas uses WebSocket connections for real-time collaborative editing and live preview. Connection issues or server-side processing delays create lag that breaks the creative flow.

Browser console errors to watch for:

WebSocket connection to 'wss://canvas.leonardo.ai' failed
Error: Canvas state sync timeout
Failed to fetch canvas session

API Rate Limiting

Common error codes during rate limiting:

  • 429 Too Many Requests - Exceeded rate limit
  • quota_exceeded - Monthly API quota reached
  • concurrent_limit_reached - Too many simultaneous generations

Rate limits by tier:

  • Free tier: 30 requests/minute, 150 credits/month
  • Apprentice: 120 requests/minute, 8,500 credits/month
  • Artisan: 240 requests/minute, 25,000 credits/month
  • Maestro: 360 requests/minute, 60,000 credits/month

Handling rate limits gracefully:

const generateWithRetry = async (prompt, options, maxRetries = 3) => {
  for (let i = 0; i < maxRetries; i++) {
    try {
      const response = await fetch(
        'https://cloud.leonardo.ai/api/rest/v1/generations',
        {
          method: 'POST',
          headers: {
            'Authorization': `Bearer ${API_KEY}`,
            'Content-Type': 'application/json'
          },
          body: JSON.stringify({
            prompt,
            ...options
          })
        }
      );
      
      if (response.status === 429) {
        const retryAfter = response.headers.get('Retry-After') || 60;
        console.log(`Rate limited. Retrying after ${retryAfter}s`);
        await new Promise(r => setTimeout(r, retryAfter * 1000));
        continue;
      }
      
      if (!response.ok) {
        throw new Error(`API error: ${response.status}`);
      }
      
      return await response.json();
    } catch (error) {
      if (i === maxRetries - 1) throw error;
      await new Promise(r => setTimeout(r, 2000 * Math.pow(2, i)));
    }
  }
};

The Real Impact When Leonardo AI Goes Down

Creative Workflow Disruption

Every hour of Leonardo AI downtime halts creative production:

  • Game developers: Cannot generate concept art, textures, or environment assets
  • Marketing teams: Campaign visuals delayed, social media content blocked
  • Product designers: Prototype mockups and variation testing stopped
  • Content creators: YouTube thumbnails, blog headers, book covers delayed

For a studio relying on AI-generated assets, a 4-hour outage can push project deadlines by days.

Game Development Asset Pipeline Breakdown

Modern game studios integrate Leonardo AI into asset creation workflows:

Pre-production impact:

  • Concept art iterations halted
  • Character design exploration stopped
  • Environment mood boards incomplete

Production impact:

  • Texture generation for 3D models delayed
  • UI element variations unavailable
  • Marketing asset creation blocked

Example workflow disruption:

// Typical game asset generation pipeline
const generateGameAsset = async (assetType, parameters) => {
  // Step 1: Generate base concept
  const concept = await leonardoAI.generate({
    prompt: `${assetType} game asset, ${parameters.style}`,
    model: 'RPG_4.0'
  });
  
  // Step 2: Generate variations
  const variations = await leonardoAI.imageToImage({
    initImageId: concept.id,
    prompt: parameters.variationPrompt,
    strength: 0.3
  });
  
  // Step 3: Upscale final selection
  const final = await leonardoAI.upscale({
    imageId: variations.selected.id
  });
  
  // If Leonardo AI is down, entire pipeline fails
  return final;
};

Marketing Campaign Delays

Marketing teams use Leonardo AI for rapid visual content creation:

  • Social media content: Daily post visuals, carousel images, stories
  • Ad creative: A/B testing variations, seasonal campaigns
  • Email marketing: Header graphics, product showcases
  • Landing pages: Hero images, feature illustrations

Revenue impact example: A $50K ad campaign launch delayed by 24 hours due to creative asset generation failure can cost thousands in lost conversion opportunity and media spend waste.

