Model technical capacity, unit cost, and budget constraints connected to demand, performance, and reliability decisions. Use when projecting capacity from growth forecasts, sizing for peak events, designing cost-aware scaling policies, defining budget thresholds or quota/rate-limit enforcement, running or planning load/soak tests as capacity evidence, resolving SLO-cost tradeoffs, or modeling multi-tenant demand distributions, hot-tenant skew, pooled or siloed headroom, fairness evidence, and tenant-variable unit cost. Do NOT use for financial P&L statements, fundraising scenarios, or SaaS metrics (route to financial-modeling); for infrastructure implementation or cloud-resource provisioning (route to platform-engineering); or for generic cloud-cost tips and universal utilization targets — this skill does not prescribe fixed savings rates or one-size-fits-all thresholds.
Model technical capacity, unit cost, and budget constraints connected to demand, performance, and reliability decisions. Use when projecting capacity from growth forecasts, sizing for peak events, designing cost-aware scaling policies, defining budget thresholds or quota/rate-limit enforcement, running or planning load/soak tests as capacity evidence, resolving SLO-cost tradeoffs, or modeling multi-tenant demand distributions, hot-tenant skew, pooled or siloed headroom, fairness evidence, and tenant-variable unit cost. Do NOT use for financial P&L statements, fundraising scenarios, or SaaS metrics (route to financial-modeling); for infrastructure implementation or cloud-resource provisioning (route to platform-engineering); or for generic cloud-cost tips and universal utilization targets — this skill does not prescribe fixed savings rates or one-size-fits-all thresholds.
license
MIT
compatibility
Platform-agnostic methodology. No runtime dependency.
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 launch decisions and constrains or supports SLO choices.
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:
Dimension
Capacity impact
Cost impact
Demand (traffic, growth rate)
Drives compute, storage, network requirements
Drives baseline and projected spend
Performance (latency, throughput target)
Constrains resource headroom per request
Tighter targets increase unit cost
Reliability (SLO, error budget)
Requires redundancy, over-provisioning, or isolation
Higher SLOs increase cost non-linearly
Spend (budget, cost constraint)
Caps capacity; may force degraded-mode operation
Limits what SLO/performance targets are achievable
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:
Trigger
What to load
Project capacity from a growth forecast
SKILL.md + templates/capacity-model.md
Size capacity for a peak event (launch, Black Friday, seasonal)
Understand ownership boundaries with adjacent skills
SKILL.md + references/discovery-brief.md
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:
Level
Mechanism
Operational consequence
Alert
Spending threshold notification
No automated action; triggers review
Soft cap
Throttling, degraded mode, reduced provisioning
Service continues at reduced capacity
Hard cap
Rate limiting, quota enforcement, resource denial
Requests above cap are rejected
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:
Project demand forward using the growth rate, with confidence intervals.
Model capacity at current utilization targets; identify the resource that saturates first.
Calculate the cost of incremental capacity at each scaling step.
Define the scaling trigger — the utilization threshold at which provisioning must begin (lead time matters).
Record assumptions (growth rate stability, no step-change events, current utilization pattern holds) and evidence sources.
Peak scenario
A known event will drive traffic well above baseline (product launch, Black Friday, seasonal peak). The question: how much capacity is needed for the peak, what does it cost, and is the cost justified?
Guidance:
Model peak demand separately from baseline — peak shape (height, duration, ramp), not just the peak number.
Size capacity for the peak, not the average. Include headroom.
Run a load test at the projected peak throughput before the event. A model without load evidence is insufficient.
Define the post-peak scale-down plan and its trigger — capacity that persists after the peak incurs unnecessary cost.
If peak capacity cost exceeds budget, model a degraded peak alternative (which functions shed, what users experience).
Degraded scenario
A dependency fails or a resource constraint forces operation below full capacity. The question: what does the system look like in degraded mode, what capacity is needed for the core path, and what does degraded operation cost?
Guidance:
Tier functions: core (must preserve) vs enhancing (can shed). Route degradation-path design to resilience-and-recovery.
Model capacity for core-path-only operation — what resources are freed by shedding enhancing functions.
Calculate the cost of degraded operation (may be lower than full operation, or higher if failover resources activate).
Define the maximum degraded-operation window — how long degraded mode can persist before escalation.
Ensure the degraded capacity model is exercised in a game day or chaos test (route to resilience-and-recovery for exercise design).
Cost-constrained scenario
A budget constraint prevents provisioning to the ideal capacity or SLO target. The question: what is the best achievable reliability and performance within the budget, and who decides?
Guidance:
Start from the budget constraint — state the cap explicitly (monthly, quarterly, annual).
Model the capacity that the budget can purchase at current unit costs.
Calculate the SLO and performance targets that capacity can support.
Compare to the unconstrained ideal: what SLO, latency, and throughput are being traded away.
Produce an SLO-cost tradeoff record with an accountable owner. Cost optimization must not degrade reliability, privacy, or user outcomes — if the budget cannot support an acceptable SLO, the decision is escalated, not silently accepted.
Cost optimization must not justify degrading reliability, privacy, or user outcomes. If a cost constraint forces a choice between budget and these non-negotiables, the tradeoff is escalated to an accountable owner with the evidence — it is never silently accepted as an optimization.
When not to use
This skill does not provide generic cloud-cost tips (reserved instances, spot instances, "turn off unused resources," "right-size," savings plans). Those are platform-specific implementation tactics that belong in platform-engineering references or cloud-provider documentation, not in a capacity-and-cost methodology skill. This skill owns the decision framework and evidence standard for capacity and cost — not a list of cost-cutting tips.
This skill does not prescribe universal utilization targets. A utilization target of 70% for a latency-sensitive service with spiky traffic is not the same as 85% for a batch-processing pipeline with predictable load. Every utilization target must be stated with context, rationale, and the evidence that supports it. "Target 70% utilization" without context is not a capacity decision — it is a guess.
This skill does not permit cost optimization to justify degrading reliability, privacy, or user outcomes. These are non-negotiable constraints. A cost-constrained scenario that would violate them must be escalated, not optimized around.
