Is Cerebras Down? How to Check Cerebras Inference Status in Real-Time

Quick Answer: To check if Cerebras is down, visit apistatuscheck.com/api/cerebras for real-time monitoring, or check the official status.cerebras.ai page. Common signs include inference timeout errors, model unavailability, slower-than-expected response times, API connection failures, and authentication errors.

When you've built your application around ultra-fast LLM inference, every second of latency matters. Cerebras powers real-time AI applications with their revolutionary wafer-scale chip architecture, delivering inference speeds that crush traditional GPU clusters. But when Cerebras experiences issues, your time-sensitive applications—from live coding assistants to real-time customer service bots—can grind to a halt. This guide shows you exactly how to verify Cerebras status and respond when issues arise.

How to Check Cerebras Status in Real-Time

1. API Status Check (Fastest Method)

The quickest way to verify Cerebras operational status is through apistatuscheck.com/api/cerebras. This real-time monitoring service:

• Tests actual inference endpoints every 60 seconds
• Measures tokens-per-second performance in real-time
• Tracks response latency with millisecond precision
• Monitors model availability for all supported models
• Compares performance against Cerebras' speed benchmarks
• Provides instant alerts when inference degrades

Unlike status pages that rely on manual updates, API Status Check performs active health checks against Cerebras' production inference API, giving you the most accurate real-time picture of service performance—critical for a platform where speed is the entire value proposition.

2. Official Cerebras Status Page

Cerebras maintains status.cerebras.ai as their official communication channel for service incidents. The page displays:

• Current operational status for inference API
• Active incidents and investigations
• Scheduled maintenance windows
• Historical incident reports
• Model-specific availability status
• Regional performance metrics

Pro tip: Subscribe to status updates via email or webhook to receive immediate notifications when incidents occur. Given Cerebras' focus on low-latency use cases, even brief degradations can impact your applications significantly.

3. Test Inference API Directly

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

curl https://api.cerebras.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $CEREBRAS_API_KEY" \
  -d '{
    "model": "llama3.1-8b",
    "messages": [{"role": "user", "content": "Test"}],
    "max_tokens": 100
  }' \
  -w "\nTime: %{time_total}s\n"

What to look for:

• Response time significantly higher than usual (>2 seconds for short prompts)
• HTTP error codes (500, 502, 503, 504)
• Model unavailable errors
• Authentication failures that shouldn't occur

Normal Cerebras performance: Most inference requests complete in under 1 second, often 200-500ms for typical prompts. If you're seeing 3-5+ second responses consistently, something is degraded.

4. Monitor Through Your Application Logs

Cerebras issues often show patterns in your application telemetry:

import time
from cerebras.cloud.sdk import Cerebras

client = Cerebras(api_key="your_api_key")

start = time.time()
try:
    response = client.chat.completions.create(
        messages=[{"role": "user", "content": "Hello"}],
        model="llama3.1-8b",
        max_tokens=100
    )
    latency = time.time() - start
    
    # Alert on unusual latency (adjust threshold for your use case)
    if latency > 2.0:
        log.warning(f"Cerebras latency spike: {latency:.2f}s")
        
except Exception as e:
    log.error(f"Cerebras API error: {e}")

Track metrics like:

• P50, P95, P99 latency percentiles
• Error rates by error type
• Model availability patterns
• Tokens-per-second throughput

5. Community Channels and Social Media

During outages, developers often report issues before official announcements:

• Twitter/X: Search for "Cerebras down" or "@CerebrasAI"
• Discord communities: AI/ML developer servers
• Reddit: r/LocalLLaMA, r/MachineLearning
• GitHub Issues: Check Cerebras SDK repositories

If you see multiple independent reports of the same issue, it's likely a platform-wide problem rather than your specific setup.

Common Cerebras Issues and How to Identify Them

API Connection Failures

Symptoms:

• Cannot establish connection to api.cerebras.ai
• SSL/TLS handshake failures
• DNS resolution errors
• Connection timeout before any response

Error examples:

ConnectionError: HTTPSConnectionPool(host='api.cerebras.ai', port=443)
requests.exceptions.Timeout: Request timed out

What it means: Complete infrastructure issues preventing any API communication. This is rare but indicates serious problems with Cerebras' edge network or load balancers.

