Is Stability AI Down? How to Check Stable Diffusion API Status in Real-Time

Is Stability AI Down? How to Check Stable Diffusion API Status in Real-Time

Quick Answer: To check if Stability AI is down, visit apistatuscheck.com/api/stability-ai for real-time monitoring, or check the official status.stability.ai page. Common signs include image generation timeouts, API rate limiting errors, model unavailability (SDXL, SD3), credit/billing issues, and NSFW filter blocks.

When your AI image generation pipeline suddenly fails, every minute of downtime means lost productivity and revenue. Stability AI powers millions of image generations daily through their Stable Diffusion APIs—from marketing assets and product mockups to creative tools and design automation. Whether you're seeing timeout errors, API failures, or unexpected model availability issues, knowing how to quickly verify Stability AI's status can save you hours of troubleshooting and help you make informed decisions about your workflow.

How to Check Stability AI Status in Real-Time

1. API Status Check (Fastest Method)

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

  • Tests actual API endpoints every 60 seconds
  • Shows response times and latency trends for image generation
  • Tracks historical uptime over 30/60/90 days
  • Provides instant alerts when issues are detected
  • Monitors multiple models (SDXL, SD3, Stable Diffusion XL Turbo)
  • Validates generation quality to catch degraded performance

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

2. Official Stability AI Status Page

Stability AI maintains status.stability.ai as their official communication channel for service incidents. The page displays:

  • Current operational status for all services
  • Active incidents and investigations
  • Model-specific availability (SDXL, SD3, SD 2.1)
  • Scheduled maintenance windows
  • Historical incident reports
  • Component-specific status (API, Dashboard, Image Generation, Upscaling)

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

3. Check the Stability AI Platform Dashboard

If the Stability AI Platform at platform.stability.ai is loading slowly or showing errors, this often indicates broader infrastructure issues. Pay attention to:

  • Login failures or timeouts
  • Credit balance not loading
  • Generation history errors
  • API key management access issues
  • Playground tool failures

4. Test API Endpoints Directly

For developers, making a test API call can quickly confirm connectivity:

import requests
import base64

# Test text-to-image generation
url = "https://api.stability.ai/v1/generation/stable-diffusion-xl-1024-v1-0/text-to-image"

headers = {
    "Authorization": f"Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
}

payload = {
    "text_prompts": [
        {
            "text": "A lighthouse on a cliff"
        }
    ],
    "cfg_scale": 7,
    "height": 1024,
    "width": 1024,
    "samples": 1,
    "steps": 30,
}

response = requests.post(url, headers=headers, json=payload)

if response.status_code == 200:
    print("✅ Stability AI is operational")
    data = response.json()
    for i, image in enumerate(data["artifacts"]):
        with open(f"test_image_{i}.png", "wb") as f:
            f.write(base64.b64decode(image["base64"]))
else:
    print(f"❌ Error: {response.status_code}")
    print(response.json())

Look for HTTP response codes outside the 2xx range, timeout errors, or authentication failures.

5. Monitor Stability AI Community Channels

Check for real-time user reports:

  • Discord: Stability AI's official Discord server
  • GitHub Issues: github.com/Stability-AI
  • Twitter/X: Search for "Stability AI down" or "@StabilityAI"
  • Reddit: r/StableDiffusion community discussions

When multiple users report similar issues simultaneously, it's likely a platform-wide problem rather than an isolated incident.

Common Stability AI Issues and How to Identify Them

Image Generation Timeouts

Symptoms:

  • Requests hanging for 60+ seconds with no response
  • Gateway timeout errors (504)
  • Connection reset during generation
  • Partial image data returned

What it means: Image generation is computationally intensive. During high load or infrastructure issues, generation requests may exceed timeout limits. This is especially common with:

  • High-resolution generations (1024x1024+)
  • Complex prompts requiring many diffusion steps
  • Peak usage hours

