Capacity And Cost Engineering

Capacity and Cost Engineering

Connect demand, performance, reliability, and spend into defensible capacity and cost decisions. This skill models technical capacity, calculates unit cost at the infrastructure level, defines budget and quota controls, requires load/soak evidence for capacity claims, and makes cost-performance tradeoffs explicit — producing evidence that feeds production-readiness launch decisions and constrains or supports site-reliability-engineering SLO choices.

Connected dimensions

Demand (traffic and growth), performance (latency and throughput), reliability (SLOs and error budgets), and spend (cost) are treated as connected dimensions — a change in any one dimension affects the others. The skill's core method is tracing the connection:

A capacity decision that changes one dimension without modeling the others is incomplete. Every capacity model, unit-cost calculation, and budget decision in this skill must name at least one connection to another dimension with evidence or an explicit assumption.

Loading Guide

Load this skill when the task involves any of:

Working method

1. Model demand and capacity

Start with the demand signal: current traffic, growth rate, and any known peak events. Translate demand into capacity requirements using a capacity model that connects:

• Demand units (requests/second, concurrent users, GB ingested, messages/second) to
• Capacity units (vCPUs, memory GB, storage GB, IOPS, network throughput, provisioned throughput units) through
• Utilization targets (maximum sustainable utilization per resource, stated with rationale — never a universal percentage without context).

A capacity model is incomplete without a stated utilization target, the evidence for that target (why 70% and not 85%?), and the scaling trigger that fires when utilization approaches the target.

Use the capacity model template (templates/capacity-model.md) which captures demand assumptions, capacity-unit mapping, utilization targets with rationale, scaling triggers, and a projection over the relevant horizon. The template requires fields for assumptions, evidence source, ownership, and tradeoffs.

2. Calculate unit cost

Unit cost is the cost of serving one unit of demand — cost per request, cost per user per month, cost per GB stored, cost per provisioned capacity unit. Unit cost connects infrastructure spend to product and reliability decisions.

Calculate unit cost as:

unit cost = total cost of capacity / number of demand units served

Both numerator and denominator must be measured over the same period, with the same scope (service, team, or platform), and with the same allocation method stated (direct resource cost, attributed shared cost, or fully loaded cost including overhead).

The unit-economics record template (templates/unit-economics-record.md) requires: the unit definition, the cost numerator with allocation method, the demand denominator with measurement source, the resulting unit cost, a cost-per-SLO comparison (what happens to unit cost at 99.9% vs 99.99%?), and an assumptions/evidence/ownership/tradeoffs section. The template includes a structured field for the unit-cost calculation formula.

For a multi-tenant service, do not use a fleet average as the only unit. Load references/multi-tenant-capacity-and-unit-cost.md and distinguish platform baseline cost from tenant-variable cost, then report a distribution of tenant costs or resource consumption. A tenant's variable unit cost may depend on request mix, storage, background work, burst shape, placement, and tier entitlements. Shared-cost allocation is an explicit modeling choice, not a claim that every tenant consumes an equal share.

3. Define budget and quota controls

Budget controls are spending limits with operational consequences — spending alerts at thresholds, hard caps that prevent further spend, and the operational behavior when a cap is hit (degrade, throttle, or stop). Quota and rate-limit enforcement are the mechanisms that implement budget controls at the request or resource level.

Budget controls operate at three levels:

Budget controls must specify what happens at each threshold — the operational behavior, the user-facing impact, and the owner accountable for responding. A budget threshold without a defined operational consequence is incomplete.

The budget/quota decision template (templates/budget-quota-decision.md) captures: budget owner, period, thresholds (alert/soft/hard), quota or rate-limit configuration, enforcement mechanism, operational behavior at each threshold, cost attribution method, anomaly detection triggers, and approval record.

4. Require load/soak evidence

Load and soak test evidence is required for capacity decisions. A capacity model alone — without observed system behavior under representative load — is insufficient evidence for a capacity claim or a scaling policy.

• A load test exercises the system at a target throughput (e.g., expected peak + 20% headroom) for a defined duration and measures latency, error rate, and resource utilization.
• A soak test exercises the system at a sustained load over an extended period (hours to days) and detects slow leaks (memory, file descriptors, connection pools, disk growth) that a short load test misses.

The load/soak test plan template (templates/load-soak-test-plan.md) captures: test objective, target throughput with rationale, duration, environment (must be representative — a dev-environment test is not sufficient), success criteria (latency percentiles, error rate, resource utilization), data collection plan, and the evidence record. The template distinguishes a component-level benchmark from an end-to-end test; a capacity decision must state which boundary was exercised.

Modeling without test evidence, or testing without a model, is incomplete. Both are required.

For multi-tenant claims, the evidence must exercise representative tenant profiles together, including ordinary tenants, high-demand tenants, bursty tenants, and relevant tier or placement variants. A single-tenant benchmark or fleet-average test cannot establish protection against hot tenants, partition skew, or fairness behavior.

5. Resolve SLO-cost tradeoffs

An SLO-cost tradeoff arises when the cost of meeting an SLO at projected demand exceeds the budget, or when a budget constraint forces a lower SLO than the team would otherwise target. This is a structured decision, not an implicit acceptance.

The SLO-cost tradeoff record template (templates/slo-cost-tradeoff-record.md) captures: the SLO under discussion, current cost to meet it, projected cost at demand forecast, alternative SLO with cost comparison, degradation path if the lower SLO is chosen, error budget impact, accountable owner, and approval record. The template requires surfacing the tradeoff with evidence (cost projection, load-test data) and ownership (who decides and who is accountable).

Scenarios

Growth scenario

Demand is growing predictably (e.g., 15% month-over-month). The question: when does current capacity become insufficient, and what does it cost to stay ahead of growth?

Guidance:

1. Project demand forward using the growth rate, with confidence intervals.
2. Model capacity at current utilization targets; identify the resource that saturates first.
3. Calculate the cost of incremental capacity at each scaling step.
4. Define the scaling trigger — the utilization threshold at which provisioning must begin (lead time matters).
5. Record assumptions (growth rate stability, no step-change events, current utilization pattern holds) and evidence sources.