Troubleshooting:

# Test basic connectivity
ping api.cerebras.ai

# Check DNS resolution
nslookup api.cerebras.ai

# Test HTTPS connectivity
curl -I https://api.cerebras.ai

Rate Limiting Issues

Symptoms:

• 429 "Too Many Requests" errors
• Rate limit errors appearing at unexpectedly low request volumes
• Inconsistent rate limiting (sometimes blocked, sometimes not)

Cerebras rate limits (typical tiers):

• Free tier: 30 requests/minute, 1M tokens/month
• Paid tier: 600 requests/minute, higher token quotas
• Enterprise: Custom limits

Error response:

{
  "error": {
    "message": "Rate limit exceeded",
    "type": "rate_limit_error",
    "code": "rate_limit_exceeded"
  }
}

Distinguishing outage vs. legitimate limits:

• Legitimate: You're making requests above your quota
• Outage indicator: Rate limits triggering well below your normal usage or quota, suggesting backend throttling to manage load

Model Availability Issues

Symptoms:

• Specific models returning "model not available" errors
• Model endpoints timing out while others work
• Reduced model selection in API responses

Common error:

{
  "error": {
    "message": "Model llama3.1-70b is temporarily unavailable",
    "type": "model_error",
    "code": "model_unavailable"
  }
}

Cerebras supported models (as of 2026):

• Llama 3.1 (8B, 70B)
• Llama 3.3 (70B)
• Llama 3.2 (1B, 3B)

What it means: Cerebras' wafer-scale engine for a specific model may be experiencing issues or undergoing maintenance. They may route traffic to fallback infrastructure with degraded performance.

Latency Spikes (The Biggest Red Flag)

This is the most critical issue for Cerebras users because speed is their core value proposition. If Cerebras performs like a standard GPU cluster, you lose your competitive advantage.

Normal Cerebras performance:

• Simple prompts (<100 tokens): 200-500ms
• Medium complexity (500 tokens): 500ms-1.5s
• Large outputs (2000+ tokens): 1.5-3s
• Tokens per second: 800-2000+ TPS

Concerning latency patterns:

• Consistent responses over 3-5 seconds for simple prompts
• Tokens-per-second dropping below 200 TPS
• Wild variance (200ms one request, 8s the next)
• Streaming delays (long pauses between tokens)

Monitoring code:

import time
from cerebras.cloud.sdk import Cerebras

def monitor_cerebras_performance():
    client = Cerebras(api_key="your_api_key")
    
    test_prompt = "Write a haiku about artificial intelligence."
    
    start = time.time()
    response = client.chat.completions.create(
        messages=[{"role": "user", "content": test_prompt}],
        model="llama3.1-8b",
        max_tokens=100
    )
    latency = time.time() - start
    
    tokens = len(response.choices[0].message.content.split())
    tps = tokens / latency if latency > 0 else 0
    
    print(f"Latency: {latency:.2f}s | Tokens: {tokens} | TPS: {tps:.1f}")
    
    # Alert on degraded performance
    if latency > 3.0 or tps < 200:
        alert_on_call("Cerebras performance degraded", {
            "latency": latency,
            "tps": tps
        })
    
    return latency, tps

# Run every 5 minutes
while True:
    monitor_cerebras_performance()
    time.sleep(300)

Authentication Errors

Symptoms:

• Valid API keys suddenly returning 401 Unauthorized
• Intermittent authentication failures
• "Invalid API key" errors for keys that worked previously

Error response:

{
  "error": {
    "message": "Invalid authentication credentials",
    "type": "authentication_error",
    "code": "invalid_api_key"
  }
}

Distinguishing outage vs. configuration issues:

• Configuration issue: Consistent failure with specific key, works with other keys
• Outage indicator: Multiple valid keys failing intermittently, or authentication succeeding then failing on subsequent requests

Verification:

# Test with known-good API key
curl https://api.cerebras.ai/v1/models \
  -H "Authorization: Bearer $CEREBRAS_API_KEY"

# Should return list of available models if auth is working

The Real Impact When Cerebras Goes Down

Real-Time Applications Break Immediately

Unlike batch processing workloads that can tolerate delays, Cerebras' typical use cases demand instant response:

Live Coding Assistants:

• GitHub Copilot alternatives relying on Cerebras
• IDE extensions providing real-time code completion
• Code review tools with instant feedback

Impact: Developers experience frozen autocomplete, timeout errors, and must switch to slower alternatives or work without assistance entirely.