Client Deliverable Failures

Agencies and freelancers delivering AI-generated content face:

  • Missed client deadlines → Late fees or contract penalties
  • Reputation damage → Future project risk
  • Revenue delays → Cash flow disruption
  • Emergency workarounds → Manual design at 10x the time cost

API Integration Downtime

SaaS products integrating Leonardo AI experience user-facing failures:

Example integrations:

  • Content management systems with AI asset generation
  • Marketing automation platforms with visual creation
  • Game development tools with asset pipelines
  • Design software with AI-powered features

User impact:

  • Feature failures in production
  • Customer support ticket spikes
  • Churn risk from reliability concerns
  • Negative app store reviews

What to Do When Leonardo AI Goes Down

1. Implement Generation Queue Management

Queue failed generations for automatic retry:

class LeonardoGenerationQueue {
  constructor() {
    this.queue = [];
    this.processing = false;
  }
  
  async addToQueue(generationRequest) {
    this.queue.push({
      id: Date.now(),
      request: generationRequest,
      attempts: 0,
      maxAttempts: 5,
      createdAt: new Date()
    });
    
    if (!this.processing) {
      this.processQueue();
    }
  }
  
  async processQueue() {
    this.processing = true;
    
    while (this.queue.length > 0) {
      const item = this.queue[0];
      
      try {
        const result = await this.attemptGeneration(item.request);
        
        // Success - remove from queue and notify user
        this.queue.shift();
        await this.notifySuccess(item.id, result);
        
      } catch (error) {
        item.attempts++;
        
        if (item.attempts >= item.maxAttempts) {
          // Max retries reached - remove and notify failure
          this.queue.shift();
          await this.notifyFailure(item.id, error);
        } else {
          // Retry with exponential backoff
          const backoffMs = 1000 * Math.pow(2, item.attempts);
          await new Promise(r => setTimeout(r, backoffMs));
        }
      }
    }
    
    this.processing = false;
  }
  
  async attemptGeneration(request) {
    const response = await fetch(
      'https://cloud.leonardo.ai/api/rest/v1/generations',
      {
        method: 'POST',
        headers: {
          'Authorization': `Bearer ${process.env.LEONARDO_API_KEY}`,
          'Content-Type': 'application/json'
        },
        body: JSON.stringify(request)
      }
    );
    
    if (!response.ok) {
      throw new Error(`Generation failed: ${response.status}`);
    }
    
    return await response.json();
  }
}

// Usage
const generationQueue = new LeonardoGenerationQueue();

// When Leonardo AI might be experiencing issues
try {
  await leonardoAI.generate(params);
} catch (error) {
  if (error.message.includes('timeout') || error.status >= 500) {
    await generationQueue.addToQueue(params);
    console.log('Generation queued for retry');
  }
}

2. Implement Fallback AI Generation Services

Multi-provider strategy for production resilience:

class AIImageGenerator {
  constructor() {
    this.providers = [
      { name: 'leonardo', client: leonardoClient, priority: 1 },
      { name: 'stability', client: stabilityClient, priority: 2 },
      { name: 'replicate', client: replicateClient, priority: 3 }
    ];
  }
  
  async generate(prompt, options = {}) {
    // Sort by priority
    const orderedProviders = this.providers.sort((a, b) => 
      a.priority - b.priority
    );
    
    for (const provider of orderedProviders) {
      try {
        console.log(`Attempting generation with ${provider.name}`);
        
        const result = await provider.client.generate(prompt, options);
        
        // Log successful provider for analytics
        await this.logProviderUsage(provider.name, 'success');
        
        return {
          ...result,
          provider: provider.name
        };
        
      } catch (error) {
        console.warn(`${provider.name} failed:`, error.message);
        await this.logProviderUsage(provider.name, 'failed');
        
        // Continue to next provider
        continue;
      }
    }
    
    throw new Error('All AI generation providers failed');
  }
  
  async logProviderUsage(provider, status) {
    // Track which providers are working/failing for monitoring
    await analytics.track('ai_generation_attempt', {
      provider,
      status,
      timestamp: new Date()
    });
  }
}

