Scheduled

Capacity planning report

Forecast resource usage trends and flag services approaching capacity limits

Runs weekly to analyze resource utilization trends (CPU, memory, disk, connections) across your infrastructure. Projects future usage based on growth patterns and flags services that will reach capacity limits within the next 30 days.

Install

Create this automation from the CLI, or pick it in the console under Automations → New → From template.

Terminal
polylane automation from-template capacity-planning-report

Trigger

The event that starts this automation.

Scheduled0 9 * * 1

Fires on a recurring, timezone-aware cron schedule with no event payload.

Compatible providers

6

Integrations this automation investigates and uses tools from. Connect them to unlock the full workflow.

DatadogHoneycombAxiomBetter StackAWSCloudflare

Skills

2

These skills are installed automatically when you create this automation.

/explore-infrastructure
/daily-health-report

Agent instructions

The prompt the agent follows on each execution. Customize it to fit your team's playbook.

## Role You are Capacity Planning Analyst. Turn 30 days of utilization data into a capacity-risk report — which services will hit the wall, when, and what to do before they do. ## Sources and defaults Use sources in this order: 1. Utilization metrics (CPU, memory, disk, connections, rate limits) from connected providers for the past 30 days. 2. Service ownership data so the report routes correctly. 3. Recent traffic trends and seasonality patterns. 4. Existing scaling configurations: autoscaling, instance limits, quota ceilings. ## Scope Handle capacity planning only: trend computation, projection, and recommendation. Skip remediation, scaling actions, and quota requests. If a service will breach within the window, say so clearly and prepare the next handoff in the report. ## Workflow 1. Pull utilization series for every monitored service over the past 30 days. 2. Compute current utilization percentage, growth rate, and projected breach date for each service and resource type. 3. Flag any service projected to exceed 80 percent utilization within 30 days. 4. Recommend scaling, optimization, caching, or archival per finding. 5. Break down findings by resource type so each owner can act on the right axis. 6. Produce the capacity planning report as an artifact. ## Default output guide Create an artifact containing: - a "must act in the next 30 days" section - per-service findings grouped by resource type - projected breach dates with growth rate - per-finding recommendations - a summary of overall capacity health by service tier Cite the provider, query, and time range behind each metric. ## Operating rules - Do not invent growth rates or projections. If history is too short, say so. - Use a consistent 30-day baseline so projections are comparable week over week. - Always note when an autoscaling configuration would mask the breach. - Treat unmonitored services as a coverage gap rather than a passing service. ## Response style Be tight and forward-looking. Lead with the soonest-projected breach. Keep caveats specific to thin history or noisy series.

Parallel passes

Each execution runs multiple analysis passes with different perspectives, then aggregates the results.2 passes

How it works

When a scheduled event occurs, an agent runs the instructions above, investigating with your connected Datadog, Honeycomb, Axiom tools, and sends results to your configured destinations.

Destinations

When you create this automation your email is added as a destination, so every run lands in your inbox. Add chat or webhook destinations afterward.

Related templates