Diagnostic code:

import requests
import time

def test_generation_latency():
    start_time = time.time()
    
    try:
        response = requests.post(
            "https://api.stability.ai/v1/generation/stable-diffusion-xl-1024-v1-0/text-to-image",
            headers={"Authorization": f"Bearer {API_KEY}"},
            json={
                "text_prompts": [{"text": "simple test image"}],
                "height": 512,
                "width": 512,
                "samples": 1,
                "steps": 20
            },
            timeout=120
        )
        
        elapsed = time.time() - start_time
        
        if response.status_code == 200:
            print(f"✅ Generation completed in {elapsed:.2f}s")
            if elapsed > 60:
                print("⚠️ WARNING: Unusually slow response time")
        else:
            print(f"❌ Failed: {response.status_code}")
            
    except requests.exceptions.Timeout:
        print(f"❌ TIMEOUT after {time.time() - start_time:.2f}s")
    except Exception as e:
        print(f"❌ ERROR: {str(e)}")

test_generation_latency()

API Rate Limiting

Common error messages:

  • 429 Too Many Requests
  • Rate limit exceeded
  • Organization rate limit reached

Understanding Stability AI rate limits:

Rate limits vary by subscription tier:

  • Free tier: 25 credits/month (≈25 generations)
  • Starter ($10/mo): 1,000 credits/month
  • Professional: Custom limits based on plan

Identifying rate limit issues:

response = requests.post(url, headers=headers, json=payload)

if response.status_code == 429:
    retry_after = response.headers.get('Retry-After')
    print(f"⚠️ Rate limited. Retry after: {retry_after} seconds")
    
    # Check rate limit headers
    print(f"Limit: {response.headers.get('X-RateLimit-Limit')}")
    print(f"Remaining: {response.headers.get('X-RateLimit-Remaining')}")
    print(f"Reset: {response.headers.get('X-RateLimit-Reset')}")

During outages: You may hit rate limits even when under normal usage quotas due to retry logic or backend issues incorrectly counting requests.

Model Availability Issues (SDXL, SD3, SD 2.1)

Symptoms:

  • Specific model endpoints returning 503 errors
  • "Model temporarily unavailable" messages
  • Successful generations on some models but not others
  • Unexpected fallback to older model versions

Popular Stability AI models:

Model Engine ID Use Case
SDXL 1.0 stable-diffusion-xl-1024-v1-0 High-quality, detailed images
SD3 Medium sd3-medium Latest model, improved prompt adherence
SD3 Large sd3-large Highest quality, slowest
SDXL Turbo stable-diffusion-xl-1024-v0-9 Fast generation (1-4 steps)
SD 2.1 stable-diffusion-v1-6 Legacy, widely compatible

Testing model availability:

models_to_test = [
    "stable-diffusion-xl-1024-v1-0",
    "sd3-medium",
    "sd3-large",
    "stable-diffusion-xl-beta-v2-2-2"
]

for model in models_to_test:
    url = f"https://api.stability.ai/v1/generation/{model}/text-to-image"
    response = requests.post(url, headers=headers, json=test_payload)
    
    status = "✅ Available" if response.status_code == 200 else f"❌ {response.status_code}"
    print(f"{model}: {status}")

Credit and Billing Issues

Common problems:

  • Generations failing despite available credits
  • Credit balance not updating after purchases
  • Invalid API key errors after subscription changes
  • Unexpected "insufficient credits" errors

Check credit balance programmatically:

response = requests.get(
    "https://api.stability.ai/v1/user/balance",
    headers={"Authorization": f"Bearer {API_KEY}"}
)

if response.status_code == 200:
    balance = response.json()
    print(f"Credit balance: {balance['credits']}")
else:
    print("❌ Unable to retrieve balance - possible account issue")

During outages: Billing systems may become disconnected from generation APIs, causing false "insufficient credits" errors even with valid balances.

NSFW Filter Blocks

Symptoms:

  • Generations rejected with "content policy violation"
  • Prompts flagged as potentially generating NSFW content
  • Inconsistent filtering (same prompt works sometimes, fails others)

Understanding the NSFW filter:

Stability AI uses automated content filtering to prevent generation of:

  • Adult/sexual content
  • Extreme violence or gore
  • Hate symbols or extremist content
  • Celebrity likenesses (in some cases)

Common false positives:

  • Medical/anatomical illustrations
  • Art history references (classical nude paintings)
  • Fashion/swimwear designs
  • Certain color combinations or keywords

Testing content filtering:

test_prompts = [
    "a beautiful sunset over mountains",  # Safe
    "medical anatomy diagram of human heart",  # May trigger filter
    "fashion photography in studio lighting"  # Usually safe
]

for prompt in test_prompts:
    payload = {"text_prompts": [{"text": prompt}], "samples": 1}
    response = requests.post(url, headers=headers, json=payload)
    
    if response.status_code == 400:
        error = response.json()
        if "content policy" in error.get("message", "").lower():
            print(f"🚫 NSFW filter triggered: {prompt}")
    elif response.status_code == 200:
        print(f"✅ Generated successfully: {prompt}")

During outages: Content filtering systems may become overly aggressive or fail entirely, causing unexpected behavior.

