together-rate-limits
Together AI rate limits for inference, fine-tuning, and model deployment. Use when working with Together AI's OpenAI-compatible API. Trigger: "together rate limits".
Best use case
together-rate-limits is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Together AI rate limits for inference, fine-tuning, and model deployment. Use when working with Together AI's OpenAI-compatible API. Trigger: "together rate limits".
Teams using together-rate-limits should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/together-rate-limits/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How together-rate-limits Compares
| Feature / Agent | together-rate-limits | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Together AI rate limits for inference, fine-tuning, and model deployment. Use when working with Together AI's OpenAI-compatible API. Trigger: "together rate limits".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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
```typescript
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
```typescript
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
```typescript
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](https://docs.together.ai/)
- [API Reference](https://docs.together.ai/reference/chat-completions-1)
- [Model List](https://docs.together.ai/docs/inference-models)
## Next Steps
See `together-performance-tuning`.Related Skills
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