Conversational AI:

• Customer service chatbots
• Interactive voice assistants
• Real-time translation services

Impact: Conversations stall mid-interaction. Users perceive chatbots as "broken" when responses take 10+ seconds instead of feeling instantaneous.

Gaming and Interactive Entertainment:

• AI NPCs with dynamic dialogue
• Real-time story generation
• Interactive game masters

Impact: Immersion breaks completely. A 5-second delay for an NPC response destroys the gaming experience.

Competitive Disadvantage Against Alternatives

Organizations choose Cerebras specifically for speed. During outages or performance degradation:

• You lose your edge: If Cerebras performs like Groq or Together AI, you're paying premium prices for commodity performance
• Users notice immediately: Applications built on "instant" response expectations create user frustration when speeds normalize to industry standard
• Difficult to justify cost: Cerebras' pricing is competitive but assumes exceptional performance. Degraded performance changes the value equation.

Infrastructure Failover Complexity

Unlike simple web services, LLM inference failover is complex:

Model compatibility: Cerebras uses standard models (Llama family), but:

• Prompt formatting may differ slightly between providers
• Temperature/sampling parameters behave differently
• Output quality and style vary

Performance expectations: Users accustomed to 200ms responses won't tolerate 3-5 second responses from fallback providers

Cost implications: Emergency failover to OpenAI or Anthropic Claude can cost 10-100x more per token than Cerebras

Token Budget Burn

If you're on a token-limited plan and Cerebras experiences issues:

• Retries burn quota: Failed requests that you retry count against monthly limits
• Timeout waste: Requests that timeout may have consumed tokens before failing
• Testing costs: Repeatedly checking if service is restored consumes additional tokens

Research and Development Delays

For AI researchers and developers:

• Experiment iteration slows: Cannot rapidly test prompt variations
• Model evaluation blocked: Comparative benchmarking requires consistent performance
• Demo failures: Live product demonstrations fail at critical moments
• Launch delays: Cannot deploy new features relying on Cerebras inference

What to Do When Cerebras Goes Down: Incident Response Playbook

1. Verify It's Actually Cerebras (Not Your Code)

Before assuming platform issues, rule out local problems:

import sys
import requests
from cerebras.cloud.sdk import Cerebras

def diagnose_cerebras_issue():
    """Systematic diagnostic to isolate the problem"""
    
    results = {}
    
    # Test 1: Basic connectivity
    try:
        response = requests.get("https://api.cerebras.ai", timeout=5)
        results['connectivity'] = 'OK'
    except Exception as e:
        results['connectivity'] = f'FAILED: {e}'
        return results  # If we can't connect at all, stop here
    
    # Test 2: Authentication
    client = Cerebras(api_key="your_api_key")
    try:
        models = client.models.list()
        results['authentication'] = 'OK'
    except Exception as e:
        results['authentication'] = f'FAILED: {e}'
        return results
    
    # Test 3: Simple inference
    try:
        start = time.time()
        response = client.chat.completions.create(
            messages=[{"role": "user", "content": "Hi"}],
            model="llama3.1-8b",
            max_tokens=10
        )
        latency = time.time() - start
        
        if latency < 2.0:
            results['inference'] = f'OK ({latency:.2f}s)'
        else:
            results['inference'] = f'DEGRADED ({latency:.2f}s - expect <2s)'
            
    except Exception as e:
        results['inference'] = f'FAILED: {e}'
    
