Your automation has been running flawlessly for weeks. Then one Tuesday morning, everything stops. Orders are not syncing, inventory updates are failing, and your automation logs are filled with HTTP 429 errors. You have hit an API rate limit, and understanding how to handle this is essential for anyone building production-grade workflows on platforms like Make.com or Zapier.
What Is API Rate Limiting?
Every API enforces limits on how many requests a client can make within a given time window. These limits protect the service's infrastructure from being overwhelmed by a single integration. When you exceed the limit, the API responds with HTTP status code 429 (Too Many Requests) and typically includes a Retry-After header telling you how long to wait before trying again.
Rate limits vary dramatically by platform. Shopify allows 2 requests per second for REST API calls. QuickBooks Online permits about 500 requests per minute. ShipStation allows 40 requests per minute. If your automation does not account for these limits, it will inevitably fail during high-volume periods.
Figure 1: Rate limits vary significantly across platforms. ShipStation's 40 req/min limit is particularly easy to hit during bulk operations.
Why Rate Limits Hit You on Busy Days
Rate limiting often manifests during your busiest periods, which is exactly when you can least afford it. A flash sale that generates 500 orders triggers 500 API calls to create those orders in your accounting system, plus 500 calls to create shipments, plus 500 calls to update inventory. That is 1,500 API calls in rapid succession. If your platform limit is 40 per minute, processing all of them takes nearly 38 minutes, assuming no other automation is sharing the same API key.
Strategy 1: Exponential Backoff with Jitter
When you receive a 429 response, the worst thing you can do is immediately retry. The best practice is exponential backoff: wait 1 second, then 2 seconds, then 4 seconds, doubling each time. Adding random "jitter" (a random delay of 0 to 1 second) prevents multiple retries from synchronizing and hitting the limit simultaneously. In Make.com, attach an error handler with a "Break" directive that auto-retries after a configurable delay. Set the retry count to 3 and enable exponential backoff.
Strategy 2: Request Batching
Many APIs offer batch endpoints that process multiple operations in a single request. QuickBooks has a batch endpoint supporting up to 30 operations per call. Shopify's GraphQL API lets you bundle multiple queries. Using batch endpoints can reduce your request count by 90% or more. In Make.com, use the Array Aggregator to collect multiple records, then send them in a single HTTP call to the batch endpoint.
Strategy 3: Queue-Based Processing
Instead of processing every order immediately as it arrives, queue them and process at a controlled rate. A queue-based approach decouples order arrival from API execution. Orders arrive in bursts; your automation processes them at a steady, rate-limit-safe pace. Use a data store or Google Sheet as a simple queue. A scheduled scenario reads from the queue at fixed intervals, processing only as many records as the rate limit allows per cycle.
Figure 2: A queue decouples order volume spikes from API rate limits, ensuring all requests succeed.
Strategy 4: Monitoring and Alerts
Track your API usage proactively. Many APIs return rate limit headers with every response: X-RateLimit-Remaining tells you how many requests you have left in the current window. Log this value and set an alert when it drops below 20% of the limit. This gives you early warning before you actually hit the limit and start losing data.
Strategy 5: Distribute Load Across Time
If your automation runs on a schedule, avoid running all scenarios at the same time. Stagger scheduled scenarios by a few minutes so they do not compete for the same rate limit window. Move non-urgent operations like report generation and analytics syncs to off-peak hours. Reserve your rate limit budget for real-time, business-critical operations like order processing and inventory updates.
API rate limiting is not a bug; it is a constraint you must design around. The difference between an amateur and professional automation is how it handles the moment when the API says "slow down." For more troubleshooting on related topics, read our guide on debugging webhook failures and fixing QuickBooks sync errors.
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