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

Quick Answer: To check if Together AI is down, visit apistatuscheck.com/api/together-ai for real-time monitoring, or check the official status.together.ai page. Common signs include model loading failures, inference timeouts, API authentication errors, streaming response interruptions, and rate limiting issues beyond normal quotas.

When your AI application suddenly stops generating responses, every second counts. Together AI powers thousands of AI applications daily with fast inference for open-source models like Llama, Mistral, and Mixtral. Whether you're building chatbots, content generation pipelines, or AI-powered SaaS products, knowing how to quickly verify Together AI's status can save you critical debugging time and help you make informed decisions about your inference strategy.

How to Check Together AI Status in Real-Time

1. API Status Check (Fastest Method)

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

• Tests actual inference endpoints every 60 seconds
• Measures first-token latency and generation speed
• Tracks model availability across popular models
• Monitors historical uptime over 30/60/90 days
• Provides instant alerts when issues are detected
• Tests both REST and streaming APIs

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

2. Official Together AI Status Page

Together AI maintains status.together.ai 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, Model Loading, Streaming, Authentication)

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

3. Test Direct API Calls

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

import together

together.api_key = "YOUR_API_KEY"

try:
    response = together.Complete.create(
        prompt="Hello, world!",
        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
        max_tokens=50,
        temperature=0.7,
        top_p=0.7,
        top_k=50,
        repetition_penalty=1
    )
    print("Together AI is operational")
    print(f"Response: {response['output']['choices'][0]['text']}")
except Exception as e:
    print(f"Together AI appears to be down: {e}")

Look for connection errors, authentication failures, or timeout exceptions.

4. Check OpenAI-Compatible Endpoint

Together AI offers OpenAI-compatible endpoints, allowing you to test with familiar tooling:

import openai

client = openai.OpenAI(
    api_key="YOUR_TOGETHER_API_KEY",
    base_url="https://api.together.xyz/v1"
)

try:
    response = client.chat.completions.create(
        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
        messages=[
            {"role": "user", "content": "Test message"}
        ]
    )
    print("Together AI OpenAI endpoint is operational")
except Exception as e:
    print(f"Error: {e}")

This helps determine if the issue is specific to Together's SDK or affects all endpoints.

5. Monitor Community Channels

Check Together AI's community for real-time reports:

• Discord community - Often first to report issues
• GitHub issues - Check github.com/togethercomputer for open issues
• Twitter/X - Search for "#TogetherAI down" or "@togethercompute"
• Developer forums - Community reports and discussions

Common Together AI Issues and How to Identify Them

API Rate Limiting

Symptoms:

• HTTP 429 "Too Many Requests" errors
• rate_limit_exceeded error messages
• Requests throttled despite being within documented limits
• Sudden decrease in successful requests

What it means: Together AI implements rate limits to ensure fair usage. During high-demand periods or platform stress, you may hit limits faster than normal. Check your current tier limits:

import together
import time

together.api_key = "YOUR_API_KEY"

try:
    response = together.Complete.create(
        prompt="Test",
        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
        max_tokens=10
    )
except together.error.RateLimitError as e:
    print(f"Rate limit hit: {e}")
    print("Check your tier limits at api.together.xyz/settings/billing")

Normal rate limits vs outage: If you're suddenly rate-limited despite normal usage patterns, this may indicate capacity issues rather than exceeding your quota.

Model Loading Delays

Indicators:

• Long delays (30+ seconds) before first token
• model_loading_timeout errors
• Cold start times exceeding normal patterns
• Requests timing out during model initialization

What's happening: Together AI uses efficient model loading, but during high demand or infrastructure issues:

import time
import together

together.api_key = "YOUR_API_KEY"

start = time.time()
try:
    response = together.Complete.create(
        prompt="Quick test",
        model="meta-llama/Llama-3-70b-chat-hf",
        max_tokens=10
    )
    load_time = time.time() - start
    print(f"Time to first token: {load_time:.2f}s")
    
    if load_time > 10:
        print("⚠️ Unusually slow model loading detected")
except Exception as e:
    print(f"Model loading failed: {e}")

Normal: 1-3 seconds for first token Degraded: 5-15 seconds Outage: 30+ seconds or timeout

