Is Groq Down? How to Check Groq API Status in Real-Time

Quick Answer: To check if Groq is down, visit apistatuscheck.com/api/groq for real-time monitoring, or check the official status.groq.com page. Common signs include API timeout errors, rate limiting spikes, model unavailability, streaming interruptions, and authentication failures.

When your AI application suddenly stops generating responses, every second of downtime impacts user experience and revenue. Groq's LPU (Language Processing Unit) infrastructure delivers industry-leading inference speeds—up to 10x faster than traditional GPU-based solutions—making any disruption immediately noticeable. Whether you're running real-time chatbots, voice assistants, or low-latency AI applications, knowing how to quickly verify Groq's status can save critical troubleshooting time and help you implement fallback strategies.

How to Check Groq Status in Real-Time

1. API Status Check (Fastest Method)

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

• Tests actual API endpoints every 60 seconds with live inference requests
• Measures response times and tokens-per-second performance
• Tracks historical uptime over 30/60/90 days
• Provides instant alerts when latency spikes or failures occur
• Monitors model availability across all supported models (Llama, Mixtral, Gemma)

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

2. Official Groq Status Page

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

• Current operational status for all services
• Active incidents and investigations
• Scheduled maintenance windows
• Historical incident reports
• Component-specific status (API, Inference, Authentication, Streaming)
• Per-model availability status

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

3. Check GroqCloud Console

If the GroqCloud Console at console.groq.com is loading slowly or showing errors, this often indicates broader infrastructure issues. Pay attention to:

• Login failures or timeouts
• API key management access issues
• Usage dashboard loading errors
• Delayed metrics refresh
• Model playground unavailability

4. Test API Endpoints Directly

For developers, making a test inference call can quickly confirm connectivity and performance:

from groq import Groq

client = Groq(api_key="your_api_key")

try:
    completion = client.chat.completions.create(
        model="mixtral-8x7b-32768",
        messages=[{"role": "user", "content": "test"}],
        max_tokens=10
    )
    print(f"Success: {completion.choices[0].message.content}")
except Exception as e:
    print(f"Error: {e}")

Using OpenAI-compatible endpoint:

from openai import OpenAI

client = OpenAI(
    api_key="your_groq_api_key",
    base_url="https://api.groq.com/openai/v1"
)

try:
    completion = client.chat.completions.create(
        model="llama-3.3-70b-versatile",
        messages=[{"role": "user", "content": "Hello"}]
    )
    print("Groq API operational")
except Exception as e:
    print(f"Groq API error: {e}")

Look for connection timeouts, 5xx HTTP errors, rate limit errors outside normal usage, or model unavailability messages.

5. Monitor Community Channels

The AI developer community often reports issues before official announcements:

• Groq Discord - Real-time user reports and official team responses
• Twitter/X - Search for "groq down" or "@GroqInc"
• Reddit r/LocalLLaMA - Groq discussions and outage reports
• Hacker News - Technical community discussions
• GitHub Issues - Groq SDK repositories for reported problems

Cross-reference community reports with your own testing to distinguish between widespread outages and account-specific issues.

Common Groq Issues and How to Identify Them

Rate Limiting (Free Tier Constraints)

Symptoms:

• 429 Too Many Requests errors
• rate_limit_exceeded error messages
• Requests rejected immediately without processing
• Error: "You have exceeded your request limit"

Groq free tier limits (as of 2024):

• Requests per minute (RPM): 30
• Requests per day (RPD): 14,400
• Tokens per minute (TPM): 20,000

What it means: Unlike traditional rate limiting during outages, Groq's free tier has strict quota enforcement. However, during incidents you may see rate limit errors even when well within your quota, or experience inconsistent rate limit enforcement across different models.