    # Test 4: Streaming (if supported)
    try:
        stream = client.chat.completions.create(
            messages=[{"role": "user", "content": "Count to 3"}],
            model="llama3.1-8b",
            stream=True,
            max_tokens=20
        )
        first_token_time = None
        for chunk in stream:
            if first_token_time is None:
                first_token_time = time.time()
                break
        
        ttft = first_token_time - start if first_token_time else None
        results['streaming'] = f'OK (TTFT: {ttft:.2f}s)' if ttft else 'FAILED'
        
    except Exception as e:
        results['streaming'] = f'FAILED: {e}'
    
    return results

# Run diagnostics
print("Running Cerebras diagnostics...")
results = diagnose_cerebras_issue()
for test, result in results.items():
    print(f"{test.capitalize()}: {result}")

2. Implement Intelligent Retry Logic

Don't hammer a degraded service, but do retry smartly:

import time
import random
from functools import wraps

def cerebras_retry_with_backoff(
    max_retries=3,
    base_delay=1.0,
    max_delay=16.0,
    exponential_base=2,
    jitter=True
):
    """
    Decorator for Cerebras API calls with exponential backoff
    """
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                    
                except Exception as e:
                    # Don't retry on authentication errors or bad requests
                    if 'authentication' in str(e).lower() or 'invalid' in str(e).lower():
                        raise
                    
                    if attempt == max_retries - 1:
                        raise
                    
                    # Calculate backoff delay
                    delay = min(base_delay * (exponential_base ** attempt), max_delay)
                    
                    # Add jitter to prevent thundering herd
                    if jitter:
                        delay *= (0.5 + random.random())
                    
                    print(f"Cerebras request failed (attempt {attempt + 1}/{max_retries}), "
                          f"retrying in {delay:.2f}s: {e}")
                    
                    time.sleep(delay)
            
        return wrapper
    return decorator

# Usage
@cerebras_retry_with_backoff(max_retries=5)
def generate_completion(prompt, model="llama3.1-8b"):
    client = Cerebras(api_key="your_api_key")
    return client.chat.completions.create(
        messages=[{"role": "user", "content": prompt}],
        model=model,
        max_tokens=500
    )

3. Implement Graceful Degradation with Fallback Providers

OpenAI-Compatible Fallback (since Cerebras uses OpenAI-compatible API):

from openai import OpenAI

class MultiProviderLLM:
    """Intelligent LLM client with automatic failover"""
    
    def __init__(self):
        self.providers = [
            {
                'name': 'cerebras',
                'client': Cerebras(api_key="cerebras_key"),
                'model': 'llama3.1-8b',
                'priority': 1,  # Try first
                'cost_per_1k': 0.10,
                'expected_latency': 0.5
            },
            {
                'name': 'groq',
                'client': OpenAI(
                    api_key="groq_key",
                    base_url="https://api.groq.com/openai/v1"
                ),
                'model': 'llama3.1-8b-instant',
                'priority': 2,  # Fast fallback
                'cost_per_1k': 0.10,
                'expected_latency': 0.7
            },
            {
                'name': 'together',
                'client': OpenAI(
                    api_key="together_key",
                    base_url="https://api.together.xyz/v1"
                ),
                'model': 'meta-llama/Llama-3.1-8B-Instruct-Turbo',
                'priority': 3,  # Reliable fallback
                'cost_per_1k': 0.20,
                'expected_latency': 1.5
            }
        ]
        
        # Sort by priority
        self.providers.sort(key=lambda x: x['priority'])
    
    def generate(self, messages, max_tokens=500, timeout=10):
        """Try providers in priority order until one succeeds"""
        
        for provider in self.providers:
            try:
                print(f"Attempting {provider['name']}...")
                
                start = time.time()
                response = provider['client'].chat.completions.create(
                    messages=messages,
                    model=provider['model'],
                    max_tokens=max_tokens,
                    timeout=timeout
                )
                latency = time.time() - start
                
                print(f"✓ {provider['name']} succeeded in {latency:.2f}s")
                
                return {
                    'response': response,
                    'provider': provider['name'],
                    'latency': latency,
                    'cost_per_1k': provider['cost_per_1k']
                }
                
            except Exception as e:
                print(f"✗ {provider['name']} failed: {e}")
                continue
        
        raise Exception("All LLM providers failed")

# Usage
llm = MultiProviderLLM()
result = llm.generate([{"role": "user", "content": "Hello!"}])
print(f"Provider: {result['provider']}, Latency: {result['latency']:.2f}s")