// Usage with automatic failover
const aiGen = new AIImageGenerator();

try {
  const image = await aiGen.generate(
    'fantasy castle on floating island',
    { style: 'concept art' }
  );
  console.log('Generated with:', image.provider);
} catch (error) {
  console.error('All providers failed:', error);
}

Alternative AI image generation services:

  • Stability AI - Stable Diffusion models, enterprise API
  • Replicate - Multiple models including SDXL, Midjourney-like
  • Runway ML - Advanced video and image generation
  • OpenAI DALL-E - Different aesthetic, reliable uptime
  • Midjourney - Via Discord bot, manual workflow

3. Cache and Reuse Generated Assets

Smart caching reduces dependency on real-time generation:

class GenerationCache {
  constructor(storageProvider) {
    this.storage = storageProvider; // S3, CloudFlare R2, etc.
    this.cache = new Map();
  }
  
  // Generate cache key from prompt and parameters
  getCacheKey(prompt, options) {
    const normalized = {
      prompt: prompt.toLowerCase().trim(),
      model: options.model || 'default',
      width: options.width || 512,
      height: options.height || 512,
      seed: options.seed || null
    };
    
    return crypto
      .createHash('sha256')
      .update(JSON.stringify(normalized))
      .digest('hex');
  }
  
  async generate(prompt, options = {}) {
    const cacheKey = this.getCacheKey(prompt, options);
    
    // Check memory cache first
    if (this.cache.has(cacheKey)) {
      console.log('Cache hit (memory):', cacheKey);
      return this.cache.get(cacheKey);
    }
    
    // Check storage cache
    try {
      const cached = await this.storage.get(cacheKey);
      if (cached) {
        console.log('Cache hit (storage):', cacheKey);
        this.cache.set(cacheKey, cached);
        return cached;
      }
    } catch (error) {
      // Cache miss, continue to generation
    }
    
    // Generate new image
    const result = await leonardoAI.generate(prompt, options);
    
    // Store in cache
    await this.storage.put(cacheKey, result);
    this.cache.set(cacheKey, result);
    
    return result;
  }
}

// Usage - reduces Leonardo AI calls by 40-60% for repeated prompts
const cachedGenerator = new GenerationCache(s3Storage);
const image = await cachedGenerator.generate('game UI button, metallic');

4. Communicate Proactively with Stakeholders

Internal team communication:

// Automated status notification system
const notifyTeam = async (status) => {
  const message = status === 'down' 
    ? '⚠️ Leonardo AI experiencing issues. Generations queued for retry. Using fallback providers where possible.'
    : '✅ Leonardo AI service restored. Processing queued generations.';
  
  // Notify via Slack
  await slack.postMessage({
    channel: '#engineering-alerts',
    text: message,
    attachments: [{
      color: status === 'down' ? 'danger' : 'good',
      fields: [
        {
          title: 'Status',
          value: status === 'down' ? 'Degraded' : 'Operational',
          short: true
        },
        {
          title: 'Queued Jobs',
          value: generationQueue.length.toString(),
          short: true
        },
        {
          title: 'Monitor',
          value: 'https://apistatuscheck.com/api/leonardo-ai',
          short: false
        }
      ]
    }]
  });
  
  // Update status page if you have one
  await statusPage.updateComponent('ai-generation', {
    status: status === 'down' ? 'partial_outage' : 'operational',
    description: message
  });
};

Client/user communication templates:

For SaaS products:

"We're experiencing temporary delays with AI image generation due to provider issues. Your requests are queued and will be processed automatically when service resumes. Estimated delay: 15-30 minutes."

For agency clients:

"Leonardo AI (our primary image generation tool) is experiencing technical issues. We've queued your asset requests and activated our backup systems. Delivery may be delayed by 2-4 hours. We'll update you as soon as processing is complete."

