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Reader has two limits: requests per minute (RPM) and concurrent jobs. This guide focuses on RPM, which is the one that bites under sustained load.

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Know your quota

TierRPMConcurrency
Free102
Pro6010
Business20050
Enterprise1,000200
60 RPM on Pro means one request per second, every second. If your pipeline can burst above that, you’ll get 429s. Design for the average, not the peak.

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Rule #1: batch where you can

The biggest footgun is firing sync scrapes in a for loop: 
// BAD: 1000 RPM for 1000 URLs
for (const url of urls) {
  await client.read({ url });
}
One POST /v1/read with urls: [...] counts as one request toward RPM: 
// GOOD: 1 RPM for 1000 URLs
await client.read({ urls }); // creates a job, SDK polls to completion
Polling that job is more requests, but polling once every 5 seconds for a minute is 12 requests, not 1000.

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Rule #2: honor Retry-After

When you get a rate_limited error, Reader tells you how long to wait: 
{
  "error": {
    "code": "rate_limited",
    "details": { "limit": 60, "windowSeconds": 60, "retryAfterSeconds": 12 }
  }
}
The HTTP response also has a Retry-After header with the same value. The SDK honors this automatically. If you’re calling the API directly, sleep for that duration before retrying: 
if (code === "rate_limited") {
  const retryAfter = parseInt(res.headers.get("Retry-After") || "5", 10);
  await sleep(retryAfter * 1000);
  // retry
}

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Rule #3: spread out bursts

If you suddenly have 500 URLs to scrape and your tier is 60 RPM, you need to feed them through at a controlled rate. Use a token bucket or a simple delay: 
async function processBatch(urls: string[], rpm = 55) {
  const delayMs = 60_000 / rpm; // ~1090ms at 55 RPM (5 RPM headroom)
  for (const url of urls) {
    const start = Date.now();
    await client.read({ url });
    const elapsed = Date.now() - start;
    const wait = Math.max(0, delayMs - elapsed);
    if (wait > 0) await sleep(wait);
  }
}
Leave a little headroom below your tier’s RPM. Other parts of your system (credit checks, webhook management, SDK heartbeats) also make API calls.

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Rule #4: don’t poll too hard

// BAD: 60 requests per minute just watching one job
while (!done) {
  const { job } = await client.getJob(jobId);
  if (terminal(job)) break;
  await sleep(1000);
}

// GOOD: 12 requests per minute
await sleep(5000);
For long jobs, prefer webhooks over polling so the job doesn’t consume any of your rate budget.

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Rule #5: separate interactive and background traffic

If you have both user-facing requests and background workers sharing one API key, the background workers will eat the rate budget and starve the users. Two options:
  1. Two API keys, two rate limit overrides (Pro+): set a low RPM override on the background key so interactive traffic always has room.
  2. A queue in front of background work: let background requests wait, never throttle interactive requests.

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Detecting approaching limits

Watch for a rising rate_limited error rate as an early signal. The Reader dashboard shows this; you can also log it yourself and alert when it crosses a threshold.

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