Is there a clean way to handle rate limits when ramping up outbound campaign creation? Running JMeter with 50 threads from Singapore against /api/v2/outbound/campaigns. Hitting 429 errors immediately after 10 requests even with valid auth. Payload below:
{
"name": "test_campaign",
"contactListId": "123",
"dialRules": {
"type": "progressive"
}
}
Retry-After: 2
This is caused by the strict rate limiting on outbound campaign endpoints, which caps concurrent writes regardless of thread count. Parse the `Retry-After` header from the 429 response and implement exponential backoff in your JMeter script to stay within the platform's transactional limits.The exponential backoff strategy suggested above is technically sound for handling the Retry-After header, but it does not address the underlying architectural mismatch. The Genesys Cloud platform is not designed to ingest high-frequency outbound campaign creation requests via direct API calls during a load test. This approach bypasses the intended orchestration layers and triggers the strict transactional limits mentioned in the previous post.
For a performance validation scenario, the recommended configuration is to shift the load testing target from campaign creation to campaign execution. Instead of repeatedly hitting /api/v2/outbound/campaigns, configure the JMeter script to trigger a single, pre-validated campaign and then monitor the downstream effects on queue activity and agent performance dashboards. This aligns with standard enterprise practices for validating system stability under load without triggering API governance controls.
If campaign creation is strictly required for the test, the payload must include a unique identifier for each request to prevent idempotency conflicts, and the thread count must be reduced to match the documented write limits for the outbound API. However, this is rarely the correct method for load testing. The primary bottleneck in outbound scenarios is usually the contact list processing and dialer capacity, not the campaign metadata storage.
Consider adjusting the test parameters to focus on the dialRules execution phase. Monitor the queue_activity metrics in the Performance dashboard to observe how the system handles the progressive dialing logic under the specified concurrency. This provides a more accurate representation of production behavior and avoids the artificial constraints of API rate limiting. The 429 errors are a protective measure, not a bug, indicating that the test design needs to reflect actual operational workflows rather than synthetic API stress.