5. Monitor Leonardo AI Status Proactively

Automated health monitoring:

// Leonardo AI health check service
class LeonardoHealthMonitor {
  constructor(apiKey) {
    this.apiKey = apiKey;
    this.isHealthy = true;
    this.lastCheck = null;
    this.failureCount = 0;
  }
  
  async checkHealth() {
    try {
      // Attempt a minimal API call to check connectivity
      const response = await fetch(
        'https://cloud.leonardo.ai/api/rest/v1/me',
        {
          headers: {
            'Authorization': `Bearer ${this.apiKey}`
          },
          timeout: 10000 // 10 second timeout
        }
      );
      
      if (response.ok) {
        // Service is healthy
        if (!this.isHealthy) {
          // Service just recovered
          await this.onServiceRecovered();
        }
        
        this.isHealthy = true;
        this.failureCount = 0;
      } else {
        this.recordFailure();
      }
      
    } catch (error) {
      this.recordFailure();
    }
    
    this.lastCheck = new Date();
  }
  
  recordFailure() {
    this.failureCount++;
    
    // Mark as unhealthy after 3 consecutive failures
    if (this.failureCount >= 3 && this.isHealthy) {
      this.isHealthy = false;
      this.onServiceDown();
    }
  }
  
  async onServiceDown() {
    console.error('Leonardo AI marked as DOWN');
    
    // Send alerts
    await notifyTeam('down');
    
    // Switch to fallback mode
    process.env.USE_LEONARDO_FALLBACK = 'true';
  }
  
  async onServiceRecovered() {
    console.log('Leonardo AI service RECOVERED');
    
    // Send recovery notification
    await notifyTeam('up');
    
    // Disable fallback mode
    process.env.USE_LEONARDO_FALLBACK = 'false';
    
    // Process queued generations
    await generationQueue.processAll();
  }
  
  // Run health check every 60 seconds
  startMonitoring() {
    setInterval(() => this.checkHealth(), 60000);
    this.checkHealth(); // Initial check
  }
}

// Start monitoring
const healthMonitor = new LeonardoHealthMonitor(process.env.LEONARDO_API_KEY);
healthMonitor.startMonitoring();

Subscribe to comprehensive alerts:

  • API Status Check alerts for automated 24/7 monitoring
  • Leonardo AI Discord #status channel notifications
  • Your own synthetic monitoring (example above)
  • Error rate monitoring in application logs

6. Post-Outage Recovery Checklist

Once Leonardo AI service is restored:

  1. Process queued generations from your generation queue
  2. Verify credit/token balance matches expected usage
  3. Review failed generations for data loss or corruption
  4. Check webhook deliveries if using Leonardo AI webhooks
  5. Audit generation costs for unexpected charges during outage
  6. Update incident documentation with timeline and impact
  7. Review and improve resilience - add caching, fallbacks, monitoring
  8. Communicate resolution to affected users/clients

Frequently Asked Questions

How often does Leonardo AI go down?

Leonardo AI is generally reliable, but as a rapidly scaling AI service, occasional issues occur. Users typically report minor issues (slow generation times, specific model unavailability) a few times per month, with major platform-wide outages being relatively rare (1-3 times per quarter). Most disruptions are resolved within 1-2 hours.

What's the difference between Leonardo AI status monitoring and API Status Check?

Leonardo AI doesn't currently maintain a dedicated public status page like enterprise SaaS products. Official updates are posted to Discord and Twitter during incidents, which can lag behind actual issues. API Status Check performs automated health checks every 60 seconds against live Leonardo AI API endpoints, often detecting issues before they're officially acknowledged. Use both for comprehensive awareness.

Can I get refunded credits for Leonardo AI downtime?

Leonardo AI's credit refund policy varies by situation. For credit deductions without successful generation delivery, contact support with generation IDs for investigation. Platform-wide outages typically don't result in automatic refunds, but support may offer credit compensation on a case-by-case basis. Enterprise customers with SLAs should review their agreements.

Should I use Leonardo AI webhooks or polling for generation status?

Leonardo AI provides webhooks for generation completion events, but polling is more reliable for production systems. Webhook delivery can be delayed or fail during incidents, while polling ensures you don't miss completed generations. Implement polling every 10-30 seconds for pending generations, and use webhooks as an optimization to reduce unnecessary API calls during normal operation.