Inference Timeouts

Common timeout scenarios:

• Requests hang without response
• Partial responses that never complete
• Connection timeout errors after 60-120 seconds
• Stream interruptions mid-generation

Testing for timeouts:

import together
from requests.exceptions import Timeout, ReadTimeout

together.api_key = "YOUR_API_KEY"

try:
    response = together.Complete.create(
        prompt="Generate a long story" * 10,
        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
        max_tokens=1000,
        request_timeout=30  # Set explicit timeout
    )
except (Timeout, ReadTimeout) as e:
    print(f"⚠️ Inference timeout detected: {e}")
    print("This may indicate Together AI performance degradation")
except Exception as e:
    print(f"Other error: {e}")

Authentication Errors

Signs of auth-related issues:

• Sudden 401 Unauthorized errors with valid API keys
• invalid_api_key errors for previously working keys
• Intermittent authentication failures
• "API key verification failed" messages

Verification script:

import together
import requests

api_key = "YOUR_API_KEY"

# Test authentication directly
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

try:
    response = requests.get(
        "https://api.together.xyz/v1/models",
        headers=headers,
        timeout=10
    )
    
    if response.status_code == 401:
        print("⚠️ Authentication failed - API key issue or service problem")
    elif response.status_code == 200:
        print("✓ Authentication successful")
        print(f"Available models: {len(response.json())}")
    else:
        print(f"Unexpected status: {response.status_code}")
except Exception as e:
    print(f"Connection error: {e}")

Distinguish between:

• Your issue: Invalid or expired API key (check dashboard)
• Together issue: Authentication service degradation (affects all users)

Streaming Response Failures

Problems specific to streaming:

• Stream starts but cuts off mid-generation
• No tokens received despite successful connection
• SSE connection closed errors
• Incomplete responses without proper end markers

Streaming health check:

import together

together.api_key = "YOUR_API_KEY"

try:
    stream = together.Complete.create_streaming(
        prompt="Count from 1 to 10 slowly",
        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
        max_tokens=100
    )
    
    token_count = 0
    last_token_time = None
    
    for token in stream:
        token_count += 1
        current_time = time.time()
        
        if last_token_time and (current_time - last_token_time) > 5:
            print(f"⚠️ Gap detected: {current_time - last_token_time:.2f}s between tokens")
        
        last_token_time = current_time
        print(token['choices'][0]['text'], end='', flush=True)
    
    print(f"\n✓ Stream completed successfully ({token_count} tokens)")
    
except Exception as e:
    print(f"\n✗ Streaming failed: {e}")

Red flags:

• Token delays exceeding 3-5 seconds
• Streams that never start
• Consistent stream interruptions at the same point

The Real Impact When Together AI Goes Down

AI Application Downtime

Every minute of Together AI downtime directly impacts your users:

• AI chatbots: Unable to respond to user queries
• Content generation tools: Writers and creators blocked
• Code assistants: Developers lose AI-powered suggestions
• Customer support automation: Support tickets pile up manually
• Translation services: Real-time translation fails

For an AI SaaS processing 1,000 requests/hour at $0.10/request, a 2-hour outage means $200 in direct inference costs saved but potentially thousands in lost revenue and user churn.

Model-Specific Disruptions

Unlike traditional APIs, AI inference platforms host dozens of models. An outage may affect:

• Specific model families: Llama models down but Mistral working
• Model sizes: 70B models affected but 7B models operational
• Fine-tuned vs base models: Custom models down but base models up
• Multi-modal models: Vision or embedding models degraded independently

Testing multiple models:

import together

together.api_key = "YOUR_API_KEY"

models_to_test = [
    "meta-llama/Llama-3-70b-chat-hf",
    "mistralai/Mixtral-8x7B-Instruct-v0.1",
    "togethercomputer/llama-2-13b-chat",
    "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO"
]

for model in models_to_test:
    try:
        response = together.Complete.create(
            prompt="Hi",
            model=model,
            max_tokens=5
        )
        print(f"✓ {model}: Operational")
    except Exception as e:
        print(f"✗ {model}: {str(e)[:50]}")

This helps you identify if you can failover to alternative models during partial outages.

