Does anyone know the specific rate limit thresholds for the Quality Management API when exporting scorecards across multiple organizations via a Partner App? We are building a premium integration that aggregates quality metrics for clients using a multi-org structure. The application uses the standard OAuth client credentials flow to generate tokens for each organization context.
The issue arises when attempting to batch export scorecard results. The API endpoint GET /api/v2/quality/scorecards/{id}/results is returning HTTP 429 Too Many Requests after approximately 15 concurrent requests per second, despite our app being whitelisted for higher throughput in AppFoundry. The response headers indicate a Retry-After value, but the calculation seems inconsistent.
Here is the current configuration snippet for our retry logic:
We have verified that the X-Genesys-Client-Id is correctly passed in every request to ensure the rate limit counter is associated with our application rather than individual user tokens. However, the limits appear to be shared across all organizations in the partner hierarchy, which is unexpected. Is there a way to isolate rate limits per organization or increase the ceiling for QAPI bulk operations?
The docs actually state that the Quality Management API endpoints, particularly those involved in bulk scorecard exports, are subject to strict rate limiting that is independent of the underlying SIP trunk capacity or BYOC registration states. While managing fifteen BYOC trunks across the Asia/Singapore region often involves dealing with carrier-specific quirks and SIP registration timeouts, the QAPI limits are calculated per organization context and per OAuth token. When building a multi-org partner application, the standard approach of generating a separate client credentials token for each organization does not automatically aggregate the rate limit buckets; instead, each token is treated as a distinct entity with its own quota. To avoid hitting the 429 Too Many Requests error during batch exports, it is crucial to implement exponential backoff logic in the integration code. A common pattern involves checking the Retry-After header returned in the 429 response and waiting for the specified duration before retrying the request. Additionally, staggering the export requests across different time windows for each organization can help distribute the load and prevent simultaneous saturation of the API gateways. For example, if exporting data for ten organizations, initiating the exports with a random delay between 100ms and 500ms for each subsequent organization can significantly reduce the likelihood of concurrent limit breaches. It is also worth noting that the rate limits may vary depending on the subscription tier of the partner organizations, so designing the integration to dynamically adapt to these constraints based on the API response headers is essential for robust performance. Monitoring the usage metrics via the admin portal can provide insights into current consumption patterns, allowing for better planning of batch operations during off-peak hours.
According to the docs, they say that Quality API rate limits are distinct from WFM scheduling endpoints. While I usually deal with schedule publishing spikes at 07:00 CST, the principle of staggering requests applies here too. If you are hitting 429s, your current parallel execution strategy is likely overwhelming the per-token limits.
Try implementing a simple delay between API calls. This prevents the flood and keeps your integration stable. A constant timer of 200ms between requests often resolves these issues without needing complex backoff algorithms.
This approach works well for batch exports. It ensures each organization context gets processed within its specific quota. I have seen similar issues with Analytics API exports, and this simple adjustment made a huge difference. Stick to sequential processing per org to stay under the radar.