Bot API 429 Rate Limit during JMeter Ramp-up in Singapore Region

Looking for advice on handling rate limits when load testing the Genesys Cloud Bot API endpoints.

I am running a performance test suite using JMeter 5.6.2 to validate the throughput of the POST /api/v2/bots and GET /api/v2/bots/{botId} endpoints. The goal is to simulate a high-concurrency environment where multiple integration services are polling for bot configuration updates simultaneously. We are targeting the Singapore region (us-east-1 is not an option for latency reasons).

The test setup involves 50 virtual users ramping up over 60 seconds, each executing a loop of 100 iterations. Every iteration includes a token refresh using the POST /api/v2/oauth/token endpoint with the offline_access scope, followed by a bot configuration fetch.

At approximately 45 seconds into the test, when concurrent threads hit around 30, we start seeing a significant spike in 429 Too Many Requests errors. The response headers indicate Retry-After: 5, but the JMeter script is configured to retry immediately, which just amplifies the issue. The error rate jumps from 0% to nearly 40% of all requests failing.

I have checked the API documentation and see mentions of rate limiting, but the specific thresholds for bot-related endpoints are not clearly defined for high-concurrency scenarios. I am currently using the default pagination settings.

Has anyone successfully load tested bot endpoints with similar concurrency? Are there specific headers or query parameters I should include to optimize the request flow? I am also wondering if the token refresh overhead is contributing to the rate limit triggers, or if it is purely the bot fetch calls hitting the ceiling.

Environment details:

• Genesys Cloud API Version: v2
• JMeter Version: 5.6.2
• Region: Singapore
• Thread Count: 50
• Ramp-up: 60s
• Loop Count: 100

Any insights on adjusting the test script or understanding the rate limit behavior would be appreciated. I want to ensure our integration can handle peak loads without getting throttled.

Make sure you implement an exponential backoff strategy in your JMeter script rather than relying on static delays. The 429 errors you are seeing during the ramp-up phase are not just about raw throughput; they are a signal that the rate limiter is enforcing strict burst controls on the Bot API endpoints.

In my experience with bulk export jobs, the most reliable way to handle this is to use a Custom Java Sampler or a JSR223 PostProcessor in JMeter to capture the Retry-After header from the 429 response. Instead of guessing a wait time, let the API tell you when to retry. This mirrors how we handle rate limiting in our recording export pipelines to ensure we do not get throttled by the S3 integration layer.

Here is a simple Groovy snippet for a JSR223 PostProcessor that you can attach to your HTTP Request samplers:

// Check if the response code is 429
if (prev.getResponseCode() == "429") {
 // Extract the Retry-After header value
 String retryAfter = prev.getResponseHeader("Retry-After")
 
 // If the header exists, set a delay for the next iteration
 if (retryAfter != null) {
 int delay = Integer.parseInt(retryAfter) * 1000 // Convert seconds to milliseconds
 log.info("Rate limited. Waiting ${delay}ms before next request.")
 
 // Set a property to control the next thread's delay if needed
 ctx.getThreadGroup().setDelay(delay)
 }
}

This approach ensures that your test simulates real-world integration behavior where downstream services must respect API constraints. It also prevents the test from failing prematurely due to excessive retries. Remember that the Singapore region may have slightly different rate limit thresholds compared to US regions, so monitoring the actual Retry-After values during the test will give you accurate data for tuning your production clients.