engineering-economics - SKILL.md Agent Skill

name: engineering-economics description: Use when estimating infrastructure costs, comparing architectural approaches by cost, evaluating managed service trade-offs, reviewing cloud pricing models, or when a design decision has unclear cost implications or vendor lock-in risk.

Engineering Economics

Overview

Design systems that are economically sustainable. Every architectural decision has a cost dimension -- compute, storage, egress, operational overhead, and lock-in. Make cost a first-class design constraint, not a surprise on the monthly bill.

When to Use

Before choosing between architectural approaches with different cost profiles.
When estimating infrastructure cost of a new feature or service.
When evaluating managed services vs self-hosted alternatives.
When cloud spending is growing without clear justification.
When reviewing egress, storage, or compute-heavy designs.
When vendor lock-in risk needs explicit analysis.

When NOT to Use

Early prototypes where speed matters more than cost optimization.
Changes with negligible infrastructure impact.

Core Competencies

Competency	Key Question
Cloud pricing models	Is cost driven by compute, storage, egress, or API calls?
Egress & storage trade-offs	Can data locality or caching reduce transfer costs?
Operational complexity vs savings	Does the cheaper option require significantly more ops work?
Performance vs infrastructure	Is the performance gain worth the additional infrastructure cost?
Managed service lock-in	Can we migrate away without rewriting? What is the switching cost?
Cost at scale	Does cost grow linearly, superlinearly, or plateau with usage?

Practice Loop

Estimate monthly cost -- before building, calculate expected cost at current and projected load.
Compare approaches -- evaluate at least two architectural options by cost, complexity, and lock-in.
Evaluate operational overhead -- cheaper infrastructure that requires more engineering time may not be cheaper.
Track spending trends -- monitor actual vs estimated cost monthly; investigate divergence.
Document cost assumptions -- record what you assumed about usage patterns and pricing; revisit quarterly.

Failure Signals

Stop and reassess if you observe:

Signal	Root Cause
Hidden cost escalation	Egress, API calls, or log volume growing unmonitored
Overprovisioned infrastructure	Resources sized for peak but running idle most of the time
Complexity without ROI	Custom solution built to save cost but costing more in engineering time
Vendor lock-in without analysis	Proprietary APIs adopted without evaluating switching cost
Cost surprises after launch	No pre-launch cost estimate or monitoring in place
Linear cost with sublinear value	Spending scales with traffic but revenue or value does not

Quick Reference

Cost Analysis Template:

## Feature / Service
What is being built or changed?

## Cost Drivers
- Compute: instance type, scaling model, utilization
- Storage: volume, tier (hot/warm/cold), retention
- Egress: cross-region, cross-cloud, internet-bound
- API calls: pricing per request, batch vs individual
- Managed services: per-unit pricing, minimum commitments

## Monthly Estimate
- At current load: $___
- At 10x load: $___
- Cost growth model: linear / superlinear / capped

## Alternative Approaches
| Approach | Monthly Cost | Ops Overhead | Lock-in Risk |
|----------|-------------|--------------|--------------|
| Option A | $___        | low/med/high | low/med/high |
| Option B | $___        | low/med/high | low/med/high |

## Operational Overhead
- Does this require dedicated engineering time to operate?
- Is there on-call burden? Maintenance burden?
- What breaks if no one touches it for 6 months?

## Lock-in Assessment
- Can we migrate to another provider without rewriting?
- What is the estimated switching cost (time, engineering, risk)?
- Are we using proprietary APIs or open standards?

## Cost Assumptions
- Expected traffic pattern (steady, bursty, seasonal)
- Data retention requirements
- Growth rate projection
- Review date: ___

Common Mistakes

Mistake	Fix
Ignoring egress costs	Map data flow across regions and boundaries; estimate transfer volume
Sizing for peak load permanently	Use autoscaling or scheduled scaling; right-size for typical load
Choosing managed services without exit plan	Evaluate switching cost before adopting; prefer open standards
No cost monitoring after launch	Set budget alerts; review actual vs estimated monthly
Optimizing cost before measuring	Profile actual usage first; optimize the largest cost driver
Treating engineering time as free	Factor in ops burden, on-call cost, and maintenance when comparing options