Failed Content Generation Pipelines

Modern AI workflows often chain multiple inference calls:

1. Generate outline
2. Expand each section
3. Refine and edit
4. Generate images/embeddings
5. Final quality check

When Together AI goes down mid-pipeline:

• Partial content: Half-generated articles or responses
• Wasted context: Lost conversation history and prompts
• Batch job failures: Overnight processing jobs abort
• Webhook failures: Event-driven workflows break

Pipeline resilience example:

import together
import json

together.api_key = "YOUR_API_KEY"

def resilient_pipeline(prompts, checkpoint_file="pipeline_checkpoint.json"):
    """Content generation pipeline with checkpoint/resume capability"""
    
    # Load checkpoint if exists
    try:
        with open(checkpoint_file, 'r') as f:
            checkpoint = json.load(f)
            completed = checkpoint.get('completed', [])
    except FileNotFoundError:
        completed = []
    
    results = []
    
    for idx, prompt in enumerate(prompts):
        if idx in completed:
            print(f"Skipping {idx} (already completed)")
            continue
            
        try:
            response = together.Complete.create(
                prompt=prompt,
                model="mistralai/Mixtral-8x7B-Instruct-v0.1",
                max_tokens=500
            )
            
            results.append({
                'index': idx,
                'prompt': prompt,
                'output': response['output']['choices'][0]['text']
            })
            
            # Checkpoint progress
            completed.append(idx)
            with open(checkpoint_file, 'w') as f:
                json.dump({'completed': completed}, f)
                
        except Exception as e:
            print(f"Failed at step {idx}: {e}")
            print(f"Progress saved. Resume by re-running.")
            return results
    
    return results

Competitive Disadvantage

In the fast-moving AI space, reliability matters:

• Users switch to competitors (OpenAI, Anthropic, Replicate)
• Lost market share during outages
• Damaged reputation in AI developer community
• Review sites and social media complaints

Increased Infrastructure Costs

When Together AI goes down, you may need to:

• Failover to more expensive providers (OpenAI charges 5-10x more for similar models)
• Scale alternative infrastructure (self-hosted models on GPUs)
• Implement complex retry logic (increased engineering costs)
• Customer compensation (SLA credits, refunds)

Cost comparison during 4-hour outage:

Scenario	Together AI	OpenAI Fallback	Self-Hosted
10,000 requests	$50	$500	$80 (GPU costs)
Engineering time	-	4 hours	8 hours
Total cost	$0 (down)	$700	$640

What to Do When Together AI Goes Down: Incident Response Playbook

1. Implement Intelligent Retry Logic with Exponential Backoff

Production-ready retry implementation:

import together
import time
import random
from functools import wraps

def retry_with_exponential_backoff(
    max_retries=5,
    base_delay=1,
    max_delay=60,
    exponential_base=2,
    jitter=True
):
    """Decorator for retrying Together AI calls with exponential backoff"""
    
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            retries = 0
            
            while retries < max_retries:
                try:
                    return func(*args, **kwargs)
                    
                except together.error.RateLimitError as e:
                    # Rate limit - longer backoff
                    delay = min(max_delay, base_delay * (exponential_base ** retries))
                    if jitter:
                        delay *= (0.5 + random.random())
                    
                    print(f"Rate limited. Retrying in {delay:.2f}s... ({retries + 1}/{max_retries})")
                    time.sleep(delay)
                    retries += 1
                    
                except (together.error.APIConnectionError, together.error.ServiceUnavailableError) as e:
                    # Service issue - standard backoff
                    delay = min(max_delay, base_delay * (exponential_base ** retries))
                    if jitter:
                        delay *= (0.5 + random.random())
                    
                    print(f"Connection error. Retrying in {delay:.2f}s... ({retries + 1}/{max_retries})")
                    time.sleep(delay)
                    retries += 1
                    
                except together.error.AuthenticationError as e:
                    # Auth error - don't retry
                    print("Authentication failed. Check your API key.")
                    raise
                    
                except Exception as e:
                    # Unknown error - retry with caution
                    print(f"Unexpected error: {e}")
                    if retries < max_retries - 1:
                        delay = min(max_delay, base_delay * (exponential_base ** retries))
                        time.sleep(delay)
                        retries += 1
                    else:
                        raise
            
            raise Exception(f"Max retries ({max_retries}) exceeded")
        
        return wrapper
    return decorator

# Usage
@retry_with_exponential_backoff(max_retries=5, base_delay=2)
def generate_completion(prompt, model="mistralai/Mixtral-8x7B-Instruct-v0.1"):
    return together.Complete.create(
        prompt=prompt,
        model=model,
        max_tokens=500
    )

