metrics.md

Metrics

This guide describes the current state of exposed metrics and how to scrape them.

Requirements

To have response metrics, set the body mode to Buffered or Streamed:

apiVersion: gateway.envoyproxy.io/v1alpha1
kind: EnvoyExtensionPolicy
metadata:
  name: ext-proc-policy
  namespace: default
spec:
  extProc:
    - backendRefs:
      - group: ""
        kind: Service
        name: inference-gateway-ext-proc
        port: 9002
      processingMode:
        request:
          body: Buffered
        response:
          body: Buffered

If you want to include usage metrics for vLLM model server streaming request, send the request with include_usage:

curl -i ${IP}:${PORT}/v1/completions -H 'Content-Type: application/json' -d '{
"model": "tweet-summary",
"prompt": "whats your fav movie?",
"max_tokens": 10,
"temperature": 0,
"stream": true,
"stream_options": {"include_usage": "true"}
}'

Exposed metrics

Metric name	Metric Type	Description	Labels	Status
inference_model_request_total	Counter	The counter of requests broken out for each model.	model_name=<model-name> target_model_name=<target-model-name>	ALPHA
inference_model_request_error_total	Counter	The counter of requests errors broken out for each model.	model_name=<model-name> target_model_name=<target-model-name>	ALPHA
inference_model_request_duration_seconds	Distribution	Distribution of response latency.	model_name=<model-name> target_model_name=<target-model-name>	ALPHA
inference_model_request_sizes	Distribution	Distribution of request size in bytes.	model_name=<model-name> target_model_name=<target-model-name>	ALPHA
inference_model_response_sizes	Distribution	Distribution of response size in bytes.	model_name=<model-name> target_model_name=<target-model-name>	ALPHA
inference_model_input_tokens	Distribution	Distribution of input token count.	model_name=<model-name> target_model_name=<target-model-name>	ALPHA
inference_model_output_tokens	Distribution	Distribution of output token count.	model_name=<model-name> target_model_name=<target-model-name>	ALPHA
inference_pool_average_kv_cache_utilization	Gauge	The average kv cache utilization for an inference server pool.	name=<inference-pool-name>	ALPHA
inference_pool_average_queue_size	Gauge	The average number of requests pending in the model server queue.	name=<inference-pool-name>	ALPHA

Scrape Metrics

Metrics endpoint is exposed at port 9090 by default. To scrape metrics, the client needs a ClusterRole with the following rule: nonResourceURLs: "/metrics", verbs: get.

Here is one example if the client needs to mound the secret to act as the service account

---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: inference-gateway-metrics-reader
rules:
- nonResourceURLs:
  - /metrics
  verbs:
  - get
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: inference-gateway-sa-metrics-reader
  namespace: default
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: inference-gateway-sa-metrics-reader-role-binding
  namespace: default
subjects:
- kind: ServiceAccount
  name: inference-gateway-sa-metrics-reader
  namespace: default
roleRef:
  kind: ClusterRole
  name: inference-gateway-metrics-reader
  apiGroup: rbac.authorization.k8s.io
---
apiVersion: v1
kind: Secret
metadata:
  name: inference-gateway-sa-metrics-reader-secret
  namespace: default
  annotations:
    kubernetes.io/service-account.name: inference-gateway-sa-metrics-reader
type: kubernetes.io/service-account-token

Then, you can curl the 9090 port like following

TOKEN=$(kubectl -n default get secret inference-gateway-sa-metrics-reader-secret  -o jsonpath='{.secrets[0].name}' -o jsonpath='{.data.token}' | base64 --decode)

kubectl -n default port-forward inference-gateway-ext-proc-pod-name  9090

curl -H "Authorization: Bearer $TOKEN" localhost:9090/metrics