elevenlabs-rate-limits

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".

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elevenlabs-pack

Claude Code skill pack for ElevenLabs (18 skills)

saas packs v1.6.0
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Installation

This skill is included in the elevenlabs-pack plugin:

/plugin install elevenlabs-pack@claude-code-plugins-plus

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Instructions

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
toomanyconcurrent_requests {"detail":{"status":"toomanyconcurrent_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 toomanyconcurrent_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
elevenmultilingualv2 1.0 10,000 credits
elevenflashv2_5 0.5 5,000 credits
eleventurbov2_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 + toomanyconcurrent_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

Next Steps

For security configuration, see elevenlabs-security-basics.

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