# Now your function automatically retries on transient failures
response = generate_completion("Write a short poem about AI")

2. Implement Multi-Provider Failover Strategy

Don't put all your inference eggs in one basket:

import together
import openai
import anthropic

class MultiProviderLLM:
    """Failover between Together AI, OpenAI, and Anthropic"""
    
    def __init__(self, together_key, openai_key, anthropic_key):
        self.together_key = together_key
        self.openai_client = openai.OpenAI(api_key=openai_key)
        self.anthropic_client = anthropic.Anthropic(api_key=anthropic_key)
        together.api_key = together_key
        
    def generate(self, prompt, max_tokens=500, temperature=0.7):
        """Try Together AI first, failover to alternatives"""
        
        # Primary: Together AI (cheapest)
        try:
            response = together.Complete.create(
                prompt=prompt,
                model="mistralai/Mixtral-8x7B-Instruct-v0.1",
                max_tokens=max_tokens,
                temperature=temperature
            )
            return {
                'text': response['output']['choices'][0]['text'],
                'provider': 'together',
                'cost': 0.0006 * max_tokens / 1000  # Approximate
            }
        except Exception as e:
            print(f"Together AI failed: {e}. Failing over to OpenAI...")
        
        # Fallback 1: OpenAI (more expensive but reliable)
        try:
            response = self.openai_client.chat.completions.create(
                model="gpt-3.5-turbo",
                messages=[{"role": "user", "content": prompt}],
                max_tokens=max_tokens,
                temperature=temperature
            )
            return {
                'text': response.choices[0].message.content,
                'provider': 'openai',
                'cost': 0.002 * max_tokens / 1000  # Approximate
            }
        except Exception as e:
            print(f"OpenAI failed: {e}. Failing over to Anthropic...")
        
        # Fallback 2: Anthropic (premium)
        try:
            response = self.anthropic_client.messages.create(
                model="claude-3-haiku-20240307",
                max_tokens=max_tokens,
                messages=[{"role": "user", "content": prompt}]
            )
            return {
                'text': response.content[0].text,
                'provider': 'anthropic',
                'cost': 0.00025 * max_tokens / 1000  # Approximate
            }
        except Exception as e:
            print(f"All providers failed: {e}")
            raise Exception("All LLM providers unavailable")

# Usage
llm = MultiProviderLLM(
    together_key="YOUR_TOGETHER_KEY",
    openai_key="YOUR_OPENAI_KEY",
    anthropic_key="YOUR_ANTHROPIC_KEY"
)

result = llm.generate("Explain quantum computing in simple terms")
print(f"Response from {result['provider']}: {result['text']}")
print(f"Cost: ${result['cost']:.6f}")

3. Queue Requests for Later Processing

Implement a robust request queue:

import redis
import json
import together
from datetime import datetime

class InferenceQueue:
    """Redis-backed queue for handling Together AI outages"""
    
    def __init__(self, redis_url="redis://localhost:6379"):
        self.redis = redis.from_url(redis_url)
        self.queue_key = "together_ai_queue"
        self.processing_key = "together_ai_processing"
        
    def enqueue(self, prompt, model, max_tokens, metadata=None):
        """Add inference request to queue"""
        request = {
            'prompt': prompt,
            'model': model,
            'max_tokens': max_tokens,
            'metadata': metadata or {},
            'timestamp': datetime.utcnow().isoformat(),
            'retries': 0
        }
        
        self.redis.lpush(self.queue_key, json.dumps(request))
        return request
        
    def process_queue(self, batch_size=10):
        """Process queued requests when Together AI is back online"""
        processed = 0
        failed = []
        
        for _ in range(batch_size):
            # Get request from queue
            request_json = self.redis.rpoplpush(self.queue_key, self.processing_key)
            if not request_json:
                break
                
            request = json.loads(request_json)
            
            try:
                # Attempt inference
                response = together.Complete.create(
                    prompt=request['prompt'],
                    model=request['model'],
                    max_tokens=request['max_tokens']
                )
                
