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Cost Engineering at Scale

Practical techniques for forecasting, attribution, and operating sustainably at high scale across clouds.

Simple Explanation​

What it is​

Cost engineering is the discipline of designing systems that scale without runaway spend.

Why we need it​

At high traffic volumes, tiny inefficiencies become large bills. You need cost awareness built into the architecture.

Benefits​

  • Predictable budgets as usage grows.
  • Higher margins for products and teams.
  • Better decision-making between speed and price.

Tradeoffs​

  • Requires measurement and cost attribution.
  • Optimization can add complexity if done too early.

Real-world examples (architecture only)​

  • Right-size memory -> Lower compute cost.
  • Cache responses -> Fewer database reads.

Cost optimization loop

What This Lesson Covers​

  • Cost attribution and tagging strategy
  • Unit economics for serverless workloads
  • Budgeting, alerts, and guardrails
  • Workload shaping and data lifecycle
  • Optimization playbook and review cadence

Cost Engineering Mindset​

  1. Measure

    • Track $ per request and $ per user action
    • Break costs by service, team, and environment

  2. Attribute

    • Enforce tags for owner and product
    • Use consistent naming for resources

  3. Optimize

    • Reduce waste (idle resources, over-provisioning)
    • Improve efficiency (batching, caching, compression)

  4. Forecast

    • Model cost per feature and growth rate
    • Plan for peak and burst usage

Python Example: Cost per Request Estimator​

def estimate_lambda_cost(memory_mb, duration_ms, requests, price_per_gb_second, price_per_request):
	 memory_gb = memory_mb / 1024
	 duration_seconds = duration_ms / 1000
	 compute_cost = memory_gb * duration_seconds * price_per_gb_second * requests
	 request_cost = price_per_request * requests
	 return compute_cost + request_cost


estimate = estimate_lambda_cost(
	 memory_mb=512,
	 duration_ms=120,
	 requests=1_000_000,
	 price_per_gb_second=0.0000166667,
	 price_per_request=0.0000002,
)

print(f"Estimated cost: ${estimate:.2f}")

Optimization Playbook (Examples)​

  • Reduce duration: faster I/O, smaller payloads
  • Right-size memory: find the sweet spot for CPU and RAM
  • Batch requests: process many events per invocation
  • Cache aggressively: reduce database calls
  • Delete old data: archive cold data to cheaper storage

Project​

Create a cost engineering plan for your busiest workload.

Deliverables:

  • Baseline cost per request
  • Three optimizations with expected savings
  • Alert thresholds for cost spikes

Email your work to [email protected].

References​