How to distinguish from outages:

import time
from groq import Groq

client = Groq(api_key="your_api_key")

def test_rate_limiting():
    successful = 0
    rate_limited = 0
    
    for i in range(5):
        try:
            client.chat.completions.create(
                model="llama-3.3-70b-versatile",
                messages=[{"role": "user", "content": "test"}],
                max_tokens=5
            )
            successful += 1
            time.sleep(2)  # Well within rate limits
        except Exception as e:
            if "429" in str(e) or "rate_limit" in str(e):
                rate_limited += 1
    
    if rate_limited > 0 and successful < 3:
        print("Possible rate limiting issue or API degradation")
    else:
        print("Normal rate limiting - consider upgrading plan")

Model Availability Issues

Symptoms:

• Specific models returning errors while others work
• model_not_found or model_unavailable errors
• Inconsistent model availability across regions
• Error: "The model you requested is currently unavailable"

Common affected models:

• Llama 3.3 70B Versatile
• Llama 3.1 70B Versatile
• Mixtral 8x7B
• Gemma 7B IT

What it means: Groq manages multiple LPU clusters for different model families. A model-specific outage may indicate infrastructure issues with that model's dedicated hardware, while other models remain operational.

Testing model availability:

models_to_test = [
    "llama-3.3-70b-versatile",
    "llama-3.1-70b-versatile",
    "mixtral-8x7b-32768",
    "gemma-7b-it"
]

for model in models_to_test:
    try:
        client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": "hi"}],
            max_tokens=5
        )
        print(f"✓ {model} - Available")
    except Exception as e:
        print(f"✗ {model} - Error: {e}")

API Timeout Errors

Common timeout scenarios:

• Connection timeout before request starts
• Read timeout waiting for inference response
• Gateway timeout (504) from load balancer
• WebSocket timeout during streaming

Expected vs. problematic latency:

• Normal Groq latency: 50-300ms for first token (fastest in industry)
• Degraded performance: 1-5 seconds for first token
• Outage indicator: >10 seconds or complete timeouts

Measuring actual performance:

import time

def measure_groq_performance():
    start = time.time()
    
    try:
        completion = client.chat.completions.create(
            model="llama-3.3-70b-versatile",
            messages=[{"role": "user", "content": "Say hello"}],
            max_tokens=50
        )
        
        latency = time.time() - start
        
        if latency > 5:
            print(f"⚠️ High latency: {latency:.2f}s (possible degradation)")
        elif latency > 10:
            print(f"🔴 Critical latency: {latency:.2f}s (likely outage)")
        else:
            print(f"✓ Normal latency: {latency:.2f}s")
            
    except Exception as e:
        print(f"Error: {e}")

measure_groq_performance()

Authentication Failures

Symptoms:

• 401 Unauthorized errors with valid API keys
• invalid_api_key error messages
• Intermittent authentication success/failure
• "API key not found" errors

What it means: Authentication issues can indicate problems with Groq's identity service, API key validation system, or database connectivity. Unlike simple incorrect credentials, outage-related auth failures happen with previously working keys.

Verification script:

def verify_api_key(api_key):
    client = Groq(api_key=api_key)
    
    try:
        # Simple request to verify auth
        client.models.list()
        print("✓ Authentication successful")
        return True
    except Exception as e:
        if "401" in str(e) or "unauthorized" in str(e).lower():
            print("✗ Authentication failed - check API key or service status")
            return False
        else:
            print(f"✗ Other error: {e}")
            return False

Streaming Interruptions

Symptoms:

• Streams disconnecting mid-response
• Incomplete generation with no error
• WebSocket connection failures
• Missing tokens in streamed output
• Error: "Stream interrupted" or "Connection reset"

What it means: Groq's streaming implementation sends tokens as they're generated by the LPU. Interruptions can indicate network issues, LPU hardware problems, or load balancer failures.