                # Success - remove from processing
                self.redis.lrem(self.processing_key, 1, request_json)
                processed += 1
                
                # Store result or trigger callback
                if 'callback_url' in request['metadata']:
                    # Post result to callback URL
                    pass
                    
            except Exception as e:
                # Failed - increment retry counter
                request['retries'] += 1
                
                if request['retries'] < 3:
                    # Re-queue for retry
                    self.redis.lrem(self.processing_key, 1, request_json)
                    self.redis.lpush(self.queue_key, json.dumps(request))
                else:
                    # Max retries - move to failed queue
                    self.redis.lrem(self.processing_key, 1, request_json)
                    failed.append(request)
                    
        return {
            'processed': processed,
            'failed': len(failed),
            'queue_length': self.redis.llen(self.queue_key)
        }

# Usage during outage
queue = InferenceQueue()

# Enqueue request instead of blocking
queue.enqueue(
    prompt="Generate marketing copy for AI product",
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
    max_tokens=500,
    metadata={'user_id': 'user_123', 'request_id': 'req_456'}
)

# Later, when Together AI is back online (cron job or manual trigger)
results = queue.process_queue(batch_size=100)
print(f"Processed {results['processed']} requests, {results['queue_length']} remaining")

4. Implement Circuit Breaker Pattern

Prevent cascading failures:

import time
from enum import Enum

class CircuitState(Enum):
    CLOSED = "closed"      # Normal operation
    OPEN = "open"          # Failing - block requests
    HALF_OPEN = "half_open"  # Testing recovery

class CircuitBreaker:
    """Circuit breaker for Together AI calls"""
    
    def __init__(
        self,
        failure_threshold=5,
        timeout=60,
        half_open_attempts=3
    ):
        self.failure_threshold = failure_threshold
        self.timeout = timeout
        self.half_open_attempts = half_open_attempts
        
        self.failure_count = 0
        self.last_failure_time = None
        self.state = CircuitState.CLOSED
        self.half_open_count = 0
        
    def call(self, func, *args, **kwargs):
        """Execute function with circuit breaker protection"""
        
        if self.state == CircuitState.OPEN:
            # Check if timeout has passed
            if time.time() - self.last_failure_time > self.timeout:
                print("Circuit breaker: entering HALF_OPEN state")
                self.state = CircuitState.HALF_OPEN
                self.half_open_count = 0
            else:
                raise Exception("Circuit breaker OPEN - Together AI likely down")
        
        try:
            result = func(*args, **kwargs)
            
            # Success - reset if in HALF_OPEN
            if self.state == CircuitState.HALF_OPEN:
                self.half_open_count += 1
                if self.half_open_count >= self.half_open_attempts:
                    print("Circuit breaker: CLOSED (service recovered)")
                    self.state = CircuitState.CLOSED
                    self.failure_count = 0
            
            return result
            
        except Exception as e:
            self.failure_count += 1
            self.last_failure_time = time.time()
            
            if self.state == CircuitState.HALF_OPEN:
                print("Circuit breaker: re-opening (test failed)")
                self.state = CircuitState.OPEN
            elif self.failure_count >= self.failure_threshold:
                print(f"Circuit breaker: OPEN (threshold reached: {self.failure_count} failures)")
                self.state = CircuitState.OPEN
            
            raise

# Usage
breaker = CircuitBreaker(failure_threshold=3, timeout=30)

def make_together_call():
    return together.Complete.create(
        prompt="Test",
        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
        max_tokens=10
    )

try:
    result = breaker.call(make_together_call)
except Exception as e:
    print(f"Call failed or circuit open: {e}")
    # Failover to alternative provider