The Real Impact When Stability AI Goes Down

Creative Workflow Disruption

For designers, marketers, and content creators, Stability AI downtime means:

  • Marketing campaigns delayed: Product launches dependent on generated assets
  • Design iterations halted: Teams unable to explore visual concepts
  • Content pipelines broken: Automated image generation for blogs, social media, ads
  • Client deliverables at risk: Agency work dependent on AI-generated mockups

A 4-hour outage during a campaign launch can mean missed deadlines and damaged client relationships.

E-commerce and Product Visualization

Online retailers using Stability AI for product imagery face:

  • New product listings delayed: Cannot generate lifestyle images or variations
  • A/B testing stopped: Marketing experiments requiring image variants halted
  • Personalization broken: Dynamic product imagery based on user preferences fails
  • Inventory expansion blocked: Scaling product catalogs that depend on AI generation

For a business generating 1,000 product images daily, an outage creates a backlog that can take days to clear.

SaaS Platform Revenue Impact

If you've built a SaaS product on Stability AI APIs:

  • User-facing features break: Customers cannot use image generation tools
  • Free trial conversions drop: New users experience failure during critical evaluation
  • Churn risk increases: Paying customers lose trust in platform reliability
  • Support costs spike: Team overwhelmed with "why isn't this working?" tickets

A platform charging $49/month with 1,000 users loses $1,633 per day in MRR if 10% churn due to reliability concerns.

Developer Productivity Loss

For teams building with Stability AI:

  • Development blocked: Cannot test new features or integrations
  • CI/CD pipelines fail: Automated tests dependent on image generation
  • Demo failures: Sales demos or investor pitches using live generation
  • Debugging confusion: Time wasted troubleshooting local code vs. API issues

A 5-person engineering team at $100/hour represents $500 in lost productivity per outage hour.

Lost Competitive Advantage

In the fast-moving AI industry:

  • Competitors on Replicate or OpenAI gain advantage
  • Market timing opportunities missed (trending topics, viral moments)
  • First-mover benefits lost when launching AI features
  • Customer acquisition halted during promotional campaigns

Stability AI Incident Response Playbook

1. Implement Intelligent Retry Logic

Exponential backoff with jitter:

import time
import random

def generate_with_retry(prompt, max_retries=3, base_delay=2):
    """
    Retry image generation with exponential backoff
    """
    for attempt in range(max_retries):
        try:
            response = requests.post(
                url,
                headers=headers,
                json={
                    "text_prompts": [{"text": prompt}],
                    "height": 1024,
                    "width": 1024,
                    "samples": 1
                },
                timeout=120
            )
            
            if response.status_code == 200:
                return response.json()
            
            # Don't retry on client errors (400-499 except 429)
            if 400 <= response.status_code < 500 and response.status_code != 429:
                raise Exception(f"Client error: {response.status_code}")
            
            # Respect Retry-After header for rate limits
            if response.status_code == 429:
                retry_after = int(response.headers.get('Retry-After', base_delay))
                time.sleep(retry_after)
                continue
                
        except requests.exceptions.Timeout:
            print(f"⚠️ Timeout on attempt {attempt + 1}")
        except Exception as e:
            print(f"⚠️ Error on attempt {attempt + 1}: {str(e)}")
        
        if attempt < max_retries - 1:
            # Exponential backoff with jitter
            delay = base_delay * (2 ** attempt) + random.uniform(0, 1)
            print(f"Retrying in {delay:.2f}s...")
            time.sleep(delay)
    
    raise Exception("Max retries exceeded")

# Usage
try:
    result = generate_with_retry("a serene mountain landscape")
    print("✅ Generation successful")
except Exception as e:
    print(f"❌ Failed after retries: {e}")

