Recommendations

ZopNight analyzes your cloud resources and generates cost optimization recommendations based on usage patterns, idle detection, and best practices.

How Recommendations Work

The recommender service subscribes to resource discovery events via Redis Streams. When new or updated resources are discovered, it runs audit rules across all three cloud providers to generate actionable recommendations.

Recommendation Categories

Severity Levels

List Resource Summaries

{
"data": {
  "items": [
    {
      "resourceUid": "i-0abc123def456",
      "resourceName": "idle-dev-server",
      "resourceType": "ec2",
      "provider": "aws",
      "cloudAccountId": "123456789012",
      "cloudAccountName": "AWS Prod",
      "recommendationCount": 3,
      "openCount": 2,
      "totalSavingsUsd": 52.56
    }
  ],
  "total": 45,
  "page": 1,
  "limit": 10,
  "hasMore": true
}
}

List Rule Summaries

{
"data": {
  "items": [
    {
      "ruleId": "RC-212",
      "title": "Azure managed disk is unattached — verify and delete if not needed",
      "category": "orphan",
      "severity": "low",
      "resourceCount": 36,
      "openCount": 35,
      "totalSavingsUsd": 5.00
    }
  ],
  "total": 8,
  "page": 1,
  "limit": 10,
  "hasMore": false
}
}

List Recommendations

{
"data": {
  "items": [
    {
      "id": "rec_001",
      "resourceUid": "i-0abc123def456",
      "resourceName": "idle-dev-server",
      "resourceType": "ec2",
      "ruleId": "RC-001",
      "title": "Stop idle instance i-0abc123def456",
      "titleParams": { "from": "i-0abc123def456", "to": "" },
      "description": "This instance has had less than 5% CPU utilization over the past 14 days.",
      "currentCostUsd": 52.56,
      "optimizedCostUsd": 0.00,
      "savingsUsd": 52.56,
      "status": "open",
      "severity": "high",
      "category": "idle",
      "remediation": "Consider stopping or terminating this instance if it is not needed.",
      "consoleUrl": "https://console.aws.amazon.com/ec2/home?region=us-east-1#Instances:instanceId=i-0abc123def456",
      "provider": "aws",
      "cloudAccountId": "123456789012",
      "cloudAccountName": "AWS Prod",
      "actionType": "stop",
      "evidence": {
        "metric": "CPUUtilization",
        "windowDays": 14,
        "p95": 3.1
      },
      "generatedAt": "2025-01-20T08:00:00Z"
    }
  ],
  "total": 36,
  "page": 1,
  "limit": 5,
  "hasMore": true
}
}

Recommendation Title

title is the complete, human-readable sentence for the recommendation, and it is composed entirely server-side. It leads with the action — "Resize db.r5.4xlarge → db.r5.2xlarge", "Delete unattached 512 GB disk" — so the finding is readable without opening anything. Render it as-is: it is the single source of the wording, and every client that re-derives a verb from category or actionType will drift from it.

titleParams carries the spec tokens that already appear verbatim inside title, so a client can style them (monospace, click-to-copy, a highlighted target) without re-composing the sentence:

Rules for consumers:

• Find the tokens inside title, don't assemble a sentence from them. Locate each token as a substring of title and style that span. Never splice a token into wording that does not already contain it, and never compose your own sentence out of from / to — that is how a second, drifting copy of the wording gets created.
• titleParams is optional and is frequently absent. It is derived from the recommendation's stored resource snapshot, which was added on 2026-06-18 with no backfill. Every recommendation generated before that date has no snapshot and therefore no titleParams — a large share of an established backlog. Fall back to rendering title plainly; there is nothing missing from the sentence itself.
• A token may be present in titleParams but absent from the wording for a given rule. Treat that as "no chip for that token", not an error.

Recommendation Summary

{
"data": {
  "totalOpen": 23,
  "totalOpenCost": 19,
  "totalSavings": 1250.80,
  "criticalCount": 2,
  "highCount": 8,
  "resourceCount": 18,
  "awsResources": 10,
  "gcpResources": 5,
  "azureResources": 3,
  "appliedCount": 12,
  "dismissedCount": 5,
  "optimisedCount": 3
}
}

Resource Recommendations (Deprecated)

Update Recommendation

curl -X PATCH https://zopnight.com/api/recommendations/rec_001 \
-H "Authorization: Bearer <token>" \
-H "Content-Type: application/json" \
-d '{ "status": "applied" }'

Filter Fields

{
"data": {
  "statuses": ["open", "applied", "dismissed", "optimised"],
  "severities": ["critical", "high", "medium", "low", "info"],
  "categories": ["idle", "rightsizing", "schedule", "orphan", "compliance", "discount", "governance"],
  "providers": ["aws", "gcp", "azure"]
}
}

Provider Breakdown

{
"data": [
  { "provider": "aws", "totalOpen": 12, "totalSavings": 820.50, "criticalCount": 1, "highCount": 5 },
  { "provider": "gcp", "totalOpen": 6, "totalSavings": 310.00, "criticalCount": 0, "highCount": 2 },
  { "provider": "azure", "totalOpen": 5, "totalSavings": 120.30, "criticalCount": 1, "highCount": 1 }
]
}

Recommendation Detail

Fields returned only by the detail endpoint

The list endpoints stay deliberately lean. The fields below are resolved per recommendation at read time and are returned only by the detail endpoint — they are not merely empty in a list response, they are absent from it. Build your UI so each one is optional, and do not show a placeholder value for one that has not been fetched.

Refresh Recommendations

Auto-Remediation Workflows

For auto-remediable rules (rightsizing, resize, oversized) a recommendation can be applied through a multi-step workflow that the provisioner executes against the cloud provider. Workflows are previewed, started, and approved through the endpoints below. See Provisioning for the apply-side detail.

Smart Tags

ZopNight derives virtual tags from tagging policies you define and stores them per resource (pending until you accept). They power tag-based cost attribution and are never written back to the cloud. The full API — GET/PATCH /smart-tags plus the tagging-policy catalog and validation endpoints — lives on its own page: Smart Tags.

The recommender runs 337+ audit rules across AWS (155), GCP (75), and Azure (107), including six autoscaler rules (RC-ASC-001..006) that feed into VM Autoscaling. See Cloud Support Matrix for provider-specific details.