---

name: elevenlabs-rate-limits description: | Implement ElevenLabs rate limiting, concurrency queuing, and backoff patterns. Use when handling 429 errors, implementing retry logic, or managing concurrent TTS request throughput for an ElevenLabs integration. Trigger with "elevenlabs rate limit", "elevenlabs throttling", "elevenlabs 429", "elevenlabs retry", "elevenlabs backoff", "elevenlabs concurrent requests". allowed-tools: Read, Write, Edit version: 1.6.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• voice
• ai
• elevenlabs
• rate-limits
• reliability compatibility: Designed for Claude Code

---

ElevenLabs Rate Limits

Overview

Handle ElevenLabs rate limits with plan-aware concurrency queuing, exponential backoff, and quota monitoring. ElevenLabs uses two rate limit mechanisms: concurrent request limits (per plan) and system-level throttling. The key insight is that a 429 means two different things depending on its detail.status — and each demands the opposite response.

Prerequisites

• ElevenLabs SDK installed (@elevenlabs/elevenlabs-js)
• Understanding of your subscription plan's limits
• p-queue package (recommended): npm install p-queue

Instructions

Step 1: Understand the Two 429 Error Types

ElevenLabs returns HTTP 429 for two different reasons. Read the detail.status field to tell them apart — the correct strategy is opposite for each.

429 Variant	Response Body	Cause	Strategy
too_many_concurrent_requests	{"detail":{"status":"too_many_concurrent_requests"}}	Exceeded plan concurrency	Queue requests, don't backoff
system_busy	{"detail":{"status":"system_busy"}}	Server overload	Exponential backoff

Step 2: Know Your Plan Concurrency Limits

Concurrency is capped per plan. Size your queue to this number — never higher.

Plan	Max Concurrent Requests	Characters/Month
Free	2	10,000
Starter	3	30,000
Creator	5	100,000
Pro	10	500,000
Scale	15	2,000,000
Business	15	Custom

Step 3: Assemble the Four Building Blocks

Write four small modules and compose them. The full, copy-ready source for each is in references/implementation.md — the skeleton below shows how they fit together.

1. Request queue (rate-limiter.ts) — a p-queue sized to your plan's concurrency limit. This is the response to too_many_concurrent_requests: queue, do not back off.
2. Backoff wrapper (backoff.ts) — exponential backoff with jitter for system_busy and 5xx; immediate short retry for concurrency; hard-fail on 401/400/404.
3. Quota monitor (quota-monitor.ts) — polls user.subscription character usage, warns at a threshold, and blocks a request that would overrun remaining quota.
4. Resilient client (resilient-client.ts) — composes all three so one generateSpeech() call guards quota, queues, and backs off automatically:

// src/elevenlabs/resilient-client.ts (skeleton — full source in references/implementation.md)
export function createResilientClient(plan = "pro") {
  const client = new ElevenLabsClient({ maxRetries: 0 }); // we handle retries
  const queue = createRequestQueue(plan);                 // Step 3.1
  const quota = new QuotaMonitor(client);                 // Step 3.3

  return {
    async generateSpeech(voiceId, text, modelId = "eleven_multilingual_v2") {
      await quota.guardRequest(text.length);              // Step 3.3
      return queue.add(() =>                              // Step 3.1
        withBackoff(() =>                                 // Step 3.2
          client.textToSpeech.convert(voiceId, { text, model_id: modelId })
        )
      );
    },
  };
}

Step 4: Mind Model Cost When Managing Quota

Quota is spent in credits-per-character, which varies by model. Use Flash/Turbo models during development to conserve quota.

Model	Credits per Character	10,000 Chars Cost
eleven_v3	1.0	10,000 credits
eleven_multilingual_v2	1.0	10,000 credits
eleven_flash_v2_5	0.5	5,000 credits
eleven_turbo_v2_5	0.5	5,000 credits

Output

Applying this skill produces four TypeScript modules under src/elevenlabs/ and a rate-limited request path:

• rate-limiter.ts — exports createRequestQueue(plan) returning a plan-sized PQueue.
• backoff.ts — exports withBackoff(operation, config) returning the operation's result or throwing after maxRetries.
• quota-monitor.ts — exports a QuotaMonitor class with check() → { used, limit, remaining, pctUsed, warning } and guardRequest(textLength).
• resilient-client.ts — exports createResilientClient(plan) whose generateSpeech() returns TTS audio, plus getQueueStats() and checkQuota().

At runtime: concurrent requests stay at or below the plan cap, system_busy responses are retried with backoff, and requests that would overrun quota fail fast with a clear error instead of a wasted API call.

Error Handling

Scenario	Detection	Response
Concurrent limit hit	429 + too_many_concurrent_requests	Queue; retry after ~50ms per queued request
System busy	429 + system_busy	Exponential backoff (1s, 2s, 4s, 8s...)
Quota exhausted	401 + quota_exceeded	Stop requests; alert; wait for reset
Server error	500-599	Exponential backoff; max 5 retries

Examples

Concise starting point — batch generation with just the queue:

import { createRequestQueue } from "./elevenlabs/rate-limiter";

const queue = createRequestQueue("pro"); // 10 concurrent
const clips = await Promise.all(
  texts.map(text =>
    queue.add(() => client.textToSpeech.convert(voiceId, { text, model_id: "eleven_flash_v2_5" }))
  )
); // 20 requests, at most 10 in flight

For the full resilient-client example, per-429-variant branching at the call site, and the batch pattern in context, see references/examples.md.

Resources

• ElevenLabs Rate Limits Help
• ElevenLabs Pricing
• p-queue Documentation
• references/implementation.md — full source for all four building blocks
• references/examples.md — worked end-to-end usage examples

Next Steps

For security configuration, see elevenlabs-security-basics.