feat(api): add Kong RED metrics for LLM routes

Cluster-wide prometheus KongClusterPlugin (kong-metrics.yaml) +
chart-native ServiceMonitor (kong-values.yaml) expose
kong_http_requests_total/kong_latency_bucket/kong_bandwidth_bytes for
every route, LLM and otherwise. Dashboard filters to route=~"llm-.*"
for request rate, error rate, p95 upstream latency, and bandwidth.
Token-count metrics still need ai-proxy-advanced (Enterprise-only);
not attempted.

k8s/apps/api/kong-metrics.yaml

@@ -0,0 +1,19 @@
+# Cluster-wide Kong Prometheus plugin -- `global: "true"` label makes the
+# ingress controller apply it to every route on this Kong instance, so all
+# five LLM routes (ornith/reasoning/qwen/embeddings/rerank) get RED metrics
+# without touching llm-routes.yaml. Scraped via kong-values.yaml's
+# serviceMonitor (status listener, already on by chart default at :8100).
+apiVersion: configuration.konghq.com/v1
+kind: KongClusterPlugin
+metadata:
+  name: prometheus
+  annotations:
+    kubernetes.io/ingress.class: kong
+  labels:
+    global: "true"
+plugin: prometheus
+config:
+  status_code_metrics: true
+  latency_metrics: true
+  bandwidth_metrics: true
+  upstream_health_metrics: true

k8s/apps/api/kong-values.yaml

@@ -118,6 +118,16 @@ podDisruptionBudget:
   enabled: true
   minAvailable: 1
 
+# Status listener (metrics/health) is on by default at :8100 (chart default,
+# verified via `helm show values`). This just wires the ServiceMonitor the
+# chart already knows how to generate for it, so kong_http_requests_total /
+# kong_latency_* / kong_bandwidth_bytes land in Prometheus. Paired with the
+# cluster-wide `prometheus` KongClusterPlugin in kong-metrics.yaml.
+serviceMonitor:
+  enabled: true
+  labels:
+    release: kube-prometheus-stack
+
 # Spread the two replicas across nodes; `ScheduleAnyway` so a single-node
 # situation degrades to co-location instead of leaving a pod Pending.
 topologySpreadConstraints:

k8s/apps/api/kustomization.yaml

@@ -6,6 +6,7 @@ kind: Kustomization
 # or it is silently dropped with no error and no drift shown.
 resources:
   - ingress.yaml
+  - kong-metrics.yaml
   - llm-routes.yaml
   - model-auth.yaml
 # No top-level `namespace:` transformer on purpose: ingress.yaml sets its own