5. Monitor and Alert Proactively

Comprehensive monitoring setup:

import together
import time
import requests
from datetime import datetime

class TogetherAIMonitor:
    """Monitor Together AI health and alert on issues"""
    
    def __init__(self, api_key, alert_webhook=None):
        self.api_key = api_key
        self.alert_webhook = alert_webhook
        together.api_key = api_key
        
    def health_check(self):
        """Comprehensive health check"""
        results = {
            'timestamp': datetime.utcnow().isoformat(),
            'checks': {}
        }
        
        # 1. API connectivity
        start = time.time()
        try:
            response = requests.get(
                "https://api.together.xyz/v1/models",
                headers={"Authorization": f"Bearer {self.api_key}"},
                timeout=10
            )
            latency = time.time() - start
            results['checks']['connectivity'] = {
                'status': 'ok' if response.status_code == 200 else 'degraded',
                'latency_ms': int(latency * 1000),
                'status_code': response.status_code
            }
        except Exception as e:
            results['checks']['connectivity'] = {
                'status': 'down',
                'error': str(e)
            }
        
        # 2. Inference speed test
        start = time.time()
        try:
            response = together.Complete.create(
                prompt="Quick test",
                model="mistralai/Mixtral-8x7B-Instruct-v0.1",
                max_tokens=10
            )
            inference_time = time.time() - start
            results['checks']['inference'] = {
                'status': 'ok' if inference_time < 5 else 'slow',
                'time_seconds': round(inference_time, 2)
            }
        except Exception as e:
            results['checks']['inference'] = {
                'status': 'down',
                'error': str(e)
            }
        
        # 3. Streaming test
        try:
            stream = together.Complete.create_streaming(
                prompt="Count to 3",
                model="mistralai/Mixtral-8x7B-Instruct-v0.1",
                max_tokens=20
            )
            token_count = sum(1 for _ in stream)
            results['checks']['streaming'] = {
                'status': 'ok' if token_count > 0 else 'degraded',
                'tokens_received': token_count
            }
        except Exception as e:
            results['checks']['streaming'] = {
                'status': 'down',
                'error': str(e)
            }
        
        # Overall status
        all_ok = all(
            check.get('status') == 'ok' 
            for check in results['checks'].values()
        )
        results['overall_status'] = 'healthy' if all_ok else 'degraded'
        
        # Alert if degraded
        if not all_ok and self.alert_webhook:
            self.send_alert(results)
        
        return results
    
    def send_alert(self, results):
        """Send alert to webhook (Slack, Discord, etc.)"""
        message = f"⚠️ Together AI Health Check Failed\n\n"
        for check_name, check_data in results['checks'].items():
            status_emoji = "✅" if check_data['status'] == 'ok' else "❌"
            message += f"{status_emoji} {check_name}: {check_data['status']}\n"
        
        try:
            requests.post(
                self.alert_webhook,
                json={'text': message},
                timeout=5
            )
        except:
            pass

# Usage: Run every 60 seconds via cron or systemd timer
monitor = TogetherAIMonitor(
    api_key="YOUR_API_KEY",
    alert_webhook="YOUR_SLACK_WEBHOOK"
)

while True:
    results = monitor.health_check()
    print(f"Health check: {results['overall_status']}")
    time.sleep(60)

6. Communicate with Users Transparently

Status page component for your app:

from flask import Flask, jsonify
import together

app = Flask(__name__)

@app.route('/api/status')
def service_status():
    """Public status endpoint for your users"""
    
    # Check Together AI health
    together_status = "operational"
    try:
        together.Complete.create(
            prompt="ping",
            model="mistralai/Mixtral-8x7B-Instruct-v0.1",
            max_tokens=5
        )
    except together.error.RateLimitError:
        together_status = "rate_limited"
    except together.error.ServiceUnavailableError:
        together_status = "degraded"
    except Exception:
        together_status = "down"
    
    return jsonify({
        'status': together_status,
        'message': {
            'operational': 'All systems operational',
            'rate_limited': 'Experiencing high demand - responses may be slower',
            'degraded': 'Service degraded - we are investigating',
            'down': 'Service temporarily unavailable - working on restoration'
        }[together_status],
        'alternative_action': 'Please try again in a few minutes' if together_status != 'operational' else None
    })

Frequently Asked Questions

How often does Together AI experience outages?