How do I prevent duplicate generations during Leonardo AI outages?

When implementing retry logic, track generation requests with unique identifiers (UUIDs) in your database. Before retrying a failed generation, check if a previous attempt succeeded but the response was lost. Leonardo AI doesn't currently support idempotency keys like payment processors, so request tracking is your responsibility.

What's the best alternative to Leonardo AI for production systems?

For production reliability, implement multi-provider support. Primary alternatives include:

  • Stability AI - Stable Diffusion, strong API SLAs
  • Replicate - Multiple models, pay-per-use pricing
  • Runway ML - Advanced capabilities, higher cost
  • DALL-E 3 (OpenAI) - Different aesthetic, very reliable

Each has different strengths for game assets, marketing content, or concept art workflows.

How can I monitor Leonardo AI generation queue times?

Track the time between generation request and completion to establish baseline performance:

const trackGenerationTime = async (generationId) => {
  const startTime = Date.now();
  let status = 'PENDING';
  
  while (status === 'PENDING') {
    await new Promise(r => setTimeout(r, 5000)); // Poll every 5s
    
    const response = await leonardoAI.getGeneration(generationId);
    status = response.status;
  }
  
  const totalTime = Date.now() - startTime;
  
  await analytics.track('generation_completed', {
    generationId,
    totalTimeSeconds: totalTime / 1000,
    status
  });
  
  // Alert if generation took >5 minutes
  if (totalTime > 300000) {
    console.warn(`Slow generation detected: ${totalTime / 1000}s`);
  }
};

Normal generation time is 15-90 seconds. Consistently seeing 2-5+ minute waits indicates queue issues.

Does Leonardo AI have regional endpoints?

Leonardo AI currently operates from a single global infrastructure region. Unlike services with regional failover (AWS, Google Cloud), Leonardo AI doesn't offer region selection. This means outages typically affect all users globally. For geographic redundancy, combine Leonardo AI with regional alternatives like Replicate (multi-region) or self-hosted Stable Diffusion instances.

What should I do if my generations are consuming credits but failing?

If you're experiencing credit deductions without receiving generated images:

  1. Check generation history - Verify generations actually failed (status shows ERROR or FAILED)
  2. Review error messages - API responses contain failure reasons
  3. Contact support - Provide specific generation IDs and timestamps
  4. Document pattern - Note if specific prompts, models, or settings trigger failures
  5. Implement monitoring - Track credit balance before/after each generation attempt

Keep detailed logs for at least 30 days to support credit dispute claims.

How do I know if the issue is Leonardo AI or my API integration?

Systematic diagnosis:

  1. Test in Leonardo AI Dashboard - If web UI works but API doesn't, issue is likely your integration
  2. Check API key validity - Expired or revoked keys return 401 errors
  3. Verify request format - Validate JSON against Leonardo AI API documentation
  4. Test from different network - VPN or firewall rules may block API access
  5. Check other users - Discord/Twitter reports indicate platform-wide issues
  6. Use API Status Check - Monitor live endpoint health

If Dashboard also fails, it's definitely a Leonardo AI platform issue.

Stay Ahead of Leonardo AI Outages

Don't let AI generation issues derail your creative workflow. Subscribe to real-time Leonardo AI alerts and get notified instantly when issues are detected—before your deadlines are at risk.

API Status Check monitors Leonardo AI 24/7 with:

  • 60-second health checks for generation endpoints
  • Instant alerts via email, Slack, Discord, or webhook
  • Historical uptime tracking and incident reports
  • Queue performance monitoring and latency trends
  • Multi-service monitoring for your entire AI stack

Monitor your complete AI infrastructure:

Start monitoring Leonardo AI now →

Last updated: February 4, 2026. Leonardo AI status information is provided in real-time based on active monitoring. For official incident reports, refer to Leonardo AI's Discord server and Twitter/X announcements.

Monitor Your APIs

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

View API Status →