Rate Limits

Understanding API rate limits, concurrency, and how to work within them

Overview

Rate limits protect the API from abuse and ensure fair usage for all users. Moknah enforces limits on request frequency, credit consumption, and concurrent connections across both our HTTP and WebSocket endpoints.

Default Limits

WebSocket Limits & Close Codes

Because WebSockets maintain a persistent connection, they do not use HTTP headers for rate limiting. Instead, limits are enforced via connection drops and JSON control messages:

Note: If your WebSocket disconnects unexpectedly, wait at least 15 seconds before reconnecting to ensure the server-side lock has fully cleared.

HTTP Rate Limit Headers

For standard REST endpoints, every API response includes headers to help you track your status:

When HTTP Rate Limited

If you exceed the HTTP rate limit, you'll receive a 429 Too Many Requests response with these additional headers:

Credit Calculation

Credits are calculated based on text length and processing options. For WebSockets, credits are deducted dynamically per-chunk as they are streamed.

Example: A 500-character text with AI-Enhanced normalization:

500 characters × 2 (AI-Enhanced) = 1,000 credits

Best Practices

1. Use WebSockets for High-Volume Sequential Text

If you are generating conversational AI responses or parsing long documents, do not make 50 rapid HTTP POST requests. Open a single WebSocket connection and stream the chunks over the persistent connection. This bypasses the 60 RPM HTTP limit entirely (though credit limits still apply).

2. Implement Request Queuing (HTTP)

Since Moknah doesn't support concurrent requests, queue your HTTP requests and process them sequentially:

import queue
import threading
import time

class TTSQueue:
    def __init__(self, api_key):
        self.api_key = api_key
        self.queue = queue.Queue()
        self.worker = threading.Thread(target=self._process_queue, daemon=True)
        self.worker.start()

    def _process_queue(self):
        while True:
            text, voice_id, callback = self.queue.get()
            try:
                result = self._generate(text, voice_id)
                callback(result, None)
            except Exception as e:
                callback(None, e)
            finally:
                self.queue.task_done()

    def _generate(self, text, voice_id):
        # Your API call here
        pass

    def add(self, text, voice_id, callback):
        self.queue.put((text, voice_id, callback))

# Usage
tts = TTSQueue("your_api_key")
tts.add("Hello world", "voice_123", lambda r, e: print(r or e))

class TTSQueue {
  constructor(apiKey) {
    this.apiKey = apiKey;
    this.queue = [];
    this.processing = false;
  }

  async add(text, voiceId) {
    return new Promise((resolve, reject) => {
      this.queue.push({ text, voiceId, resolve, reject });
      this.processNext();
    });
  }

  async processNext() {
    if (this.processing || this.queue.length === 0) return;

    this.processing = true;
    const { text, voiceId, resolve, reject } = this.queue.shift();

    try {
      const result = await this.generate(text, voiceId);
      resolve(result);
    } catch (error) {
      reject(error);
    } finally {
      this.processing = false;
      this.processNext(); // Process next in queue
    }
  }

  async generate(text, voiceId) {
    // Your API call here
  }
}

// Usage
const tts = new TTSQueue("your_api_key");
const audio = await tts.add("Hello world", "voice_123");

2. Implement Request Queuing (HTTP)

Since Moknah doesn't support concurrent requests, queue your HTTP requests and process them sequentially:

import time
import random

def request_with_backoff(func, max_retries=5):
    retries = 0

    while retries < max_retries:
        try:
            response = func()

            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 60))
                # Add jitter to prevent thundering herd
                wait_time = retry_after + random.uniform(0, 1)
                print(f"Rate limited. Waiting {wait_time:.1f}s...")
                time.sleep(wait_time)
                retries += 1
                continue

            return response

        except Exception as e:
            # Exponential backoff for other errors
            wait_time = (2 ** retries) + random.uniform(0, 1)
            print(f"Error: {e}. Retrying in {wait_time:.1f}s...")
            time.sleep(wait_time)
            retries += 1

    raise Exception("Max retries exceeded")

async function requestWithBackoff(func, maxRetries = 5) {
  let retries = 0;

  while (retries < maxRetries) {
    try {
      const response = await func();

      if (response.status === 429) {
        const retryAfter = parseInt(response.headers.get("Retry-After") || 60);
        // Add jitter to prevent thundering herd
        const waitTime = retryAfter + Math.random();
        console.log(`Rate limited. Waiting ${waitTime.toFixed(1)}s...`);
        await new Promise(r => setTimeout(r, waitTime * 1000));
        retries++;
        continue;
      }

      return response;

    } catch (error) {
      // Exponential backoff for other errors
      const waitTime = Math.pow(2, retries) + Math.random();
      console.log(`Error: ${error.message}. Retrying in ${waitTime.toFixed(1)}s...`);
      await new Promise(r => setTimeout(r, waitTime * 1000));
      retries++;
    }
  }

  throw new Error("Max retries exceeded");
}

3. Implement Exponential Backoff

When rate limited (HTTP 429) or connection limited (WebSocket 4290), use exponential backoff to retry securely without spamming the server:

Summary