| name | calculate-capacity-plan |
| description | Use when provisioning infrastructure for a new system, preparing for anticipated traffic growth, or ensuring headroom before a major product launch |
| source | Google SRE Workbook capacity planning chapter; Allspaw "The Art of Capacity Planning" (O'Reilly 2008); NIST cloud scalability guidelines |
| tags | ["performance","capacity","infrastructure","reliability"] |
| verified | true |
Calculate Capacity Plan
Model current resource utilization and traffic growth to determine when and how much additional infrastructure to provision to maintain SLO compliance.
Why This Is Best Practice
Adopted by: Google SRE (capacity planning is a core SRE function), Netflix (capacity headroom model), AWS (Auto Scaling Group sizing methodology)
Impact: Allspaw (2008): organizations without capacity planning experience 2-3 avoidable capacity-related outages per year; over-provisioning without capacity plans wastes 30-40% of cloud spend; under-provisioning causes SLO violations
Why best: Infrastructure takes time to provision; without a forward-looking model, you react to capacity issues after they cause outages rather than before
Sources: Allspaw "The Art of Capacity Planning" O'Reilly (2008); Murphy et al. "Site Reliability Workbook" O'Reilly (2018) Ch. 17; Google SRE capacity planning practices
Steps
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Measure current resource utilization — Collect 90-day p50 and p95 utilization for each resource dimension: CPU, memory, disk I/O, network bandwidth, database connections, and cache hit rate. Use percentiles, not averages — averages mask peaks. P95 utilization is your effective utilization baseline.
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Identify the bottleneck resource — From your utilization data, identify which resource reaches saturation first as traffic increases. Common bottlenecks: CPU (compute-bound services), database connections (connection pool exhaustion), memory (high-memory workloads), I/O (storage-bound services). All capacity modeling focuses on the bottleneck resource.
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Establish a traffic growth model — Analyze 90-day traffic trends (requests per second, active users, data volume). Fit a growth model: linear (constant traffic addition), exponential (percentage growth per period), or step-function (event-driven spikes). Get business context: planned marketing campaigns, product launches, seasonal peaks. Your growth model drives the capacity timeline.
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Calculate resource-to-traffic ratio — Determine: at current traffic, what resource utilization exists? Example: 1000 RPS → 40% CPU on 8 vCPUs. Compute the ratio: 1000 RPS / 40% = 2500 RPS before CPU saturation. Apply a safety buffer: plan capacity for 70% max utilization (30% headroom for traffic spikes and failover).
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Project when capacity will be exhausted — Apply the growth model to the safe capacity threshold. Example: 2500 RPS safe limit, current 1000 RPS, growing 15% per month. Months to exhaustion = log(2500/1000) / log(1.15) ≈ 6.5 months. Subtract your infrastructure provisioning lead time (cloud: 0 days for on-demand, 2-4 weeks for reserved procurement).
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Model for N+1 and multi-region redundancy — Capacity must support N+1 node failure. If you have 3 web servers and one fails, the remaining 2 must handle 100% of traffic. Plan capacity at N+1 utilization: 2/3 of normal utilization per server (67%), so 3 servers run at 67%, not 90%. Multi-region: each region must handle full traffic load if the peer region fails.
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Account for efficiency initiatives — If planned optimizations will reduce resource consumption (caching, query optimization, algorithm improvement), model their impact. Example: adding read replicas expected to reduce primary DB load by 40%. Apply these reductions conservatively (50% of the planned gain) to account for implementation delays.
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Calculate the procurement/provisioning timeline — Determine lead times: on-demand cloud instances (minutes), reserved instances (1-3 days to activate), physical hardware (6-12 weeks), network capacity increases (2-4 weeks for ISP circuits). The capacity plan output is: "We need to provision X resources by date Y or we will breach our SLO."
Rules
- Never plan for > 70% utilization on any bottleneck resource; 30% headroom is the minimum for safe operations (handles 1.4x traffic spikes without degradation).
- Always include N+1 redundancy in capacity calculations; single-server headroom calculations underestimate real requirements by 50%+ for multi-node services.
- Capacity plans must include the provisioning timeline as an output; "we'll need more capacity" without "by this date" is not actionable.
- Model for the business scenario with the highest traffic impact (major launch, seasonal peak) not the average scenario.
Common Mistakes
- Using average utilization — averages hide peaks; p95 utilization is the correct baseline for capacity decisions.
- No growth model — "current capacity is sufficient" without a growth timeline misses the window to provision before SLO breach.
- Forgetting stateful resource limits — database connection limits, in-memory cache size, and file descriptor limits are hard caps that don't auto-scale; model them explicitly.
- Not accounting for failover — a system with 95% CPU utilization on three nodes has no capacity to absorb a node failure; always plan for N+1.
When NOT to Use
- Serverless-first architectures where capacity is managed by the provider and auto-scales without provisioning decisions (though cost planning is still required)
- Prototype systems with no SLO and no production traffic
- Systems with unpredictable traffic spikes where horizontal auto-scaling is the capacity strategy (still need to set auto-scaling bounds correctly)
