Infrastructure

How We Cut Infrastructure
Costs by 40% in 90 Days

🏢 Mid-market SaaS (200 employees) ⏱ 90-day engagement 📅 March 2026
40% Cost reduction
3x Deployment speed
99.99% Uptime achieved
0 Customer-facing incidents

The Challenge

A rapidly growing SaaS company had hit a wall. Their AWS bill was growing 15% month-over-month, deployments took 45 minutes, and the engineering team was spending more time fighting infrastructure than building product. They knew something had to change — they just couldn't see the path forward.

"We were afraid to touch anything because we didn't know what would break. Our infrastructure had become a liability instead of an asset."

— VP of Engineering

The Assessment

Over the first two weeks, I conducted a comprehensive assessment of their existing infrastructure, team workflows, and cost structure. Key findings included:

  • 73% of EC2 instances were oversized — most were running at <15% CPU utilization
  • No infrastructure as code — everything was manually configured through the AWS console
  • A single monolithic database handling both transactional and analytical workloads
  • No observability beyond basic CloudWatch alerts
  • CI/CD pipeline with 23 manual approval steps
Before
☁️ Monolith
EC2 c5.4xlarge (×8)
CPU: <15% · $85K/mo
🗄️ Single DB
RDS — OLTP + OLAP
Manual · No replicas
Original infrastructure — monolithic, over-provisioned, manually configured

The Approach

Rather than a big-bang migration, I proposed a phased approach. Each phase delivered measurable value independently, so the client saw results early and could stop at any point.

Phase 1: Right-sizing & Quick Wins

Weeks 1–3

Identified and right-sized all over-provisioned instances. Implemented auto-scaling policies. Set up basic tagging for cost attribution. Immediate 25% cost reduction.

Phase 2: Infrastructure as Code

Weeks 3–6

Migrated all infrastructure to Terraform. Created reusable modules for common patterns. Implemented CI/CD for infrastructure changes. Zero-downtime deployments became the default.

Phase 3: Architecture Optimization

Weeks 6–10

Separated transactional and analytical workloads. Introduced read replicas and caching layers. Migrated from EC2 to containerized workloads where appropriate. Deployment time dropped from 45 to 8 minutes.

Phase 4: Observability & Handoff

Weeks 10–13

Implemented comprehensive observability stack (metrics, logs, traces). Created runbooks and incident response procedures. Trained the team on all new systems. Full knowledge transfer.

After
🚢 Containers
ECS Fargate (auto-scale)
Right-sized · 40% less
📊 DB Split
Aurora (OLTP) + Redshift (OLAP)
Read replicas · Caching layer
🔍 Observability
Metrics · Logs · Traces
Automated alerts · Runbooks
Optimized infrastructure — containerized, auto-scaled, fully observable

The Results

After 90 days, the transformation was complete. The numbers speak for themselves:

  • 40% cost reduction — from $85K/month to $51K/month, saving $408K annually
  • 3x faster deployments — from 45 minutes to 8 minutes, with zero manual steps
  • 99.99% uptime — up from 99.5%, with automated failover and recovery
  • Full team ownership — the engineering team can now manage and evolve the infrastructure independently

"The biggest win wasn't the cost savings — it was that our team got their confidence back. They can now ship without fear."

— CTO

Lessons Learned

  • Start with quick wins to build trust and momentum before tackling architectural changes
  • Infrastructure as code isn't optional — it's the foundation everything else builds on
  • The best architecture is one your team can understand and maintain without external help
  • Observability must be baked in from the start, not bolted on after an incident

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