Together AI API Monitoring Guide 2026

Covers: Serverless inference, dedicated endpoints, fine-tuning jobs, and the platform dashboard

Covers: Your API key usage, rate limit consumption, and billing

Malformed request — invalid model name, empty messages array, or unsupported parameter

Check the error message body for the specific field. Common causes: a deprecated model string or a max_tokens value that exceeds the model's context window.

Missing or invalid API key

Verify your TOGETHER_API_KEY is set and current. Generate a fresh key from the Together AI dashboard.

API key lacks permission for this model or endpoint

Some gated open-weight models require accepting a license agreement in the dashboard before your key can call them. Check the model page for license requirements.

Model or endpoint not found

Verify the model string matches Together AI's naming convention (e.g., "meta-llama/Llama-4-Maverick-17B", "Qwen/Qwen3-235B"). Models are occasionally deprecated as newer versions release.

Request was well-formed but semantically invalid

Usually caused by exceeding the model's context window or an invalid JSON schema for structured output/function calling. Check input length against the specific model's context limit — this varies widely across Together's model catalog.

Rate limit exceeded — requests/min or tokens/min for that model on the serverless tier

Implement exponential backoff (1s → 2s → 4s...). If this happens under steady production load, evaluate a dedicated endpoint to remove shared rate-limit contention.

Server-side error on Together AI's infrastructure — not your fault

Retry with backoff. Persistent 500s across multiple requests indicate a genuine incident — check status.together.ai.

Together AI temporarily overloaded or the specific model is scaling capacity

Retry with exponential backoff or fail over to another open-model provider. Set up alerts so you know immediately when this happens in production.

Use a third-party service to ping the Together AI API every 60 seconds from outside your infrastructure. This catches incidents before your application logs start filling with errors.

• →Monitor api.together.xyz/v1/models (lightweight list endpoint, no tokens consumed)
• →Alert on: non-200 responses, response time > 3s, SSL issues
• →A synthetic monitor with email/Slack/webhook alerts catches this before users report it

Track these metrics in your observability stack (Better Stack Logs, Datadog, Grafana):

• • 429 rate per model — track separately since different models have different serverless limits
• • Time to first token (TTFT) — spikes indicate infrastructure stress on that specific model
• • Fallback trigger rate — how often your app falls back to a second provider or model
• • Monthly spend vs. plan — especially important if you mix serverless and dedicated endpoints
• • Dedicated endpoint utilization — if using dedicated GPUs, track utilization to right-size reserved capacity

Since serverless rate limits are set per model rather than a single account-wide pool, track consumption separately for each model you call:

// Track 429 responses per model separately
const modelCalls: Record<string, { total: number; rateLimited: number }> = {};

function recordTogetherCall(model: string, status: number) {
  const stats = modelCalls[model] ??= { total: 0, rateLimited: 0 };
  stats.total++;
  if (status === 429) stats.rateLimited++;

  // Alert when a specific model's 429 rate exceeds 5%
  if (stats.total > 100 && stats.rateLimited / stats.total > 0.05) {
    metrics.increment(`together.${model}.rate_limit.warning`);
  }
}

How do I check if the Together AI API is down?

Check the official status page at status.together.ai for real-time incident updates on serverless inference, dedicated endpoints, and fine-tuning jobs. API Status Check also maintains a dedicated Together AI status guide with troubleshooting steps and monitoring recommendations.

What are the Together AI API rate limits?

Serverless endpoints enforce requests-per-minute and tokens-per-minute limits that vary by model size and your account's billing tier — new pay-as-you-go accounts start on lower limits that scale up with usage history. Dedicated endpoints are provisioned per-GPU and are not subject to the shared serverless rate limits, but are capped by the throughput of the hardware you provision.

What does a Together AI API 429 error mean?

A 429 error means you have exceeded your requests-per-minute or tokens-per-minute limit for that model on the serverless endpoint. Implement exponential backoff starting around 1 second. If 429s are frequent under normal load, consider a dedicated endpoint, which removes shared rate-limit contention entirely.

Is the Together AI API compatible with the OpenAI SDK?

Yes. Together AI's inference API is OpenAI chat-completions compatible, so you can point the official openai SDK at Together's base URL with just a base_url and model name change for most chat and completion use cases.

Should I use serverless or dedicated endpoints for production?

Serverless is the right default for variable or moderate traffic — it's pay-per-token with no infrastructure to manage, but shares capacity with other customers and is subject to rate limits. Dedicated endpoints provision reserved GPUs for your workload alone, removing rate-limit contention and giving predictable latency, at the cost of paying for reserved capacity even during idle periods.