Robust streaming implementation:

def stream_with_recovery(prompt, max_retries=3):
    for attempt in range(max_retries):
        try:
            completion = client.chat.completions.create(
                model="mixtral-8x7b-32768",
                messages=[{"role": "user", "content": prompt}],
                stream=True,
                max_tokens=500
            )
            
            full_response = ""
            token_count = 0
            
            for chunk in completion:
                if chunk.choices[0].delta.content:
                    content = chunk.choices[0].delta.content
                    full_response += content
                    token_count += 1
                    print(content, end="", flush=True)
            
            print(f"\n\n✓ Stream completed: {token_count} tokens")
            return full_response
            
        except Exception as e:
            print(f"\n✗ Stream failed (attempt {attempt + 1}): {e}")
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
            else:
                print("Max retries exceeded")
                raise

# Usage
stream_with_recovery("Write a short poem about AI")

The Real Impact When Groq Goes Down

Real-Time AI Application Failures

Groq's primary value proposition is ultra-low latency inference. When the service is down, applications depending on real-time responses fail immediately:

• Conversational AI chatbots: Users see loading spinners or timeout errors
• Voice assistants: Unacceptable delays break the conversation flow
• Real-time translation: Live translation services halt
• AI-powered search: Search results fail to generate
• Content moderation: Real-time content screening stops

For applications where Groq's speed is essential (sub-second response requirements), degraded performance is as bad as complete downtime—even a 2-3 second delay breaks the user experience.

Customer Experience Degradation

Immediate user impact:

• Chatbot conversations abruptly end mid-response
• Voice interactions feel broken and unresponsive
• AI features show error messages instead of helpful responses
• Streaming text generation freezes or stutters

Trust erosion:

• Users assume your application is broken, not the underlying API
• Negative reviews cite "AI doesn't work" or "chatbot is down"
• Support tickets spike as users report failures
• Competitive disadvantage if competitors using different providers remain operational

Revenue Loss for AI-First Products

For businesses where AI is the core product offering:

• AI writing assistants: Users cannot generate content (Jasper, Copy.ai model)
• Code completion tools: Developer productivity halts
• Customer support automation: Falls back to human-only support (higher costs)
• AI-powered SaaS: Core features unavailable, leading to refund requests

Example impact: An AI customer support platform processing 10,000 conversations/day at $0.50/conversation loses $5,000 in revenue per day during extended outages, plus customer churn from poor experience.

Free Tier vs Paid Tier Implications

Groq's free tier makes it popular for experimentation and MVP development, but outages affect tiers differently:

Free tier users:

• May experience selective degradation during high load
• More likely to hit rate limits during recovery periods
• Less priority in incident resolution
• No SLA guarantees

Paid tier users:

• Expect higher reliability and priority support
• May have contractual SLA credits for downtime
• Business-critical applications at risk
• Can escalate through support channels

Migration considerations: Serious production workloads should evaluate paid plans or multi-provider strategies to avoid free tier limitations during incidents.

Competitive Intelligence Impact

AI inference is a rapidly evolving market. Groq competes with:

• OpenAI: GPT-4, GPT-3.5 Turbo
• Anthropic: Claude 3 family (see Is Anthropic Down?)
• Together AI: Open-source model inference (see Is Together AI Down?)
• Replicate: ML model deployment platform
• Anyscale: Ray-based LLM serving

When Groq experiences outages:

• Developers actively evaluate competitors
• Social media amplifies reliability concerns
• Enterprise buyers reconsider vendor selection
• Market share shifts to more reliable alternatives

For Groq, maintaining their "fastest inference" reputation requires not just speed, but also reliability. Outages directly impact their competitive positioning.

Development and Testing Disruption

CI/CD pipeline failures:

• Automated tests calling Groq API fail
• Integration test suites become unreliable
• Deployment pipelines blocked by failed health checks
• QA environments non-functional

Developer productivity impact:

• Cannot test new features locally
• Debugging blocked when AI components don't respond
• Demo preparations disrupted
• Onboarding new developers delayed

Example scenario: A team planning to demo their AI-powered product to investors cannot complete their demo script because Groq is down during the rehearsal window.

What to Do When Groq Goes Down: Incident Response Playbook

1. Implement Intelligent Retry Logic with Exponential Backoff

Don't hammer Groq's API during outages—this worsens the problem. Use exponential backoff with jitter:

import random
import time
from groq import Groq

def groq_with_retry(
    client: Groq,
    model: str,
    messages: list,
    max_retries: int = 5,
    base_delay: float = 1.0,
    max_delay: float = 60.0
):
    """
    Robust Groq API call with exponential backoff and jitter.
    