Together AI maintains strong uptime, typically exceeding 99.9% availability. Major platform-wide outages affecting all models are rare (1-2 times per year), but you may occasionally experience model-specific issues, regional latency spikes, or capacity constraints during peak demand. Most issues are resolved within 30-60 minutes.

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

Together AI's official status page (status.together.ai) is manually updated by their team during incidents, which can lag behind actual issues by several minutes. API Status Check performs automated health checks every 60 seconds against live inference endpoints, often detecting issues before they're officially reported. Use both for comprehensive monitoring.

Can I get a refund or credit for Together AI downtime?

Together AI's Enterprise plans include SLA guarantees with credits for downtime exceeding defined thresholds. Standard and Pro tier customers typically do not receive automatic credits, but Together AI has historically provided goodwill credits for significant outages. Review your specific plan's SLA or contact support@together.xyz for clarification.

Should I use Together AI as my only LLM provider?

For production applications with strict uptime requirements, we recommend a multi-provider strategy. Use Together AI as your primary provider (cost-effective, good performance) but implement failover to alternatives like OpenAI, Anthropic, or Hugging Face Inference API. This ensures your application stays online even during provider-specific outages.

How do I prevent duplicate generations during retry logic?

Implement idempotency in your application layer. Store a unique request ID (UUID) with each inference request, and check your database before processing retries to ensure you haven't already handled the request. Together AI's API doesn't natively support idempotency keys like Stripe, so you must implement this in your application.

What's the best model to use for reliability?

Popular models like Mixtral-8x7B-Instruct and Llama-3-70b-chat typically have the highest availability since Together AI prioritizes capacity for high-demand models. Smaller models (7B-13B) often have faster cold start times and higher capacity. Fine-tuned custom models may have lower availability during platform stress. Monitor multiple models and implement model fallback logic.

How long does Together AI typically take to resolve outages?

Based on historical incident reports:

• Minor issues (single model or region): 15-30 minutes
• Moderate outages (multiple models): 30-90 minutes
• Major platform outages (all services): 1-4 hours

Together AI's engineering team is responsive, and they typically provide status updates every 15-30 minutes during active incidents.

Can I self-host models as a backup to Together AI?

Yes! Together AI uses open-source models (Llama, Mistral, etc.) that you can self-host using:

• vLLM - Fast inference engine
• Text Generation Inference (HuggingFace)
• Ollama - Local development
• Replicate - Serverless alternative

Self-hosting requires GPU infrastructure (expensive) but provides complete control. For most businesses, multi-provider cloud strategy (Together AI + OpenAI + Anthropic) is more cost-effective than self-hosting.

What monitoring should I implement for Together AI?

Implement multi-layer monitoring:

1. Infrastructure layer: Monitor API response times, error rates, and availability
2. Application layer: Track inference latency, token generation speed, and completion rates
3. Business layer: Monitor user-facing metrics (chatbot response times, content generation success rates)
4. External monitoring: Use API Status Check for independent verification

Set up alerts for:

• Response time > 10 seconds
• Error rate > 5%
• Streaming interruptions > 10% of requests
• Complete API unavailability

Does Together AI have regional redundancy?

Together AI operates global infrastructure with multiple availability zones. However, their routing is generally transparent to users—you don't typically choose specific regions. During regional issues, requests may be automatically routed to alternative zones, but this can increase latency. For latency-sensitive applications serving specific geographies, consider regional providers or CDN-based inference solutions.

Stay Ahead of Together AI Outages

Don't let AI inference issues catch you off guard. Subscribe to real-time Together AI alerts and get notified instantly when issues are detected—before your users notice.

API Status Check monitors Together AI 24/7 with:

• 60-second health checks across multiple models
• First-token latency tracking
• Streaming stability monitoring
• Instant alerts via email, Slack, Discord, or webhook
• Historical uptime data and incident reports
• Multi-provider monitoring for your entire AI stack

Start monitoring Together AI now →

Related AI Platform Guides:

• Is OpenAI Down? Real-Time Status
• Is Anthropic Down? Claude API Status
• Is Hugging Face Down? Inference API Monitoring
• Is Replicate Down? AI Model Status

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