Trying to understand the rate limit behavior for the messaging APIs. Running JMeter scripts hitting /api/v2/conversations/messaging with 500 concurrent users. Responses return 429 Too Many Requests after 100 requests per second. The retry-after header is inconsistent. Is this a hard limit per org or per user? Need to adjust load patterns to avoid throttling during capacity validation. Any docs on burst limits?
The problem here is treating the messaging endpoint like a static rate-limit bucket. It is not. The 429s usually stem from the underlying WebSocket connection pool exhaustion rather than a simple HTTP request count.
Try shifting the load test strategy. Instead of hammering the REST API directly for every message send, use the Genesys Cloud CLI to pre-warm the session tokens or leverage the genesyscloud_utility resource in Terraform to manage concurrent connection limits programmatically.
Warning: Do not blindly retry on 429 without exponential backoff. You will trigger a harder lockout.
Here is a snippet for managing the concurrency in your pipeline:
resource "genesyscloud_outbound_campaign" "messaging" {
max_concurrent_conversations = 50 # Start low
dialer_type = "progressive"
}
The retry-after header inconsistency is a known artifact of the regional load balancers in EU-West. Align your JMeter thread groups with the max_concurrent_conversations setting in the campaign resource. This usually stabilizes the throughput without hitting the hard cap. Check the analytics reporting for actual accepted vs dropped metrics to validate the fix.
This issue stems from the default webhook throttling in ServiceNow incident creation, which caps at 50 calls per minute. If you are batching high-concurrency messaging events, the downstream integration will choke before the Genesys API does. Check these:
- ServiceNow REST message rate limits
- Genesys Data Action retry policies
- Webhook payload size constraints
Check your JMeter thread group configuration. The 429s are likely hitting the platform_api rate limit, not a WebSocket issue. For messaging endpoints, the limit is often per org for bulk operations, but per user for agent actions. You need to implement an exponential backoff in your script.
Here is a basic JSR223 PostProcessor snippet to handle retry-after:
def responseCode = prev.getResponseCode()
if (responseCode == 429) {
def retryAfter = prev.getResponseHeader("Retry-After")
if (retryAfter != null) {
SampleResult.setIgnore(true)
Thread.sleep(retryAfter.toLong() * 1000)
} else {
Thread.sleep(1000) // fallback
}
}
Also, verify your JMeter HTTP Request Defaults. Ensure Use KeepAlive is checked to reduce handshake overhead. The 429 header inconsistency suggests the load balancer is dropping packets before the API gateway processes them. Try reducing concurrency to 200 users and monitor api_throughput in Genesys Cloud analytics.
def retryAfter = prev.getResponseHeader("Retry-After")
if (retryAfter != null) {
// Convert seconds to milliseconds for Thread.sleep
sleepTime = Long.parseLong(retryAfter) * 1000
log.info "Throttled. Sleeping for ${sleepTime}ms"
Thread.sleep(sleepTime)
prev.setResponseCode(0)
prev.setSuccessful(true)
}
> "The retry-after header is inconsistent. Is this a hard limit per org or per user?"
The 429 errors you are encountering are not strictly a function of the messaging API’s internal queue depth, but rather a direct consequence of how the platform enforces rate limits across different resource types. In the context of high-concurrency load testing, especially when simulating BYOC trunk interactions or bulk message sends, the Retry-After header is the single most reliable indicator for backoff timing. The inconsistency mentioned in the original post often stems from the fact that rate limits can be scoped differently depending on whether the request originates from a user context or an application token.
For messaging endpoints, the limit is generally applied per organization for bulk operations, but it can shift to per-user or per-queue limits for agent-facing actions. The Groovy snippet above ensures that your JMeter script respects the server’s explicit backoff instruction, which is crucial for avoiding cascading failures during capacity validation.
Additionally, it is worth noting that carrier-specific failover logic can sometimes introduce latency that mimics throttling behavior. If the underlying SIP registration state is fluctuating during the test, the platform may temporarily restrict API calls to maintain stability. Ensure that your test environment’s BYOC trunks are in a stable registered state and that no outbound routing conflicts are triggering unexpected failover sequences. This approach should provide a more accurate reflection of the system’s true throughput capabilities without triggering artificial rate limits.