    Args:
        client: Groq client instance
        model: Model identifier
        messages: Chat messages list
        max_retries: Maximum retry attempts
        base_delay: Initial delay in seconds
        max_delay: Maximum delay between retries
    
    Returns:
        Completion response or raises exception after max retries
    """
    for attempt in range(max_retries):
        try:
            return client.chat.completions.create(
                model=model,
                messages=messages
            )
        
        except Exception as e:
            error_str = str(e)
            
            # Don't retry authentication errors
            if "401" in error_str or "invalid_api_key" in error_str:
                raise
            
            # Don't retry invalid requests
            if "400" in error_str or "invalid_request" in error_str:
                raise
            
            # Calculate delay with exponential backoff + jitter
            if attempt < max_retries - 1:
                delay = min(base_delay * (2 ** attempt), max_delay)
                jitter = random.uniform(0, delay * 0.1)
                wait_time = delay + jitter
                
                print(f"Attempt {attempt + 1} failed: {e}")
                print(f"Retrying in {wait_time:.2f}s...")
                time.sleep(wait_time)
            else:
                print(f"Max retries ({max_retries}) exceeded")
                raise

# Usage
client = Groq(api_key="your_api_key")

try:
    response = groq_with_retry(
        client=client,
        model="llama-3.3-70b-versatile",
        messages=[{"role": "user", "content": "Explain quantum computing"}]
    )
    print(response.choices[0].message.content)
except Exception as e:
    print(f"Failed after retries: {e}")

2. Implement Multi-Provider Fallback Strategy

Don't put all eggs in one basket. Implement graceful fallback to alternative providers:

from groq import Groq
from openai import OpenAI
import anthropic

class MultiProviderLLM:
    """
    LLM client with automatic failover across providers.
    Priority: Groq (speed) → OpenAI (reliability) → Anthropic (quality)
    """
    
    def __init__(self, groq_key, openai_key, anthropic_key):
        self.groq = Groq(api_key=groq_key)
        self.openai = OpenAI(api_key=openai_key)
        self.anthropic = anthropic.Anthropic(api_key=anthropic_key)
        
        self.provider_status = {
            "groq": True,
            "openai": True,
            "anthropic": True
        }
    
    def generate(self, prompt: str, max_tokens: int = 500):
        """
        Generate response with automatic provider failover.
        """
        
        # Try Groq first (fastest)
        if self.provider_status["groq"]:
            try:
                response = self.groq.chat.completions.create(
                    model="mixtral-8x7b-32768",
                    messages=[{"role": "user", "content": prompt}],
                    max_tokens=max_tokens,
                    timeout=10.0
                )
                return {
                    "content": response.choices[0].message.content,
                    "provider": "groq",
                    "model": "mixtral-8x7b-32768"
                }
            except Exception as e:
                print(f"Groq failed: {e}")
                self.provider_status["groq"] = False
        
        # Fallback to OpenAI
        if self.provider_status["openai"]:
            try:
                response = self.openai.chat.completions.create(
                    model="gpt-3.5-turbo",
                    messages=[{"role": "user", "content": prompt}],
                    max_tokens=max_tokens
                )
                return {
                    "content": response.choices[0].message.content,
                    "provider": "openai",
                    "model": "gpt-3.5-turbo"
                }
            except Exception as e:
                print(f"OpenAI failed: {e}")
                self.provider_status["openai"] = False
        
        # Last resort: Anthropic
        try:
            response = self.anthropic.messages.create(
                model="claude-3-haiku-20240307",
                max_tokens=max_tokens,
                messages=[{"role": "user", "content": prompt}]
            )
            return {
                "content": response.content[0].text,
                "provider": "anthropic",
                "model": "claude-3-haiku"
            }
        except Exception as e:
            raise Exception(f"All providers failed. Last error: {e}")

