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API rate limits are one of the most common frustrations in automation work. Your script runs perfectly in testing, then hits production and immediately gets blocked with HTTP 429 errors. Understanding how to handle rate limits properly is the difference between automation that works and automation that constantly breaks.
This guide explains what rate limits are, why they exist, and how to handle them effectively in your automation scripts.
What are API rate limits?
Rate limits control how many requests you can make to an API within a specific time window. Common patterns include:
- Per-minute limits: 100 requests per minute
- Per-hour limits: 1,000 requests per hour
- Per-day limits: 10,000 requests per day
- Concurrent request limits: Maximum 5 simultaneous connections
Most APIs implement multiple limits simultaneously. You might be allowed 1,000 requests per hour but only 100 per minute, meaning you need to respect both constraints.
Why rate limits exist
Rate limits protect API infrastructure from overload. When you make hundreds of requests per second, you consume:
- Server processing power
- Database query capacity
- Network bandwidth
- Cache resources
Without rate limits, a few aggressive clients could degrade service for all users. Rate limits ensure fair resource distribution and prevent accidental or intentional abuse.
Common rate limiting strategies
APIs use different approaches to enforce limits:
Fixed window: Limits reset at specific times (e.g., every hour at :00). Simple to implement but can lead to traffic spikes at reset times.
Sliding window: Tracks requests over a rolling time period. More accurate but slightly more complex to implement.
Token bucket: Allows burst traffic while enforcing average rate over time. The most flexible approach used by major APIs.
Understanding which strategy an API uses helps you optimize your request patterns.
Reading rate limit headers
Most well-designed APIs tell you exactly where you stand with rate limits through response headers:
X-RateLimit-Limit: 100
X-RateLimit-Remaining: 47
X-RateLimit-Reset: 1704067200These headers show:
- Your total limit (100 requests)
- How many requests remain (47)
- When the limit resets (Unix timestamp)
Always check these headers in your automation code. They're more accurate than trying to track limits yourself.
The wrong way to handle rate limits
Many developers start with approaches that seem reasonable but cause problems:
Adding fixed delays between all requests:
javascript
for (const item of data) {
await fetchData(item);
await sleep(600); // Wait after every request
}This wastes quota. If you have 100 requests available per minute but only need 10, you're artificially slowing yourself down.
Ignoring limits until you get blocked: Waiting for 429 errors and then retrying is reactive, not proactive. Your script sits idle during cooldown periods when it could be doing other work.
Guessing at safe request rates: Estimating "safe" speeds without checking actual limits leads to either wasted quota or unexpected blocking.
Better approaches to rate limiting
Track your usage proactively: Monitor how many requests you've made in the current window and pause before hitting the limit, not after.
Use response headers as truth: Your API provider knows the exact limits and current usage. Trust their headers over your own calculations.
Implement exponential backoff: When you do hit limits, wait progressively longer between retries (1 second, 2 seconds, 4 seconds, etc.) instead of retrying immediately.
Batch operations when possible: Some APIs offer batch endpoints that process multiple items in a single request. Use these to stay well under rate limits.
Handling multiple rate limit windows
When APIs enforce both per-minute and per-hour limits, you need to respect whichever is more restrictive at any moment.
The challenge: you might have plenty of hourly quota remaining but hit your per-minute limit if you burst too quickly. Or vice versa. You carefully pace requests to avoid the per-minute limit but forget about the hourly cap.
The solution: track all limit windows simultaneously and wait for whichever requires the longest delay before making your next request.
Rate limiting in distributed systems
Single-process rate limiting is straightforward. Distributed systems complicate everything because multiple processes don't know what each other are doing.
Without coordination, each process might think it's safe to make a request because it hasn't hit the limit. But collectively, they're exceeding the API's total limit.
This requires shared state, typically through Redis or a similar data store where all processes can check current usage before making requests. Most production automation platforms handle this coordination automatically.
When to pay for higher limits
If you're constantly fighting rate limits, calculate the cost of your time versus the cost of higher API tiers.
Consider upgrading when:
- You regularly hit limits during normal operations
- Rate limits add hours to essential workflows
- You're spending significant engineering time optimizing around limits
- Your automation needs run on tight schedules
A $50/month plan with 10x higher limits often costs less than the developer time spent working around free tier restrictions.
Requesting temporary limit increases
Many API providers accommodate legitimate high-volume use cases. If you need to run a one-time data migration or have a specific project requiring higher limits, reach out directly.
What to include in your request:
- Specific use case and timeline
- Expected volume and duration
- Why the default limits are insufficient
- Confirmation that usage is legitimate and non-abusive
Most API teams respond positively to clear, reasonable requests from real users with genuine needs.
Using automation platforms to handle complexity
Building robust rate limiting into your automation scripts takes time and ongoing maintenance. Platforms designed for data extraction and automation handle these details automatically:
- Distributed rate limit coordination across multiple processes
- Automatic retry logic with exponential backoff
- Proxy rotation to distribute requests across IP addresses
- Built-in monitoring and alerting when limits are hit
This lets you focus on extracting and using data instead of building infrastructure to work around API constraints.
Key takeaways
Rate limits exist to protect API infrastructure, and every automation project needs to handle them properly. The key principles:
- Read and respect rate limit headers from API responses
- Track your usage proactively instead of waiting for errors
- Handle multiple limit windows simultaneously
- Consider paying for higher limits when they save engineering time
- Use existing tools and platforms instead of building everything from scratch
With proper rate limit handling, your automation runs reliably at scale instead of breaking unexpectedly in production.
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