2. Queue Failed Generations

Background job processing:

from redis import Redis
from rq import Queue

# Setup job queue
redis_conn = Redis()
job_queue = Queue('image-generation', connection=redis_conn)

def queue_generation_job(prompt, user_id, parameters):
    """
    Queue image generation for background processing
    """
    job = job_queue.enqueue(
        'tasks.generate_image',
        prompt=prompt,
        user_id=user_id,
        params=parameters,
        retry=True,
        job_timeout='10m',
        result_ttl=86400  # Keep result for 24 hours
    )
    
    return job.id

# In your application
try:
    # Try immediate generation
    result = generate_image_sync(prompt)
except Exception as e:
    # Queue for later if API is down
    job_id = queue_generation_job(prompt, user_id, params)
    
    notify_user(user_id, 
        f"Image generation queued (ID: {job_id}). "
        "You'll receive an email when ready."
    )

3. Implement Fallback to Alternative AI Providers

Multi-provider strategy:

class ImageGenerator:
    def __init__(self):
        self.providers = [
            StabilityAIProvider(),
            ReplicateProvider(),  # Replicate.com hosts Stable Diffusion
            OpenAIProvider(),     # DALL-E 3 fallback
        ]
    
    def generate(self, prompt, **kwargs):
        """
        Try providers in order until one succeeds
        """
        last_error = None
        
        for provider in self.providers:
            try:
                print(f"Trying {provider.name}...")
                result = provider.generate(prompt, **kwargs)
                
                # Log successful provider for analytics
                analytics.track('image_generated', {
                    'provider': provider.name,
                    'fallback': provider != self.providers[0]
                })
                
                return result
                
            except Exception as e:
                print(f"❌ {provider.name} failed: {str(e)}")
                last_error = e
                continue
        
        raise Exception(f"All providers failed. Last error: {last_error}")

# Usage
generator = ImageGenerator()
try:
    image = generator.generate("a cyberpunk cityscape")
except Exception as e:
    # All providers down - queue or notify
    handle_complete_outage(e)

Provider comparison:

Provider Best For Pricing Latency
Stability AI Direct SDXL access, fine-tuning $10-100+/mo 10-30s
Replicate Stable Diffusion variants, custom models Pay per use ($0.0023/gen) 15-45s
OpenAI (DALL-E) Text adherence, consistent style $0.04-0.08/image 8-15s
Midjourney Artistic quality (Discord bot, no API) $10-60/mo 30-60s

4. Local Model Fallback (Advanced)

For critical applications, run Stable Diffusion locally:

from diffusers import StableDiffusionXLPipeline
import torch

class LocalStableDiffusion:
    def __init__(self, model_id="stabilityai/stable-diffusion-xl-base-1.0"):
        print("Loading local SDXL model (this may take a minute)...")
        self.pipe = StableDiffusionXLPipeline.from_pretrained(
            model_id,
            torch_dtype=torch.float16,
            use_safetensors=True,
            variant="fp16"
        )
        
        # Move to GPU if available
        if torch.cuda.is_available():
            self.pipe.to("cuda")
            print("✅ Model loaded on GPU")
        else:
            print("⚠️ Running on CPU (will be slow)")
    
    def generate(self, prompt, **kwargs):
        image = self.pipe(
            prompt,
            num_inference_steps=kwargs.get('steps', 30),
            guidance_scale=kwargs.get('cfg_scale', 7.5)
        ).images[0]
        
        return image

# Hybrid approach: API with local fallback
try:
    image = stability_api.generate(prompt)
except Exception:
    print("⚠️ API down, using local model...")
    local_generator = LocalStableDiffusion()
    image = local_generator.generate(prompt)

Trade-offs:

  • Pros: Complete independence from API, no per-generation costs
  • Cons: Requires GPU infrastructure ($0.50-2/hour cloud GPUs), slower, maintenance burden

5. Proactive Communication

User notification system:

def check_stability_status():
    """
    Monitor Stability AI and notify users of issues
    """
    try:
        # Quick health check
        response = requests.get(
            "https://api.stability.ai/v1/engines/list",
            headers={"Authorization": f"Bearer {API_KEY}"},
            timeout=10
        )
        
        if response.status_code != 200:
            notify_admins(
                "🚨 Stability AI Health Check Failed",
                f"Status code: {response.status_code}"
            )
            
