Skills Development Implementing Rate Limit Handling for AI APIs

Implementing Rate Limit Handling for AI APIs

v20260423
together-rate-limits
A comprehensive guide and implementation demonstrating how to handle API rate limits when interacting with AI services like Together AI. It details the token bucket algorithm for client-side throttling and provides robust retry mechanisms (exponential backoff) to manage 429 (Rate Limit Exceeded) and 5xx server errors, ensuring stable, high-throughput batch processing.
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Overview

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

Next Steps

See together-performance-tuning.

Info
Category Development
Name together-rate-limits
Version v20260423
Size 4.59KB
Updated At 2026-04-28
Language