k8s/infra/monitoring/dashboards/llm-frontend.yaml

@@ -7,12 +7,15 @@ metadata:
     grafana_dashboard: "1"
   annotations:
     grafana_folder: "LLM"
-    # No ServiceMonitor/PodMonitor exists yet for Kong or the KServe
+    # Request rate/error/latency/bandwidth now come from Kong's prometheus
-    # predictors (ornith/reasoning/qwen/embeddings/reranker/verifier), so
+    # plugin (KongClusterPlugin in kong-metrics.yaml, global: true) via the
-    # there is no request-rate/latency/token metric to show. This dashboard
+    # chart's own ServiceMonitor (kong-values.yaml serviceMonitor.enabled) --
-    # is resources + logs only, sourced from cAdvisor/kube-state-metrics
+    # every LLM route runs through Kong, so this covers ornith/reasoning/qwen/
-    # (cluster-wide, no extra scrape config needed) and Loki. Add RED-metric
+    # embeddings/rerank uniformly without per-backend instrumentation.
-    # panels once a metrics exporter exists for those services.
+    # Token-count metrics are still not available: that needs response-body
+    # parsing, which Kong only does via ai-proxy-advanced (Enterprise-only).
+    # Predictor-level metrics (native Ollama/vLLM stats) also still need a
+    # dedicated exporter -- not added here.
 data:
   llm-frontend.json: |
-    {"title":"LLM Frontend","uid":"llm-frontend","schemaVersion":39,"timezone":"browser","time":{"from":"now-6h","to":"now"},"refresh":"30s","panels":[{"id":1,"title":"Row: Availability","type":"row","collapsed":true,"gridPos":{"h":1,"w":24,"x":0,"y":0},"panels":[{"id":2,"title":"llm-serving pods ready","type":"stat","gridPos":{"h":4,"w":8,"x":0,"y":1},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(kube_pod_status_ready{namespace=\"llm-serving\",condition=\"true\"})"}]},{"id":3,"title":"agent-pod ready","type":"stat","gridPos":{"h":4,"w":8,"x":8,"y":1},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(kube_pod_status_ready{namespace=\"agent-pod\",condition=\"true\"})"}]},{"id":4,"title":"kong (api) pods ready","type":"stat","gridPos":{"h":4,"w":8,"x":16,"y":1},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(kube_pod_status_ready{namespace=\"api\",condition=\"true\"})"}]}]},{"id":10,"title":"Row: Resources","type":"row","collapsed":true,"gridPos":{"h":1,"w":24,"x":0,"y":1},"panels":[{"id":11,"title":"CPU by pod","type":"timeseries","gridPos":{"h":8,"w":12,"x":0,"y":2},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(rate(container_cpu_usage_seconds_total{namespace=~\"llm-serving|agent-pod|api\"}[5m])) by (namespace, pod)","legendFormat":"{{namespace}}/{{pod}}"}]},{"id":12,"title":"Memory by pod","type":"timeseries","gridPos":{"h":8,"w":12,"x":12,"y":2},"fieldConfig":{"defaults":{"unit":"bytes"}},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(container_memory_working_set_bytes{namespace=~\"llm-serving|agent-pod|api\"}) by (namespace, pod)","legendFormat":"{{namespace}}/{{pod}}"}]},{"id":13,"title":"GPU-node predictor restarts","type":"timeseries","gridPos":{"h":8,"w":24,"x":0,"y":10},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(rate(kube_pod_container_status_restarts_total{namespace=\"llm-serving\"}[15m])) by (pod)","legendFormat":"{{pod}}"}]}]},{"id":20,"title":"Row: Logs","type":"row","collapsed":true,"gridPos":{"h":1,"w":24,"x":0,"y":2},"panels":[{"id":21,"title":"llm-serving logs","type":"logs","gridPos":{"h":10,"w":24,"x":0,"y":3},"datasource":{"type":"loki","uid":"loki"},"targets":[{"expr":"{namespace=\"llm-serving\"}"}]},{"id":22,"title":"agent-pod logs (pi runs)","type":"logs","gridPos":{"h":10,"w":24,"x":0,"y":13},"datasource":{"type":"loki","uid":"loki"},"targets":[{"expr":"{namespace=\"agent-pod\"}"}]},{"id":23,"title":"api (kong) logs","type":"logs","gridPos":{"h":10,"w":24,"x":0,"y":23},"datasource":{"type":"loki","uid":"loki"},"targets":[{"expr":"{namespace=\"api\"}"}]}]}]}