            # Update service status page
            update_status_page(
                service="image-generation",
                status="degraded",
                message="We're experiencing delays with our AI image generation service due to upstream issues."
            )
            
            # Notify active users
            notify_active_users(
                "Image generation is currently experiencing delays. "
                "We're monitoring the situation and will update you shortly."
            )
            
    except Exception as e:
        handle_monitoring_error(e)

# Run every 5 minutes
schedule.every(5).minutes.do(check_stability_status)

6. Post-Outage Recovery

After service restoration:

def process_queued_generations():
    """
    Process all queued generation jobs after outage
    """
    queued_jobs = job_queue.get_jobs(status='queued')
    
    print(f"Processing {len(queued_jobs)} queued generations...")
    
    for job in queued_jobs:
        try:
            # Process with rate limiting to avoid overwhelming API
            result = generate_image_sync(job.kwargs['prompt'])
            
            # Notify user
            send_email(
                job.kwargs['user_email'],
                "Your AI image is ready!",
                f"Your queued image generation has completed. View it here: {result.url}"
            )
            
            # Throttle to respect rate limits
            time.sleep(2)
            
        except Exception as e:
            print(f"Failed to process job {job.id}: {e}")
            # Re-queue or escalate

# Analytics and reporting
def generate_outage_report(outage_start, outage_end):
    """
    Analyze impact of outage
    """
    impacted_users = db.query(
        "SELECT COUNT(DISTINCT user_id) FROM generation_attempts "
        "WHERE created_at BETWEEN %s AND %s AND status = 'failed'",
        [outage_start, outage_end]
    )
    
    failed_generations = db.query(
        "SELECT COUNT(*) FROM generation_attempts "
        "WHERE created_at BETWEEN %s AND %s AND status = 'failed'",
        [outage_start, outage_end]
    )
    
    report = {
        'duration_minutes': (outage_end - outage_start).total_seconds() / 60,
        'impacted_users': impacted_users[0][0],
        'failed_generations': failed_generations[0][0],
        'estimated_revenue_impact': impacted_users[0][0] * 0.10  # Estimated churn
    }
    
    return report

Alternative AI Image Generation Services

If Stability AI is experiencing extended downtime, consider these alternatives:

Replicate (Recommended)

Replicate hosts multiple Stable Diffusion models and variants:

import replicate

# SDXL on Replicate
output = replicate.run(
    "stability-ai/sdxl:39ed52f2a78e934b3ba6e2a89f5b1c712de7dfea535525255b1aa35c5565e08b",
    input={
        "prompt": "a majestic mountain landscape",
        "width": 1024,
        "height": 1024,
        "num_outputs": 1
    }
)

Pros: Same models as Stability AI, pay-per-use pricing, good reliability Cons: Slightly slower, different API structure

OpenAI DALL-E 3

OpenAI's image generation API:

from openai import OpenAI

client = OpenAI(api_key="YOUR_OPENAI_KEY")

response = client.images.generate(
    model="dall-e-3",
    prompt="a majestic mountain landscape",
    size="1024x1024",
    quality="hd",
    n=1
)

image_url = response.data[0].url

Pros: Excellent prompt adherence, consistent quality, reliable infrastructure Cons: More expensive ($0.04-0.08/image vs $0.01-0.02), less control over model parameters

Leonardo.ai

Creative AI platform with custom models:

Pros: User-friendly interface, unique model variants, commercial licensing Cons: No official API (yet), requires manual workflow

Midjourney

High-quality artistic generation via Discord:

Pros: Exceptional artistic quality, strong community Cons: No API, Discord-only interface, less suitable for automation

Frequently Asked Questions

How often does Stability AI experience outages?

Stability AI typically maintains strong uptime (99%+), but being a GPU-intensive service, occasional performance degradations occur during high demand periods. Major outages affecting all users are rare (1-2 times per quarter), though specific models may have brief unavailability. Regional variations and rate limiting during peak hours are more common than full platform outages.

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

Stability AI's official status page (status.stability.ai) is manually updated by their operations team during incidents, which can lag behind actual issues. API Status Check performs automated health checks every 60 seconds, including actual image generation tests, detecting problems often before they're officially reported. Use both for comprehensive monitoring.

Can I get credits back if Stability AI is down during my generation?