4. Queue Requests During Outages

For non-real-time workloads, queue requests to process when service resumes:

import redis
import json
from datetime import datetime

class CerebrasRequestQueue:
    """Queue inference requests during outages"""
    
    def __init__(self, redis_client):
        self.redis = redis_client
        self.queue_key = "cerebras:request_queue"
        self.processing_key = "cerebras:processing"
    
    def enqueue(self, messages, model="llama3.1-8b", max_tokens=500, metadata=None):
        """Add request to queue"""
        request = {
            'messages': messages,
            'model': model,
            'max_tokens': max_tokens,
            'metadata': metadata or {},
            'queued_at': datetime.utcnow().isoformat()
        }
        
        self.redis.rpush(self.queue_key, json.dumps(request))
        print(f"Queued Cerebras request (queue size: {self.redis.llen(self.queue_key)})")
    
    def process_queue(self, client):
        """Process all queued requests"""
        processed = 0
        failed = 0
        
        while True:
            # Atomically move request from queue to processing
            request_json = self.redis.lpop(self.queue_key)
            if not request_json:
                break
            
            request = json.loads(request_json)
            
            try:
                response = client.chat.completions.create(
                    messages=request['messages'],
                    model=request['model'],
                    max_tokens=request['max_tokens']
                )
                
                # Store result or trigger callback
                print(f"✓ Processed queued request: {request['metadata']}")
                processed += 1
                
            except Exception as e:
                # Re-queue on failure
                self.redis.rpush(self.queue_key, request_json)
                print(f"✗ Failed to process, re-queued: {e}")
                failed += 1
                
                # Stop processing if service is still down
                if failed >= 3:
                    break
        
        return processed, failed

# Usage during outage
queue = CerebrasRequestQueue(redis.Redis(host='localhost'))

# Instead of failing immediately
try:
    response = cerebras_client.generate(prompt)
except:
    queue.enqueue(
        messages=[{"role": "user", "content": prompt}],
        metadata={"user_id": "123", "request_id": "abc"}
    )
    # Return cached/default response to user

# Later, when service resumes
processed, failed = queue.process_queue(cerebras_client)
print(f"Processed {processed} queued requests, {failed} still pending")

5. User Communication Strategy

In-app messaging:

# Example notification system
def notify_users_cerebras_issue():
    """Display status banner when Cerebras is degraded"""
    
    # Check if Cerebras is down
    if check_cerebras_status() == 'degraded':
        return {
            'show_banner': True,
            'message': 'AI responses may be slower than usual. We\'re using backup systems.',
            'severity': 'warning',
            'show_status_link': True,
            'status_url': 'https://apistatuscheck.com/api/cerebras'
        }
    
    return {'show_banner': False}

Email template for affected users:

Subject: Brief AI Service Delays - Now Resolved

Hi [User],

We noticed you may have experienced slower AI responses between [START_TIME] and [END_TIME] today.

What happened:
Our primary AI inference provider (Cerebras) experienced performance degradation. Your requests were automatically routed to backup systems, which meant response times of 2-3 seconds instead of our usual sub-second performance.

Current status:
Fully resolved. We're back to lightning-fast responses.

What we're doing:
- Enhanced monitoring to detect issues even faster
- Improved automatic failover systems
- Added redundancy to prevent similar delays

We appreciate your patience!

[Team]

6. Post-Outage Analysis

After service restoration, conduct a systematic review:

def analyze_outage_impact(start_time, end_time):
    """
    Analyze the impact of a Cerebras outage on your application
    """
    
    # Query your application logs/database
    metrics = {
        'total_requests': 0,
        'failed_requests': 0,
        'requests_to_fallback': 0,
        'avg_latency_during': 0,
        'avg_latency_normal': 0,
        'affected_users': set(),
        'lost_revenue': 0
    }
    
    # Calculate metrics...
    
    # Generate report
    report = f"""
    Cerebras Outage Impact Report
    ============================
    Duration: {end_time - start_time}
    
