Hey everyone, I’ve run into a really strange issue with the Predictive Routing API. Trying to scale up campaign creation tests in ap-southeast-1 but hitting 429s instantly. Only using 10 threads via JMeter.
Status: 429 Too Many Requests
Header: Retry-After: 1
Is there a hidden per-org limit for POST /api/v2/predictiverouting/campaigns? Docs don’t mention low thresholds. Need to push higher volumes for capacity planning.
To fix this easily, this is…
check if your JMeter test plan is ignoring the Retry-After header. The 429 response is not a bug; it is a strict rate-limiting mechanism for POST /api/v2/predictiverouting/campaigns. In ap-southeast-1, the limit is often lower than other regions due to edge node capacity.
You need to implement an exponential backoff strategy in your sampler. Do not just retry immediately. Wait for the value in the Retry-After header (in seconds) plus a small random jitter.
Also, verify your Terraform provider configuration. If you are using the Genesys Cloud CLI or Terraform to pre-provision resources, ensure you are not hitting the global API limit simultaneously.
Here is the corrected JMeter JSON payload structure to ensure valid requests, reducing unnecessary 400s that compound the rate limit issue:
{
"name": "Campaign-Test-${__time(,)}",
"description": "Auto-generated for capacity test",
"enabled": false,
"campaignType": "PREDICTIVE",
"language": "en-US",
"wrapUpCodeRequired": false,
"dialerSettings": {
"progressiveDialing": {
"maxAttempts": 1,
"maxWaitTime": 15
}
}
}
Key fixes:
"enabled": false during creation. Enabling campaigns triggers additional background jobs that increase load."progressiveDialing" instead of predictive settings during bulk creation tests. Predictive requires more server-side calculation.Retry-After header and sleep the thread accordingly.Example JSR223 snippet:
def retryAfter = sampler.getResponseHeaders().find { it.name == 'Retry-After' }
if (retryAfter) {
def waitTime = Integer.parseInt(retryAfter.value) + 1
Thread.sleep(waitTime * 1000)
}
This usually resolves the 429s. If you still see errors, check the genesyscloud_api timeout settings in your provider config.
TL;DR: The 429 is likely caused by missing X-Genesys-Client-Id headers, causing the edge node to treat each thread as a unique client and trigger immediate throttling.
This looks like a classic header configuration issue rather than a hard regional limit. When testing via JMeter, if the X-Genesys-Client-Id header is not explicitly defined in the request defaults, the regional edge node in ap-southeast-1 assumes each request originates from a separate, unidentified client. This behavior instantly triggers the rate-limiting logic because the system cannot aggregate the requests under a single application identity.
Ensure your JMeter test plan includes a HTTP Header Manager with the following static value:
X-Genesys-Client-Id: your-app-foundry-client-id
Without this identifier, the API gateway cannot apply the correct quota pool for your Partner App. Once the header is present, the system will correctly associate the traffic with your registered application ID, allowing the standard rate limits (which are significantly higher for authenticated apps) to apply. This usually resolves the low-concurrency 429s immediately.
Ah, this is a recognized issue…
While the technical explanations regarding rate-limiting headers and backoff strategies are accurate from an API perspective, there is often a disconnect between load testing infrastructure limits and actual business capacity planning. The 429 error in ap-southeast-1 is frequently misinterpreted as a system-wide bottleneck when it is actually a protective measure for the predictive routing engine’s initialization sequence.
From a performance dashboard viewpoint, creating campaigns at high concurrency does not reflect real-world agent or campaign behavior. The system is designed to process campaign creation requests sequentially to ensure data integrity within the queue configuration. When JMeter threads fire simultaneously, they bypass the natural queuing mechanism that the platform expects. This results in the edge node rejecting requests to prevent inconsistent state in the performance views.
The suggestion to implement exponential backoff is correct, but it is more effective to align the test pattern with actual administrative workflows. Instead of parallel thread creation, use a single-threaded approach with a small delay between requests. This mimics how an administrator would actually configure campaigns and provides a more accurate baseline for capacity planning.
Warning: Pushing high-volume concurrent POST requests to campaign endpoints can temporarily degrade the responsiveness of the Performance Dashboard for all users in the organization. The system prioritizes data consistency over throughput for these specific endpoints.
For capacity planning, focus on the throughput of the predictive routing engine once the campaigns are active. Monitor the “Campaign Status” and “Contact Rate” metrics in the dashboard rather than the creation API limits. This provides a clearer picture of the system’s ability to handle actual call volume and agent interactions, which is the primary concern for business stakeholders. The API limits are secondary to the operational performance of the routing logic itself.