Stability AI typically does not charge credits for failed generation requests that return error codes. However, requests that timeout or partially complete may consume credits. If you believe you were incorrectly charged during an outage, contact Stability AI support with your request IDs. Enterprise customers may have SLA provisions for credit refunds.

Which Stable Diffusion model should I use when SDXL is down?

If SDXL (stable-diffusion-xl-1024-v1-0) is unavailable, try these alternatives in order:

  1. SD3 Medium - Latest model, similar quality
  2. SDXL Turbo - Faster variant of SDXL
  3. SD 2.1 - Older but highly stable fallback
  4. Replicate's SDXL - Same model on different infrastructure

Each has different pricing and performance characteristics. Test before production use.

How do I prevent duplicate image generations during retries?

Use idempotency or request tracking:

import uuid

generation_id = str(uuid.uuid4())

# Store attempt
db.insert('generation_attempts', {
    'id': generation_id,
    'prompt': prompt,
    'status': 'pending'
})

try:
    result = generate_image(prompt)
    db.update('generation_attempts', 
              {'id': generation_id}, 
              {'status': 'completed', 'image_url': result.url})
except:
    db.update('generation_attempts', 
              {'id': generation_id}, 
              {'status': 'failed'})
    # Retry logic here

This ensures you can safely retry without creating duplicate generations.

Why is my image generation slow even when Stability AI is "up"?

Slow generation can occur even without full outages:

  • High demand periods - More users = longer queue times
  • Complex prompts - High step counts (50+) take longer
  • Resolution - 1024x1024+ images require more computation
  • Model load - First request after model spin-down is slower
  • Network latency - Geographic distance from Stability AI servers

If consistently slow, try SDXL Turbo (1-4 steps) or lower resolutions (512x512).

What regions does Stability AI operate in?

Stability AI operates globally with primary infrastructure in:

  • United States (primary)
  • Europe (secondary)

Unlike some providers, Stability AI doesn't offer region-specific endpoints. All requests route through their global infrastructure. Latency varies by your geographic location but service availability is global.

Is there a Stability AI downtime notification service?

Yes, multiple options:

  • Subscribe to official updates at status.stability.ai
  • Use API Status Check for automated monitoring with email, Slack, Discord, or webhook alerts
  • Monitor their Discord server for real-time community reports
  • Set up custom monitoring with tools like Datadog or Pingdom

We recommend combining official status subscriptions with active monitoring for fastest detection.

Can I run Stable Diffusion locally to avoid API downtime?

Yes! You can run Stable Diffusion models locally using:

Options:

  1. Automatic1111 WebUI - Popular open-source interface
  2. ComfyUI - Node-based workflow tool
  3. Diffusers library - Python library for developers (code example above)
  4. InvokeAI - User-friendly local installation

Requirements:

  • NVIDIA GPU with 8GB+ VRAM (for SDXL)
  • 20GB+ disk space for models
  • Technical setup knowledge

Trade-offs: No API costs but requires hardware investment and maintenance. Best for high-volume users or those requiring complete control.

How do I integrate multiple AI image providers for redundancy?

Implement an abstraction layer:

class ImageGenerationService:
    def __init__(self):
        self.providers = {
            'stability': StabilityAIClient(),
            'replicate': ReplicateClient(),
            'openai': OpenAIClient()
        }
        self.primary = 'stability'
    
    def generate(self, prompt, fallback=True):
        try:
            return self.providers[self.primary].generate(prompt)
        except Exception as e:
            if fallback:
                for name, provider in self.providers.items():
                    if name != self.primary:
                        try:
                            return provider.generate(prompt)
                        except:
                            continue
            raise e

This adds resilience but increases complexity. Evaluate based on your reliability requirements and budget.

Stay Ahead of Stability AI Outages

Don't let AI image generation failures disrupt your workflow. Subscribe to real-time Stability AI alerts and get notified instantly when issues are detected—before your users or customers notice.

API Status Check monitors Stability AI 24/7 with:

  • 60-second health checks including actual image generation tests
  • Instant alerts via email, Slack, Discord, or webhook
  • Historical uptime tracking and incident reports
  • Multi-model monitoring (SDXL, SD3, SD 2.1)
  • Performance metrics and latency tracking

Also monitor your entire AI stack:

Start monitoring Stability AI now →

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

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