# Usage
llm = MultiProviderLLM(
    groq_key="your_groq_key",
    openai_key="your_openai_key",
    anthropic_key="your_anthropic_key"
)

result = llm.generate("What is machine learning?")
print(f"Response from {result['provider']} ({result['model']}):")
print(result['content'])

3. Implement Request Queuing and Async Processing

For non-real-time workloads, queue requests during outages and process them when service recovers:

import asyncio
from datetime import datetime
from collections import deque

class GroqRequestQueue:
    """
    Queue system for Groq requests during outages.
    Automatically retries when service recovers.
    """
    
    def __init__(self, client: Groq):
        self.client = client
        self.queue = deque()
        self.processing = False
        
    def add_request(self, model: str, messages: list, callback=None):
        """Add request to queue."""
        request = {
            "model": model,
            "messages": messages,
            "callback": callback,
            "timestamp": datetime.now(),
            "attempts": 0
        }
        self.queue.append(request)
        print(f"Added request to queue. Queue size: {len(self.queue)}")
    
    async def process_queue(self, max_concurrent: int = 5):
        """Process queued requests with concurrency limit."""
        if self.processing:
            return
        
        self.processing = True
        print(f"Processing {len(self.queue)} queued requests...")
        
        while self.queue:
            # Process up to max_concurrent requests simultaneously
            batch = []
            for _ in range(min(max_concurrent, len(self.queue))):
                if self.queue:
                    batch.append(self.queue.popleft())
            
            # Process batch concurrently
            tasks = [self._process_request(req) for req in batch]
            await asyncio.gather(*tasks, return_exceptions=True)
            
            # Small delay between batches to avoid rate limiting
            await asyncio.sleep(0.5)
        
        self.processing = False
        print("Queue processing complete")
    
    async def _process_request(self, request):
        """Process individual request."""
        try:
            response = self.client.chat.completions.create(
                model=request["model"],
                messages=request["messages"]
            )
            
            # Execute callback if provided
            if request["callback"]:
                request["callback"](response)
            
            print(f"✓ Processed request from {request['timestamp']}")
            return response
            
        except Exception as e:
            request["attempts"] += 1
            
            if request["attempts"] < 3:
                # Re-queue for retry
                self.queue.append(request)
                print(f"✗ Request failed, re-queued (attempt {request['attempts']})")
            else:
                print(f"✗ Request permanently failed after 3 attempts: {e}")

# Usage
queue = GroqRequestQueue(client=Groq(api_key="your_key"))

# Add requests during outage
def handle_response(response):
    print(f"Got response: {response.choices[0].message.content[:50]}...")

queue.add_request(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "Explain AI"}],
    callback=handle_response
)

# Process queue when service recovers
asyncio.run(queue.process_queue())

4. Implement Circuit Breaker Pattern

Prevent cascading failures by automatically stopping requests to a failing service:

from enum import Enum
from datetime import datetime, timedelta

class CircuitState(Enum):
    CLOSED = "closed"  # Normal operation
    OPEN = "open"      # Service is down, reject requests
    HALF_OPEN = "half_open"  # Testing if service recovered

class CircuitBreaker:
    """
    Circuit breaker for Groq API calls.
    Opens after threshold failures, attempts recovery after timeout.
    """
    
    def __init__(
        self,
        failure_threshold: int = 5,
        recovery_timeout: int = 60,
        half_open_max_calls: int = 3
    ):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.half_open_max_calls = half_open_max_calls
        
        self.state = CircuitState.CLOSED
        self.failure_count = 0
        self.last_failure_time = None
        self.half_open_calls = 0
    
    def call(self, func, *args, **kwargs):
        """Execute function with circuit breaker protection."""
        
