HTTP 429 "Too Many Requests" means the API is

HTTP 429 "Too Many Requests" means the API is rate-limiting your client because it has received more requests than allowed in a given window. To resolve and prevent it, follow these diagnostic and mitigation steps.

Immediate checks - Inspect the API response headers for Retry-After (or X-RateLimit-* headers) to see how long to wait before retrying. - Confirm which endpoint triggered the limit and whether limits differ by endpoint or method. - Check your provider dashboard for rate-limit and quota details and recent usage spikes.

HTTP 429 "Too Many Requests" means the API is rate-limiting your client because it has received more requests than allowed in a given window. To resolve and prevent it, follow these diagnostic and mitigation steps.

Rate-limit handling strategies

Respect Retry-After: if present, delay retries for the specified time.
Implement exponential backoff with jitter to avoid synchronized retries across clients.
Limit concurrency: reduce parallel requests from threads/processes or add a request queue.
Batch requests where possible or use bulk endpoints to reduce call volume.
Cache responses for repeated requests to avoid unnecessary API calls.
Debounce frequent client actions (e.g., typing-triggered requests) and aggregate them server-side.
Add server-side throttling to prevent client bursts from exhausting quota.

Common causes to investigate

Background jobs or cron tasks sending bursts of requests.
Multiple services or deployments sharing the same API key.
Endless retry loops where retries are immediate and unbounded.
Missing or incorrect use of pagination causing repeated full-data fetches.
Webhook re-delivery storms or misconfigured clients.

Example exponential backoff (pseudocode)

Use sleep = base * 2^attempt + random_jitter, cap at max_delay, and stop after max_attempts.

JavaScript example:

const maxAttempts = 6;

const baseDelay = 500; // ms

async function requestWithBackoff(fn) {

for (let attempt = 0; attempt < maxAttempts; attempt++) {

try {

  return await fn();

} catch (err) {

  if (err.status !== 429) throw err;

  const jitter = Math.random() * 100;

  const delay = Math.min(baseDelay * 2 ** attempt + jitter, 10000);

  await new Promise(r => setTimeout(r, delay));

}

}

throw new Error(‘Max retry attempts reached’);

}

Python example:

import time, random

def request_with_backoff(func):

max_attempts = 6

base = 0.5

for attempt in range(max_attempts):

    try:

        return func()

    except ApiError as e:

        if e.status_code != 429:

            raise

        jitter = random.random() * 0.1

        delay = min(base * (2 ** attempt) + jitter, 10)

        time.sleep(delay)

raise RuntimeError("Max retries reached")

Operational recommendations

Add monitoring/alerts for 429s and unusual request spikes.
Log request timestamps, endpoints, and API key usage to identify patterns.
If appropriate, request a quota increase from the API provider with evidence of legitimate usage patterns.
Rotate or segregate API keys per service to isolate noisy clients.
Ensure idempotency for operations so retries don’t cause side effects.

If you can share the response headers or a snippet of the request flow (no secrets or full keys), I can suggest targeted changes to reduce 429s.