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.

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

Process

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

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 |

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