# Cost Analysis > Use when modeling infrastructure costs, projecting scaling expenses, or identifying optimization opportunities across cloud providers and third-party services. Covers per-unit cost estimation, growth milestone projections, and budget alerting setup. Do not use for deployment strategy design (use deployment-plan) or monitoring architecture (use observability-design). - Skill: `tomevault-io/cost-analysis` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/cost-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/cost-analysis/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/cost-analysis --- # Cost Analysis ## Purpose Model infrastructure costs at current and projected scale, identify optimization opportunities, and establish cost monitoring with budget alerting. Produces a cost breakdown that enables informed architecture and scaling decisions. ## Scope Constraints Reads infrastructure inventories, pricing documentation, and usage metrics for cost analysis. Does not modify files, provision resources, or access billing APIs or financial systems directly. ## Inputs - Current infrastructure inventory (services, providers, tiers) - Current usage metrics (requests/day, storage volume, compute hours) - Growth projections or scaling targets - Budget constraints or cost reduction goals ## Input Sanitization No user-provided values are used in commands or file paths. All inputs are treated as read-only analysis targets. ## Procedure ### Progress Checklist - [ ] Step 1: Inventory infrastructure components - [ ] Step 2: Estimate per-unit costs at current scale - [ ] Step 3: Model cost projections at scale - [ ] Step 4: Identify optimization opportunities - [ ] Step 5: Design cost monitoring and alerting - [ ] Step 6: Plan budget allocation and review cadence ### Step 1: Inventory Infrastructure Components Catalog all cost-bearing components: - **Compute**: Application servers, serverless functions, background workers, build runners - **Storage**: Object storage, block storage, database storage, backup storage - **Database**: Managed database instances, read replicas, connection poolers - **CDN**: Bandwidth, edge compute, cache storage - **Third-party services**: Auth providers, email/SMS, payment processing, analytics, error tracking - **DNS and networking**: Domain registration, DNS queries, load balancers, NAT gateways, data transfer - **Email**: Transactional email, marketing email, inbound processing ### Step 2: Estimate Per-Unit Costs at Current Scale For each component, calculate: - **Monthly base cost**: Fixed costs regardless of usage (reserved instances, minimum tiers) - **Variable cost**: Per-request, per-GB, per-user marginal costs - **Cost per user**: Total infrastructure cost divided by active users - **Cost per request**: Total infrastructure cost divided by total requests - Document pricing tier thresholds and current utilization against limits ### Step 3: Model Cost Projections at Scale Project costs at growth milestones: - **2x scale**: Which components scale linearly vs step-function? Where do tier upgrades hit? - **5x scale**: Which pricing tiers break? Where do volume discounts apply? - **10x scale**: What architectural changes become necessary? Which components become dominant costs? - Identify cost cliffs — points where a small usage increase triggers a large cost jump ### Step 4: Identify Optimization Opportunities Evaluate cost reduction strategies: - **Right-sizing**: Over-provisioned instances, unused reserved capacity, oversized database tiers - **Reserved/committed use**: Savings from 1-year or 3-year commitments on stable workloads - **Spot/preemptible instances**: Suitable workloads for interruptible compute (batch jobs, builds) - **Caching to reduce compute**: CDN caching, application-level caching, database query caching - **Query optimization**: Slow queries consuming excess database resources, missing indexes - **Architecture changes**: Serverless for bursty workloads, edge compute for latency, static generation ### Step 5: Design Cost Monitoring and Alerting Establish ongoing cost visibility: - **Budget thresholds**: Alert at 50%, 75%, 90%, 100% of monthly budget - **Anomaly detection**: Unexpected cost spikes from runaway processes, misconfigured auto-scaling, or attacks - **Cost-per-user trending**: Track unit economics over time to catch efficiency degradation - **Tag-based allocation**: Cost attribution by service, team, environment, feature - **Review dashboard**: Real-time cost breakdown accessible to engineering and leadership ### Step 6: Plan Budget Allocation and Review Cadence Define the financial process: - **Budget allocation**: Per-service or per-team budget breakdown - **Review cadence**: Monthly cost review meetings, quarterly budget adjustments - **Cost ownership**: Which team owns which infrastructure costs - **Approval process**: Threshold for new infrastructure spending requiring approval - **Cost-benefit framework**: How to evaluate infrastructure investments against engineering time > **Compaction resilience**: If context was lost during a long session, re-read the Inputs section to reconstruct what system is being analyzed, check the Progress Checklist for completed steps, then resume from the earliest incomplete step. ## Output Format ```markdown # Cost Analysis: [Project/Service Name] ## Infrastructure Cost Table | Component | Provider | Tier | Monthly Cost | Cost Driver | Notes | |-----------|----------|------|-------------|-------------|-------| | App Server | [provider] | [tier] | $X | requests | ... | | Database | [provider] | [tier] | $X | storage + queries | ... | | CDN | [provider] | [tier] | $X | bandwidth | ... | | **Total** | | | **$X** | | | **Cost per user**: $X/month | **Cost per 1K requests**: $X ## Scaling Projections | Component | Current | 2x | 5x | 10x | |-----------|---------|-----|-----|------| | Compute | $X | $X | $X | $X | | Database | $X | $X | $X | $X | | Storage | $X | $X | $X | $X | | **Total** | **$X** | **$X** | **$X** | **$X** | ## Optimization Recommendations | Optimization | Estimated Savings | Effort | Risk | Priority | |-------------|-------------------|--------|------|----------| | [description] | $X/month (Y%) | Low/Med/High | Low/Med/High | P1/P2/P3 | ## Budget Alert Thresholds | Threshold | Monthly Amount | Action | |-----------|---------------|--------| | 50% | $X | Review dashboard | | 75% | $X | Investigate anomalies | | 90% | $X | Escalate to lead | | 100% | $X | Freeze non-critical spending | ``` ## Handoff - Hand off to deployment-plan if cost optimization requires changes to deployment strategy (e.g., switching from blue-green to rolling to reduce compute overhead). - Hand off to observability-design if cost monitoring needs integration with existing alerting and dashboard infrastructure. ## Quality Checks - [ ] All cost-bearing infrastructure components are inventoried - [ ] Per-unit costs (per user, per request) are calculated for current scale - [ ] Scaling projections identify cost cliffs and tier boundaries - [ ] Optimization recommendations include estimated savings and effort - [ ] Cost monitoring covers budget alerts and anomaly detection - [ ] Budget review cadence and cost ownership are defined - [ ] Third-party service costs are included (not just cloud infrastructure) - [ ] Cost projections account for both linear and step-function scaling ## Evolution Notes --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/dtsong) — claim your Tome and manage your conversions.