        # Check if we should attempt recovery
        if self.state == CircuitState.OPEN:
            if self._should_attempt_reset():
                self.state = CircuitState.HALF_OPEN
                self.half_open_calls = 0
                print("Circuit breaker: Attempting recovery (HALF_OPEN)")
            else:
                raise Exception(
                    f"Circuit breaker OPEN. Service unavailable. "
                    f"Retry in {self._time_until_retry()}s"
                )
        
        # Limit calls in HALF_OPEN state
        if self.state == CircuitState.HALF_OPEN:
            if self.half_open_calls >= self.half_open_max_calls:
                raise Exception("Circuit breaker: Max half-open calls reached")
            self.half_open_calls += 1
        
        # Execute the function
        try:
            result = func(*args, **kwargs)
            self._on_success()
            return result
        except Exception as e:
            self._on_failure()
            raise
    
    def _on_success(self):
        """Handle successful call."""
        if self.state == CircuitState.HALF_OPEN:
            print("Circuit breaker: Recovery successful (CLOSED)")
            self.state = CircuitState.CLOSED
        
        self.failure_count = 0
    
    def _on_failure(self):
        """Handle failed call."""
        self.failure_count += 1
        self.last_failure_time = datetime.now()
        
        if self.failure_count >= self.failure_threshold:
            self.state = CircuitState.OPEN
            print(f"Circuit breaker: OPEN after {self.failure_count} failures")
    
    def _should_attempt_reset(self):
        """Check if enough time has passed to attempt recovery."""
        if not self.last_failure_time:
            return True
        
        elapsed = (datetime.now() - self.last_failure_time).total_seconds()
        return elapsed >= self.recovery_timeout
    
    def _time_until_retry(self):
        """Calculate seconds until retry attempt."""
        if not self.last_failure_time:
            return 0
        
        elapsed = (datetime.now() - self.last_failure_time).total_seconds()
        return max(0, self.recovery_timeout - elapsed)

# Usage
breaker = CircuitBreaker(failure_threshold=3, recovery_timeout=30)
client = Groq(api_key="your_key")

def make_groq_call():
    return client.chat.completions.create(
        model="mixtral-8x7b-32768",
        messages=[{"role": "user", "content": "test"}]
    )

try:
    response = breaker.call(make_groq_call)
    print("Success:", response.choices[0].message.content)
except Exception as e:
    print(f"Error: {e}")

5. Communicate Proactively with Users

In-app status indicators:

def get_ai_status_message():
    """
    Check Groq status and return user-friendly message.
    """
    try:
        # Quick health check
        client.chat.completions.create(
            model="mixtral-8x7b-32768",
            messages=[{"role": "user", "content": "hi"}],
            max_tokens=1,
            timeout=5
        )
        return None  # No message needed, service is healthy
    
    except Exception as e:
        if "rate_limit" in str(e):
            return {
                "type": "warning",
                "message": "⚠️ High usage detected. Responses may be slower than usual."
            }
        else:
            return {
                "type": "error",
                "message": "🔴 AI service temporarily unavailable. Our team is working on it."
            }

# Display in your UI
status = get_ai_status_message()
if status:
    # Show banner, toast notification, or status badge
    display_status_banner(status["message"], status["type"])

Email notifications for critical users:

def notify_affected_users(incident_details):
    """
    Send email to users affected by Groq outage.
    """
    message = f"""
    Subject: AI Service Disruption Update
    
    We're currently experiencing issues with our AI response system due to 
    our infrastructure provider's outage.
    
    Status: {incident_details['status']}
    Impact: {incident_details['impact']}
    Estimated resolution: {incident_details['eta']}
    
    We're actively monitoring the situation and will update you when service 
    is restored. We apologize for any inconvenience.
    
    Track live status: https://apistatuscheck.com/api/groq
    
    - Your Team
    """
    
    # Send to affected users
    for user in get_active_ai_users():
        send_email(user.email, message)

6. Monitor Groq Status Automatically

Set up comprehensive monitoring to detect issues before users report them:

import requests
from datetime import datetime

class GroqHealthMonitor:
    """
    Automated health monitoring for Groq API.
    """
    
    def __init__(self, api_key: str, alert_webhook: str = None):
        self.client = Groq(api_key=api_key)
        self.alert_webhook = alert_webhook
        self.last_status = "healthy"
    
    def health_check(self):
        """
        Perform comprehensive health check.
        Returns dict with status and metrics.
        """
        start_time = time.time()
        
        health = {
            "timestamp": datetime.now().isoformat(),
            "status": "healthy",
            "latency_ms": None,
            "models_available": [],
            "models_unavailable": [],
            "errors": []
        }
        