    Request Impact:
    - Total requests: {metrics['total_requests']}
    - Failed: {metrics['failed_requests']} ({metrics['failed_requests']/metrics['total_requests']*100:.1f}%)
    - Fallback used: {metrics['requests_to_fallback']}
    
    Performance Impact:
    - Normal latency: {metrics['avg_latency_normal']:.2f}s
    - During outage: {metrics['avg_latency_during']:.2f}s
    - Degradation: {(metrics['avg_latency_during']/metrics['avg_latency_normal'] - 1)*100:.0f}%
    
    User Impact:
    - Affected users: {len(metrics['affected_users'])}
    - Estimated revenue impact: ${metrics['lost_revenue']:.2f}
    
    Recommendations:
    - [Specific actions based on your analysis]
    """
    
    return report

Frequently Asked Questions

How often does Cerebras go down?

Cerebras maintains strong uptime, but as a relatively newer player in the inference space (compared to established providers), occasional performance degradations or model-specific issues can occur. Major outages affecting all customers are rare (typically 1-3 times per year), though you may experience regional or model-specific issues more frequently. Track historical uptime at apistatuscheck.com/api/cerebras.

What's Cerebras' typical response time, and when should I be concerned?

Normal performance expectations:

• Simple prompts (10-50 tokens output): 200-500ms
• Medium prompts (100-300 tokens output): 500ms-1.5s
• Large generations (1000+ tokens): 2-4s
• Streaming first token time: <200ms

Be concerned when:

• Simple prompts consistently take >2 seconds
• Tokens-per-second drops below 300 TPS
• Streaming first token time exceeds 1 second
• Response times show extreme variance (200ms then 8s then 400ms)

These patterns indicate either infrastructure issues or that Cerebras is routing your requests to degraded/fallback systems.

Should I use Cerebras as my only inference provider?

For most production applications: No. Implement a multi-provider strategy:

Primary: Cerebras for speed-critical workloads where sub-second latency matters

Fallback 1: Groq (similar speed focus, good compatibility)

Fallback 2: Together AI or OpenAI for reliability when speed matters less than availability

This approach gives you Cerebras' speed advantage while maintaining business continuity during outages.

How does Cerebras compare to other fast inference providers?

Cerebras vs. Groq:

• Both focus on ultra-fast inference with specialized hardware
• Cerebras: Wafer-scale chip architecture
• Groq: Tensor Streaming Processor (TSP) architecture
• Performance is comparable (both sub-second for most queries)
• Choose based on model availability, pricing, and reliability needs

Cerebras vs. standard GPU providers (Together, Replicate):

• 5-10x faster for typical workloads
• Better for real-time applications
• Similar or better pricing per token
• Smaller model selection (focused on Llama family)

What authentication errors are common with Cerebras?

Common auth issues:

1. API key not activated: New keys may take a few minutes to activate
2. Incorrect key format: Ensure no extra spaces or characters
3. Environment variable not loaded: Verify CEREBRAS_API_KEY is set
4. Key revoked: Check Cerebras dashboard for key status
5. IP restrictions: Enterprise accounts may have IP allowlists

Test your key:

curl https://api.cerebras.ai/v1/models \
  -H "Authorization: Bearer $CEREBRAS_API_KEY"

Should return JSON with available models. If 401 error, your key has issues.

Can I get compensated for losses during Cerebras outages?

Cerebras' standard Terms of Service typically include service level commitments but exclude liability for consequential damages (lost revenue, missed opportunities). Enterprise customers may negotiate custom SLAs with uptime guarantees and credits for downtime. Review your specific service agreement or contact Cerebras sales for clarification.

How do I monitor Cerebras performance automatically?

