Is Together AI Down? How to Check Together AI Status in 2026

How to Check If Together AI Is Down

1. Check the Official Together AI Status Page

Together AI maintains a status page at status.together.ai. It tracks API availability, model serving health, and any ongoing incidents. This is the authoritative source — check here first before assuming it's your code.

The status page typically shows per-component status: inference API, fine-tuning API, billing dashboard, and the developer console. If you see “Degraded Performance” or “Partial Outage,” specific model endpoints may be affected while others remain operational.

2. Test the Together AI API Directly

A direct API test immediately confirms whether the issue is Together AI or your setup:

curl https://api.together.xyz/v1/chat/completions \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo",
    "messages": [{"role": "user", "content": "ping"}],
    "max_tokens": 5
  }'

A 200 response confirms the API is working. A 503 or connection timeout indicates a service outage. A 429 means you've hit rate limits — not an outage.

3. Try a Different Model

Together AI hosts 100+ models across different GPU clusters. If one model is down (e.g., a large 70B model), try a smaller variant like meta-llama/Llama-3.2-3B-Instruct-Turbo or switch to a different model family entirely (Mistral, Qwen, DeepSeek). Partial outages often affect specific model deployments rather than the entire platform.

4. Search X (Twitter) for Real-Time Reports

Search “Together AI down” or “together.ai outage” filtered by Latest. Developer communities report AI API failures within minutes on X — often before the official status page is updated.

5. Use API Status Check for Automated Monitoring

For production systems, API Status Check monitors Together AI's inference endpoints continuously and sends instant alerts via Slack, email, or PagerDuty when inference fails.

Why Does Together AI Go Down?

Together AI's architecture as a multi-model inference platform creates unique failure patterns:

• GPU Cluster Capacity Limits: Together AI serves 100+ models across heterogeneous GPU fleets (A100s, H100s). During traffic spikes, specific GPU pools can hit capacity before autoscaling kicks in, causing queuing delays or timeouts for specific model families.
• Model Deployment Rollouts: When Together AI deploys a new model version or fine-tuned variant, the rollout process can temporarily interrupt inference for that model while old instances drain and new instances warm up.
• API Gateway Overload: The routing layer that maps requests to the correct model serving pods can become a bottleneck during sudden traffic spikes, causing latency spikes or 502 errors even when compute capacity is available.
• Fine-Tuning Job Interference: Together AI's fine-tuning service shares infrastructure with inference. Large fine-tuning jobs can consume GPU memory or bandwidth that temporarily degrades inference performance.
• Network Peering Issues: Together AI uses cloud infrastructure across multiple providers. BGP route changes or peering issues can cause latency spikes for specific geographic regions.

Together AI Troubleshooting Checklist

Together AI Alternatives for Failover

If Together AI is down and you need to maintain service continuity, these providers offer similar open-source model access:

Building a Resilient Together AI Integration

Together AI's OpenAI-compatible API makes it straightforward to implement multi-provider fallback:

import OpenAI from 'openai';

const providers = [
  {
    client: new OpenAI({
      baseURL: 'https://api.together.xyz/v1',
      apiKey: process.env.TOGETHER_API_KEY,
    }),
    model: 'meta-llama/Llama-3.3-70B-Instruct-Turbo',
  },
  {
    client: new OpenAI({
      baseURL: 'https://api.groq.com/openai/v1',
      apiKey: process.env.GROQ_API_KEY,
    }),
    model: 'llama-3.3-70b-versatile',
  },
];

async function inferWithFallback(prompt: string) {
  for (const provider of providers) {
    try {
      const response = await provider.client.chat.completions.create({
        model: provider.model,
        messages: [{ role: 'user', content: prompt }],
      });
      return response;
    } catch (e) {
      console.warn('Provider failed, trying next...', e);
    }
  }
  throw new Error('All providers down');
}

Together AI Uptime & Outage History

Together AI has generally maintained strong uptime for its core inference API, with most incidents involving specific model deployments rather than full platform outages. The most common issue is degraded performance during high-demand events (major model launches, viral demos of new Llama or Mistral releases) when GPU clusters hit capacity limits. Full outages are rare and typically resolved within 30–90 minutes.

For real-time uptime history, check API Status Check's Together AI monitoring page — it tracks rolling 30-day availability and response time trends.

Frequently Asked Questions

Don't Let AI Outages Catch You Off Guard

Together AI is a critical piece of many production AI stacks. When it goes down, you need to know immediately — not after user complaints start rolling in.