+    {"title":"LLM Frontend","uid":"llm-frontend","schemaVersion":39,"timezone":"browser","time":{"from":"now-6h","to":"now"},"refresh":"30s","panels":[{"id":1,"title":"Row: Availability","type":"row","collapsed":true,"gridPos":{"h":1,"w":24,"x":0,"y":0},"panels":[{"id":2,"title":"llm-serving pods ready","type":"stat","gridPos":{"h":4,"w":8,"x":0,"y":1},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(kube_pod_status_ready{namespace=\"llm-serving\",condition=\"true\"})"}]},{"id":3,"title":"agent-pod ready","type":"stat","gridPos":{"h":4,"w":8,"x":8,"y":1},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(kube_pod_status_ready{namespace=\"agent-pod\",condition=\"true\"})"}]},{"id":4,"title":"kong (api) pods ready","type":"stat","gridPos":{"h":4,"w":8,"x":16,"y":1},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(kube_pod_status_ready{namespace=\"api\",condition=\"true\"})"}]}]},{"id":10,"title":"Row: Resources","type":"row","collapsed":true,"gridPos":{"h":1,"w":24,"x":0,"y":1},"panels":[{"id":11,"title":"CPU by pod","type":"timeseries","gridPos":{"h":8,"w":12,"x":0,"y":2},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(rate(container_cpu_usage_seconds_total{namespace=~\"llm-serving|agent-pod|api\"}[5m])) by (namespace, pod)","legendFormat":"{{namespace}}/{{pod}}"}]},{"id":12,"title":"Memory by pod","type":"timeseries","gridPos":{"h":8,"w":12,"x":12,"y":2},"fieldConfig":{"defaults":{"unit":"bytes"}},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(container_memory_working_set_bytes{namespace=~\"llm-serving|agent-pod|api\"}) by (namespace, pod)","legendFormat":"{{namespace}}/{{pod}}"}]},{"id":13,"title":"GPU-node predictor restarts","type":"timeseries","gridPos":{"h":8,"w":24,"x":0,"y":10},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(rate(kube_pod_container_status_restarts_total{namespace=\"llm-serving\"}[15m])) by (pod)","legendFormat":"{{pod}}"}]}]},{"id":15,"title":"Row: Request Rate & Latency (Kong)","type":"row","collapsed":true,"gridPos":{"h":1,"w":24,"x":0,"y":2},"panels":[{"id":16,"title":"Request rate by route","type":"timeseries","gridPos":{"h":8,"w":8,"x":0,"y":3},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(rate(kong_http_requests_total{route=~\"llm-.*\"}[5m])) by (route)","legendFormat":"{{route}}"}]},{"id":17,"title":"Error rate %","type":"timeseries","gridPos":{"h":8,"w":8,"x":8,"y":3},"fieldConfig":{"defaults":{"unit":"percent"}},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(rate(kong_http_requests_total{route=~\"llm-.*\",code=~\"5..\"}[5m])) / sum(rate(kong_http_requests_total{route=~\"llm-.*\"}[5m])) * 100"}]},{"id":18,"title":"p95 upstream latency","type":"timeseries","gridPos":{"h":8,"w":8,"x":16,"y":3},"fieldConfig":{"defaults":{"unit":"ms"}},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"histogram_quantile(0.95, sum(rate(kong_latency_bucket{route=~\"llm-.*\",type=\"upstream\"}[5m])) by (le, route))","legendFormat":"{{route}}"}]},{"id":19,"title":"Bandwidth by route","type":"timeseries","gridPos":{"h":8,"w":24,"x":0,"y":11},"fieldConfig":{"defaults":{"unit":"Bps"}},"datasource":{"type":"prometheus","uid":"prometheus"},"targets":[{"expr":"sum(rate(kong_bandwidth_bytes{route=~\"llm-.*\"}[5m])) by (route, direction)","legendFormat":"{{route}}/{{direction}}"}]}]},{"id":20,"title":"Row: Logs","type":"row","collapsed":true,"gridPos":{"h":1,"w":24,"x":0,"y":3},"panels":[{"id":21,"title":"llm-serving logs","type":"logs","gridPos":{"h":10,"w":24,"x":0,"y":4},"datasource":{"type":"loki","uid":"loki"},"targets":[{"expr":"{namespace=\"llm-serving\"}"}]},{"id":22,"title":"agent-pod logs (pi runs)","type":"logs","gridPos":{"h":10,"w":24,"x":0,"y":14},"datasource":{"type":"loki","uid":"loki"},"targets":[{"expr":"{namespace=\"agent-pod\"}"}]},{"id":23,"title":"api (kong) logs","type":"logs","gridPos":{"h":10,"w":24,"x":0,"y":24},"datasource":{"type":"loki","uid":"loki"},"targets":[{"expr":"{namespace=\"api\"}"}]}]}]}