Option 1: Use API Status Check apistatuscheck.com/api/cerebras provides:

• 60-second health checks
• Performance tracking (latency, TPS)
• Instant alerts via email, Slack, Discord, webhook
• Historical data and incident reports

Option 2: Build custom monitoring

import time
from cerebras.cloud.sdk import Cerebras
import requests

def monitor_cerebras():
    """Simple Cerebras health check"""
    client = Cerebras(api_key="your_key")
    
    try:
        start = time.time()
        response = client.chat.completions.create(
            messages=[{"role": "user", "content": "Test"}],
            model="llama3.1-8b",
            max_tokens=10
        )
        latency = time.time() - start
        
        if latency > 2.0:
            # Send alert (Slack, PagerDuty, etc.)
            requests.post("your_webhook_url", json={
                "alert": "Cerebras performance degraded",
                "latency": latency
            })
        
        return "healthy" if latency < 2.0 else "degraded"
        
    except Exception as e:
        # Send critical alert
        requests.post("your_webhook_url", json={
            "alert": "Cerebras API error",
            "error": str(e)
        })
        return "down"

# Run every 60 seconds
import schedule
schedule.every(60).seconds.do(monitor_cerebras)

Option 3: Use observability platforms

• Datadog: Create synthetic tests for Cerebras endpoints
• New Relic: Monitor API performance from your application
• Grafana: Dashboard Cerebras metrics from your logs

Does Cerebras support all the same models as other providers?

No. Cerebras focuses on a curated set of high-performance models, primarily the Llama family:

Currently supported (as of 2026):

• Llama 3.1 8B
• Llama 3.1 70B
• Llama 3.3 70B
• Llama 3.2 1B, 3B

Not available: GPT models, Claude, Mistral, Gemma, and many other families. If your application requires model diversity, you'll need multi-provider infrastructure from the start.

What's the best way to test if Cerebras is actually faster for my use case?

Run a comparative benchmark across providers:

import time
from cerebras.cloud.sdk import Cerebras
from openai import OpenAI

def benchmark_providers(prompt, num_runs=10):
    """Compare inference speed across providers"""
    
    providers = {
        'cerebras': Cerebras(api_key="cerebras_key"),
        'groq': OpenAI(api_key="groq_key", base_url="https://api.groq.com/openai/v1"),
        'together': OpenAI(api_key="together_key", base_url="https://api.together.xyz/v1"),
        'openai': OpenAI(api_key="openai_key")
    }
    
    models = {
        'cerebras': 'llama3.1-8b',
        'groq': 'llama3.1-8b-instant',
        'together': 'meta-llama/Llama-3.1-8B-Instruct-Turbo',
        'openai': 'gpt-4o-mini'
    }
    
    results = {}
    
    for provider_name, client in providers.items():
        latencies = []
        
        for i in range(num_runs):
            try:
                start = time.time()
                response = client.chat.completions.create(
                    messages=[{"role": "user", "content": prompt}],
                    model=models[provider_name],
                    max_tokens=100
                )
                latency = time.time() - start
                latencies.append(latency)
                time.sleep(1)  # Rate limiting courtesy
                
            except Exception as e:
                print(f"{provider_name} error: {e}")
        
        if latencies:
            results[provider_name] = {
                'mean': sum(latencies) / len(latencies),
                'min': min(latencies),
                'max': max(latencies),
                'p95': sorted(latencies)[int(len(latencies) * 0.95)]
            }
    
    # Print results
    print(f"\nBenchmark Results ({num_runs} runs):")
    print(f"Prompt: '{prompt[:50]}...'")
    print("\nProvider | Mean | Min | Max | P95")
    print("-" * 50)
    for provider, metrics in sorted(results.items(), key=lambda x: x[1]['mean']):
        print(f"{provider:12} | {metrics['mean']:.2f}s | {metrics['min']:.2f}s | "
              f"{metrics['max']:.2f}s | {metrics['p95']:.2f}s")
    
    return results

# Test with your actual use case prompts
benchmark_providers("Write a product description for ergonomic office chairs")

Run this periodically to ensure Cerebras continues delivering value for your specific workload.

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• 60-second health checks across all models
• Performance tracking (latency, tokens-per-second)
• Instant alerts via email, Slack, Discord, or webhook
• Historical uptime data and incident reports
• Comparative benchmarks vs. other inference providers

Perfect for:

• Real-time AI applications requiring sub-second responses
• Development teams running on Cerebras infrastructure
• Product managers tracking AI service reliability
• Engineers managing multi-provider inference strategies

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Last updated: February 5, 2026. Cerebras status information is provided in real-time based on active monitoring. For official incident reports, always refer to status.cerebras.ai.