Predictive Outbound Campaign failing with 429 errors during JMeter load test in ap-southeast-1

Does anyone know how to properly handle rate limiting for Predictive Outbound campaigns when simulating high concurrency?

Background

I am running load tests for a new Predictive Outbound campaign setup in the ap-southeast-1 region. The goal is to validate API throughput and system stability under stress. I am using JMeter to simulate a burst of call dispositions and campaign status updates. The script targets the /api/v2/outbound/campaigns/{id}/start endpoint followed by rapid succession calls to /api/v2/outbound/predictivedials/contacts to update disposition codes. My JMeter thread group is configured with 200 concurrent users, ramp-up period of 10 seconds, and a loop count of 5. This is meant to mimic a high-volume BPO environment during peak hours.

Issue

Almost immediately after the ramp-up phase, the responses start failing. Instead of the expected 200 OK or 202 Accepted, I am getting a flood of 429 Too Many Requests errors. The response headers include Retry-After: 1, but even with that delay, the error rate stays above 80% when concurrency exceeds 50 threads. The error message body just says “Rate limit exceeded”. This is happening specifically for the predictive dialer endpoints, while other API calls like user management seem fine.

Troubleshooting

I have checked the API documentation and confirmed the rate limits for outbound operations. I tried adding a constant throughput timer in JMeter to limit requests per second, but the errors persist. I also verified that the campaign is active and has enough contacts. The issue seems to be tied to the concurrent nature of the predictive dialer logic rather than a simple endpoint limit. Is there a specific header or parameter I need to include to handle predictive outbound load better? Or is there a known limitation for ap-southeast-1 that I am missing? Any advice on tuning JMeter for this specific use case would be appreciated.

This is caused by thread group pacing that ignores Genesys Cloud’s API rate limits, unlike Zendesk’s bulk import throttling. Adjust Ramp-Up Period and Concurrent Users in JMeter to match the natural flow of Zendesk ticket updates. This prevents the 429 errors by smoothing out the burst of disposition calls.

{
 "rampUpTime": 120,
 "threadCount": 50,
 "loopCount": 1
}

This looks like a classic case of JMeter thread groups ignoring Genesys Cloud’s strict API rate limits. The 429 errors occur when the burst of disposition calls exceeds the allowed throughput for the ap-southeast-1 region. Adjusting the ramp-up period and concurrent users in JMeter helps smooth out the burst, mimicking natural agent flow rather than a sudden spike.

Genesys Cloud’s predictive outbound system handles concurrency differently than Zendesk’s bulk imports, so treating them the same way leads to throttling. By pacing the load test, you prevent overwhelming the API endpoints. Also, consider implementing exponential backoff in your script to handle retries gracefully when rate limits are hit. This approach ensures more accurate stress testing without triggering unnecessary errors.