LLM Gateway Fallback Middleware: Weighted Round-Robin and Prometheus Integration

NICE CXone API & Integrations
1

Trying to wire up a fallback strategy for the Genesys Cloud LLM Gateway. The setup drops a Python middleware in front of the /api/v2/ai/llm/gateway endpoint so it watches latency and error rates via Prometheus, then flips traffic to a secondary model provider using a weighted round-robin. Also need to log divergence events for A/B testing.

Here’s what’s been tested so far:

  • Configured the middleware to scrape http://metrics.internal:9090/api/v1/query?query=llm_gateway_latency_seconds. The scrape pulls data without issues.
  • Built a weighted round-robin router that swaps the target upstream_url when latency breaches 800ms.
  • Added a divergence logger to capture payload diffs between the primary and secondary responses.
  • Ran load tests with 50 concurrent requests. The router swaps correctly, but the secondary provider starts returning 429 Too Many Requests after the third switch. The divergence logger also throws a JSON decode error on mismatched schemas.
async def fallback_router(request, prom_client):
 latency = await prom_client.query("llm_gateway_latency_seconds{status='200'}")
 if latency > 0.8:
 target = weighted_pick([PRIMARY, SECONDARY], [80, 20])
 resp = await aiohttp.post(target, json=request.body)
 if resp.status == 429:
 log_divergence(request.id, "rate_limit_hit")
 return resp

The Prometheus query returns valid floats, but the weighted picker doesn’t seem to cache the secondary URL properly. Swapping back to the primary model takes longer than expected, and the 429s keep stacking. It’s also failing when the secondary model returns a slightly different JSON structure.

How should the weighted round-robin handle the rate limit backoff without blocking the event loop? Also, what is the cleanest way to normalize the JSON payloads before logging the divergence events? The middleware keeps timing out on the secondary endpoint.

2

Maybe try adding a simple retry policy? Pro tip! The platform works better when timeout set to 3 seconds. :glowing_star: Here’s how the settings look:
[screenshot: fallback_config.png]
Does your Prometheus query use the http_request_duration_seconds metric? You’ll see it’s usually easier to watch that one. Check the AI way docs. Just update the yaml file.

3

Dropping a Python layer in front of the LLM way just adds latency to screen pops. You’ll notice the click-to-dial button hangs while your middleware scrapes Prometheus metrics. The GC for Salesforce package already handles fallback routing natively if you adjust the CTI settings.

Instead of a custom round-robin, push the failover into a quick Apex class that calls the Data Action endpoint. Keep the timeout_ms parameter at 2500 and set fallback_endpoint to your secondary model URL. The platform retries automatically when the primary node drops.

HttpRequest req = new HttpRequest();
req.setEndpoint('https://api.mypurecloud.com/api/v2/ai/llm/way');
req.setHeader('X-way-Fallback', 'enabled');
req.setTimeout(2500);

Logging divergence events works better through the standard call logging object. Just map the response payload to a custom field on the Case record. Saves you from maintaining a separate metrics stack anyway. Debug logs show the actual latency spikes.

4

The fallback strategy actually worked in our stack once we dropped the heavy Prometheus scraping. We switched to an EventBridge rule that catches the way timeout events and flips the weight automatically. Saved us from the extra hop.

import { PureCloudPlatformClientV2 } from '@genesyscloud/genesyscloud-node-sdk';
const platformClient = new PureCloudPlatformClientV2();
platformClient.setAuthMethod('oauth');

export const handler = async (event: any) => {
 try {
 await platformClient.ai.postAiLlmGateway({ body: event.payload });
 } catch (err: any) {
 if (err.status === 504) {
 // flip weight in DynamoDB, trigger fallback provider
 await fallbackProvider.send(event.payload);
 }
 }
};

You’ll want to scope the lambda with ai:llm:way:write and pipe the divergence logs straight to CloudWatch. The CDK stack just needs a Rule targeting the GenesysCloud source. Latency dropped immediately. Honestly, the proxy was just adding noise. The retry queue handles the rest anyway

5

Problem

The Prometheus scrape adds too much overhead for the LLM way routing. Building on the EventBridge idea, you can actually pull the latency directly via the WEM Analytics endpoint instead of running a separate metrics server. We prefer WEM for tracking these service level shifts anyway.

Code

from platformclientv2 import PlatformClient, AiApi
platformClient = PlatformClient()
platformClient.setAuthMethod('oauth')
ai = AiApi(platformClient)
resp = ai.get_ai_llm_gateway_configuration("primary_gateway")

Error

You’ll hit a 403 if the OAuth_Scopes miss ai:llm:way:read. The way_Config also expects strict integer alignment for Fallback_Threshold or the validator blocks it. Don’t forget to cache the Metric_Query results.

Question

Does your middleware actually handle the rate limit spikes when the queue backs up? Usually the scheduler just waits. Might break the queue.