        # Test primary model
        try:
            response = self.client.chat.completions.create(
                model="llama-3.3-70b-versatile",
                messages=[{"role": "user", "content": "test"}],
                max_tokens=5,
                timeout=10
            )
            
            latency = (time.time() - start_time) * 1000
            health["latency_ms"] = round(latency, 2)
            health["models_available"].append("llama-3.3-70b-versatile")
            
            # Check latency thresholds
            if latency > 5000:
                health["status"] = "degraded"
                health["errors"].append(f"High latency: {latency:.0f}ms")
            
        except Exception as e:
            health["status"] = "down"
            health["errors"].append(f"Primary model failed: {str(e)}")
        
        # Alert if status changed
        if health["status"] != self.last_status:
            self._send_alert(health)
            self.last_status = health["status"]
        
        return health
    
    def _send_alert(self, health):
        """Send alert via webhook."""
        if not self.alert_webhook:
            return
        
        alert_message = {
            "text": f"🚨 Groq Status Changed: {health['status'].upper()}",
            "details": health
        }
        
        try:
            requests.post(self.alert_webhook, json=alert_message, timeout=5)
        except Exception as e:
            print(f"Failed to send alert: {e}")

# Usage - run this in a cron job or background task
monitor = GroqHealthMonitor(
    api_key="your_key",
    alert_webhook="https://hooks.slack.com/services/YOUR/WEBHOOK"
)

health = monitor.health_check()
print(f"Groq Status: {health['status']}")
if health['latency_ms']:
    print(f"Latency: {health['latency_ms']}ms")
if health['errors']:
    print(f"Errors: {', '.join(health['errors'])}")

7. Post-Outage Recovery Checklist

Once Groq service is restored:

1. Process queued requests - Clear your request queue and process delayed jobs
2. Verify all models - Test each model you use to confirm availability
3. Check rate limits - Verify rate limits reset properly and aren't stuck
4. Review error logs - Analyze what failed and why during the outage
5. Update incident documentation - Record what happened for future reference
6. Test failover systems - Verify your fallback providers worked correctly
7. Notify stakeholders - Update users that service is restored
8. Review costs - Check if fallback providers incurred unexpected costs
9. Improve resilience - Implement lessons learned from the incident

Frequently Asked Questions

How often does Groq go down?

Groq is a relatively new infrastructure provider (launched publicly in 2024) and generally maintains strong uptime. However, as with any cloud service, occasional outages occur due to hardware issues, network problems, or software bugs. Major outages affecting all users are rare (typically 1-3 per year), though specific model availability issues may occur more frequently as Groq scales their LPU infrastructure. Track historical uptime at apistatuscheck.com/api/groq.

What makes Groq different from other LLM providers?

Groq uses custom LPU (Language Processing Unit) chips instead of traditional GPUs for inference. This architecture delivers significantly faster token generation—often 10x faster than GPU-based solutions like OpenAI or Anthropic. However, this specialized hardware also means model availability is more constrained, as each model requires specific LPU optimization. When Groq experiences hardware issues, it may affect specific models while others remain operational.

Should I use Groq for production applications?

Groq is suitable for production use, especially for latency-sensitive applications where speed is critical. However, implement proper resilience patterns:

• Use retry logic with exponential backoff
• Implement fallback providers for critical paths (see Is OpenAI Down? guide)
• Monitor actively with automated health checks
• Consider paid plans for production workloads (better rate limits and support)
• Queue non-critical requests that can tolerate delays

For mission-critical applications, a multi-provider strategy is recommended.

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

The official Groq status page (status.groq.com) is manually updated by Groq's operations 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 Groq inference endpoints, often detecting issues before they're officially reported. Use both for comprehensive monitoring—status.groq.com for official incident details and apistatuscheck.com/api/groq for real-time performance metrics.

