Recommend Mode¶

Recommend mode is the safest way to start with attune. The operator collects Prometheus metrics, computes recommendations, and writes them to the policy's .status.recommendations field. No pods are modified.

apiVersion: attune.io/v1alpha1
kind: AttunePolicy
metadata:
  name: api-services
  namespace: production
spec:
  targetRef:
    kind: Deployment
    selector:
      matchLabels:
        tier: api
  metricsSource:
    prometheus:
      address: http://prometheus-server.monitoring:80
    historyWindow: 168h
    minimumDataPoints: 48
  cpu:
    percentile: 95
    overhead: "20"
    minAllowed: "50m"
    maxAllowed: "4000m"
    controlledValues: RequestsAndLimits
  memory:
    percentile: 99
    overhead: "30"
    minAllowed: "64Mi"
    maxAllowed: "8Gi"
    allowDecrease: false
  updateStrategy:
    type: Recommend
    cooldown: 1h

Reading recommendations from status¶

kubectl get rsp api-services -o jsonpath='{.status.recommendations[*]}' | jq .

Each entry in the array contains:

Field	Description
`workload`	Name of the matched Deployment/StatefulSet/DaemonSet
`containers[].name`	Container name
`containers[].current`	Current CPU/memory requests and limits
`containers[].recommended`	Proposed CPU/memory requests and limits
`containers[].confidence`	Score between 0 and 1
`containers[].dataPoints`	Number of Prometheus samples used

Interpreting confidence scores¶

The confidence score reflects how much data backs the recommendation:

Score	Meaning	Action
0.0 - 0.3	Very low; sparse recent data	Wait for more data
0.3 - 0.6	Moderate; partial coverage	Review manually before acting
0.6 - 0.8	Good; substantial recent coverage	Safe to promote to Canary
0.8 - 1.0	High; near-complete history coverage	Safe to promote to Auto

Tip

Increase historyWindow and minimumDataPoints for workloads with weekly traffic patterns so the estimator captures weekday/weekend variation.

Estimating savings¶

The policy status includes aggregated savings:

kubectl get rsp api-services -o jsonpath='{.status.savings}' | jq .

{
  "cpuRequestReduction": "1200m",
  "memoryRequestReduction": "2Gi"
}

These values represent the total reduction across all matched workloads. Multiply by your per-core and per-GiB cloud pricing to estimate monthly cost savings.

Promoting to an active mode¶

When you are satisfied with the recommendations, change the mode:

Use Canary to resize a subset first.
Use OneShot to resize a single pod per reconciliation cycle.
Use Auto to resize all eligible pods (best for non-critical workloads).

kubectl patch rsp api-services --type merge \
  -p '{"spec":{"updateStrategy":{"type":"Canary","canary":{"percentage":10,"observationPeriod":"30m"}}}}'