Together rate limits

Together AI Rate Limits

Overview

Together AI's OpenAI-compatible inference API enforces per-key rate limits that vary by model tier and operation type. Chat completions and embeddings share a global request quota, while fine-tuning jobs and batch inference have separate concurrency caps. High-throughput workloads like embedding entire document corpora or running evaluations across 100+ prompts require client-side token bucket limiting. Together's batch inference endpoint offers 50% cost savings but has its own queue depth limits that differ from real-time inference.

Rate Limit Reference

Endpoint	Limit	Window	Scope
Chat completions	600 req	1 minute	Per API key
Embeddings	300 req	1 minute	Per API key
Image generation (FLUX)	60 req	1 minute	Per API key
Fine-tune jobs (concurrent)	3 jobs	Rolling	Per API key
Batch inference	100 req/batch, 10 batches	Rolling	Per API key

Rate Limiter Implementation

class TogetherRateLimiter {
  private tokens: number;
  private lastRefill: number;
  private readonly max: number;
  private readonly refillRate: number;
  private queue: Array<{ resolve: () => void }> = [];

  constructor(maxPerMinute: number) {
    this.max = maxPerMinute;
    this.tokens = maxPerMinute;
    this.lastRefill = Date.now();
    this.refillRate = maxPerMinute / 60_000;
  }

  async acquire(): Promise<void> {
    this.refill();
    if (this.tokens >= 1) { this.tokens -= 1; return; }
    return new Promise(resolve => this.queue.push({ resolve }));
  }

  private refill() {
    const now = Date.now();
    this.tokens = Math.min(this.max, this.tokens + (now - this.lastRefill) * this.refillRate);
    this.lastRefill = now;
    while (this.tokens >= 1 && this.queue.length) {
      this.tokens -= 1;
      this.queue.shift()!.resolve();
    }
  }
}

const chatLimiter = new TogetherRateLimiter(500);  // buffer under 600
const embedLimiter = new TogetherRateLimiter(250);

Retry Strategy

async function togetherRetry<T>(
  limiter: TogetherRateLimiter, fn: () => Promise<Response>, maxRetries = 4
): Promise<T> {
  for (let attempt = 0; attempt <= maxRetries; attempt++) {
    await limiter.acquire();
    const res = await fn();
    if (res.ok) return res.json();
    if (res.status === 429) {
      const retryAfter = parseInt(res.headers.get("Retry-After") || "5", 10);
      const jitter = Math.random() * 2000;
      await new Promise(r => setTimeout(r, retryAfter * 1000 + jitter));
      continue;
    }
    if (res.status >= 500 && attempt < maxRetries) {
      await new Promise(r => setTimeout(r, Math.pow(2, attempt) * 1000));
      continue;
    }
    throw new Error(`Together API ${res.status}: ${await res.text()}`);
  }
  throw new Error("Max retries exceeded");
}

Batch Processing

async function batchEmbedDocuments(texts: string[], model: string, batchSize = 20) {
  const results: any[] = [];
  for (let i = 0; i < texts.length; i += batchSize) {
    const batch = texts.slice(i, i + batchSize);
    const result = await togetherRetry(embedLimiter, () =>
      fetch("https://api.together.xyz/v1/embeddings", {
        method: "POST", headers,
        body: JSON.stringify({ model, input: batch }),
      })
    );
    results.push(result);
    if (i + batchSize < texts.length) await new Promise(r => setTimeout(r, 3000));
  }
  return results;
}

Error Handling

Issue	Cause	Fix
429 on chat completions	Exceeded 600 req/min key limit	Use token bucket, avoid burst patterns
429 on embeddings	Embedding limit is half of chat	Batch inputs (up to 20 texts per request)
Model not found	Wrong model ID string	Verify with GET /v1/models endpoint
503 model overloaded	Popular model at peak demand	Retry with backoff, or use fallback model
Fine-tune 409	3 concurrent job limit reached	Wait for running job to complete first

Resources

• Together AI Documentation
• API Reference
• Model List

Next Steps

See together-performance-tuning.