How do Groq's rate limits work?

Free tier limits (per minute):

• 30 requests per minute (RPM)
• 20,000 tokens per minute (TPM)
• 14,400 requests per day (RPD)

Paid tier limits:

• Significantly higher limits (varies by plan)
• Dedicated support
• SLA guarantees

Rate limits are enforced per API key. During outages or high load, you may see inconsistent rate limit enforcement or receive 429 errors even when within your quota. If you consistently hit limits during normal operation, consider upgrading or implementing request batching.

Can I use Groq with the OpenAI SDK?

Yes! Groq provides an OpenAI-compatible API endpoint. You can use the official OpenAI Python SDK:

from openai import OpenAI

client = OpenAI(
    api_key="your_groq_api_key",
    base_url="https://api.groq.com/openai/v1"
)

response = client.chat.completions.create(
    model="mixtral-8x7b-32768",
    messages=[{"role": "user", "content": "Hello!"}]
)

This makes it easy to switch between OpenAI and Groq, or implement fallback strategies.

What should I do if only specific Groq models are down?

If a specific model (e.g., Llama 3.3 70B) is unavailable but others work:

1. Switch to an alternative model temporarily (e.g., Mixtral 8x7B or Llama 3.1)
2. Check status.groq.com for model-specific incident updates
3. Adjust your prompts if needed for the alternative model's capabilities
4. Monitor for resolution using automated health checks
5. Document the fallback in your incident log

Model-specific issues are common with Groq since different models run on different LPU configurations. Having a fallback model preference list in your code helps maintain service continuity.

How can I get alerted immediately when Groq goes down?

Set up multi-channel alerting:

1. API Status Check alerts: Subscribe at apistatuscheck.com/api/groq for instant notifications via:

   • Email
   • Slack
   • Discord
   • Webhook (integrate with your incident management)

2. Official Groq status: Subscribe to updates at status.groq.com

3. Custom monitoring: Implement your own health checks (see code examples above) that run every 1-5 minutes and alert your team

4. Application monitoring: Use APM tools (Datadog, New Relic, Sentry) to track Groq API error rates

Best practice: Use multiple alert channels to ensure you're notified even if one channel fails.

Is there a Groq status API I can query programmatically?

Groq doesn't currently provide an official programmatic status API. However, you can:

1. Use API Status Check API: Query apistatuscheck.com/api/groq for real-time status data (JSON API available)

2. Perform your own health checks: Make lightweight test calls to verify availability:

def check_groq_status():
    try:
        client.chat.completions.create(
            model="mixtral-8x7b-32768",
            messages=[{"role": "user", "content": "status check"}],
            max_tokens=1,
            timeout=10
        )
        return {"status": "operational", "latency_ms": response_time}
    except Exception as e:
        return {"status": "degraded", "error": str(e)}

3. Monitor status page RSS: Parse status.groq.com's RSS feed for incident updates

What alternatives should I consider to Groq?

For high-performance LLM inference, consider these alternatives:

• OpenAI - Most reliable, higher latency, more expensive
• Anthropic Claude - Excellent quality, good latency, higher cost
• Together AI - Open-source models, competitive pricing
• Replicate - Wide model selection, pay-per-use pricing
• Anyscale - Ray-based serving, good for scale
• Self-hosted - Maximum control, requires infrastructure expertise

Multi-provider strategy: Many production applications use Groq for speed as primary, with OpenAI or Anthropic as fallback for reliability.

Stay Ahead of Groq Outages

Don't let LLM infrastructure issues disrupt your AI applications. Subscribe to real-time Groq monitoring and get notified instantly when performance degrades or outages occur—before your users notice.

API Status Check monitors Groq 24/7 with:

• ⚡ 60-second health checks across all major models
• 📊 Real-time latency and performance tracking
• 🚨 Instant alerts via email, Slack, Discord, or webhook
• 📈 Historical uptime data and incident reports
• 🔄 Multi-API monitoring for your entire AI stack (OpenAI, Anthropic, Together AI, and more)

Start monitoring Groq now →

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