All case studies
Case study 09Distributed SystemsAutomation

Cloud Cost & Release Engineering

Autoscaling and query tuning that cut cloud costs 18%, plus release checks for consistent deploys.

Role
Senior engineer · cloud cost and releases
Core stack
AWSAutoscalingPostgreSQLQuery tuningKafka
−18%
Cloud infrastructure cost via autoscaling and query tuning
99.95%
Availability held for core services during the changes
1 process
Shared release checks and reviews across the team

Overview

A cost and reliability program for the modernized commerce platform: autoscaling matched to real demand, faster database queries, and one shared release process for the engineering team.

Services were provisioned for peak load around the clock, and a handful of heavy queries drove database size and cost. Releases and incident response also depended on who was on shift.

Product walkthrough

The flow, screen by screen

Cost by service
01 / 03

Cost by service. Monthly spend by service with the autoscaling and query-tuning changes marked; spend fell 18% while core availability held at 99.95%.

ArchitectureCorePlatformAsync / control
usagedeployUsage metricsCPU · queue lagCost reportsby service · environmentTrafficstorefront · APIsLoad balancerServicestuned autoscalingPostgreSQLindexes · smaller tierCI/CDbuild · testRelease gatechecks · rollback plan
Traffic reaches autoscaled services backed by a tuned PostgreSQL database; usage metrics feed cost reports, and every release passes the same gate.

Approach

  1. 01Broke cloud cost down by service and environment to find idle capacity and the most expensive workloads.
  2. 02Tuned autoscaling policies on real traffic patterns, so services scale in off-peak and scale out ahead of demand.
  3. 03Optimized the heaviest database queries with composite and covering indexes and rewritten access patterns, which allowed a smaller instance class.
  4. 04Standardized release checks (reviewed migrations, linked dashboards, a rollback plan, an assigned on-call engineer) across a six-person team.
  5. 05Ran consistent technical reviews and incident follow-ups so lessons turned into checklist items.

Key decisions

Measure before cutting

Cost per service showed where savings were safe and where capacity was protecting availability.

Fix queries before buying hardware

Query tuning reduced database load enough to move down an instance class instead of up.

Checklists over heroics

A shared release gate made every deployment look the same, no matter who shipped it.

Outcomes

  • Lower cost without trading away availability.
  • More consistent deployments and incident response.
  • AWS
  • Autoscaling
  • PostgreSQL
  • Query tuning
  • Kafka
  • Docker
  • CI/CD

Working on something similar?

I work with clients worldwide, across any time zone. Tell me what you're building.

Send a message Email meMore case studies