Pre-Flight Impact Analysis: Cloud Run

Blast radius runs as a pre-flight check on every cloud action surfaced in ZopNight. A 1-hop adjacency graph across AWS, GCP, and Azure lists every directly connected neighbor and classes each as affected, warning, or safe. A 0-100 risk score gates one-click actions on an org-level threshold so high-impact remediations route to an approver instead of running silently. Applied to GCP Cloud Run, it is one of the most reliable ways to take waste out of non-production without touching how the service runs in production.

Serverless container platform bill around the clock, and pre-flight impact analysis is about making sure you only pay for the hours and capacity you actually use. ZopNight Analyze this against your measured usage, the same FinOps discipline that separates it from dashboard-first tools like CloudHealth.

Why pre-flight impact analysis matters for Cloud Run

What pre-flight impact analysis actually buys you:

• 1-hop adjacency graph with six edge types: db_access, routes_to, attached_to, triggers, member_of, cross_region.
• Three impact classes per neighbor: affected (breaks), warning (degrades), safe (unchanged).
• 0-100 risk score combines worst class and neighbor count, configurable threshold per org.
• Blast radius snapshot captured to the activity log at approval time for audit replay.

For Cloud Run specifically, the win comes from the gap between how long the resource runs and how little of that time anyone is using it.

How ZopNight does it

Connect a read-only role, let ZopNight discover your Cloud Run across regions and accounts, and act, scheduling stops and starts on your hours, guided rightsizing for the oversized, idle detection for the forgotten. It ships 490 built-in audit rules across AWS (216), GCP (127), and Azure (147) and 124 of those recommendations are wired to act end to end, 28 one-click and 96 guided. Production is excluded by default and every action is logged.

How ZopNight schedules Cloud Run

The loop that does this is deliberately mechanical, and it starts read-only. You connect AWS, GCP, and Azure with a read-only role, and ZopNight discovers every Cloud Run across your regions and accounts. It records a per-action permission verdict for each one, so you can see where it can list a resource but not yet stop it, and you review that inventory, filter it by status or type, and search for the specific resources you care about before anything is scheduled.

Scheduling itself is a cron you write once in plain terms, stop at 7 PM, start at 8 AM on weekdays, pinned to your timezone so the jobs fire at local business hours rather than UTC. A weekly 24-hour grid shows the schedule visually so you catch gaps and overlaps before you save, and an estimate of active versus inactive hours appears before you commit. Resources attach individually or bundle into groups like “dev-cluster” or “staging-db” so a whole environment follows one cadence.

Actions run in dependency order, so a database comes up before the app server that depends on it. When something needs to stay up, an override forces a Cloud Run ON or OFF for a defined window, carries a reason so teammates understand why it exists, and expires automatically so nothing is left running by accident. If a start or stop fails, ZopNight retries up to three times and falls back to a dead-letter queue rather than silently dropping the action, and every state change lands in an audit trail that records whether a schedule, an override, or a specific user triggered it.

Getting started

Getting started is intentionally low-stakes:

• Connect AWS, GCP, and Azure with a read-only role. Nothing is scheduled or changed at this stage.
• Let ZopNight discover your Cloud Run and review exactly what it found, filtered by account, region, and status.
• Create a schedule in your timezone and attach the non-production resources or groups you want it to cover.
• Watch the first cycle run, with Slack, Teams, or Google Chat notifications on every start, stop, and failure, then layer in idle cleanup and guided rightsizing.

Production stays excluded by default throughout, and because discovery and recommendations are read-only, you can prove the value before you enable a single action.