How a B2B SaaS Team Cut Their AWS Bill by 40% with Rightsizing, Graviton, Savings Plans and S3 Lifecycle Policies
A 120-engineer SaaS company slashed its AWS spend by 40% in 90 days by combining instance rightsizing, Graviton3 migration, Compute Savings Plans, and aggressive S3 lifecycle tiering — without touching a single line of product code.
Infrastructure · intermediate · 12 min read · 2259 words · By Sandip Das · Published
What this covers
- The Situation
- The Problem
- Constraints
- Options Considered
- The Solution
- Implementation
- Results
- What Went Wrong Along the Way
- Lessons Learned
- Apply It Yourself
Key numbers
| Metric | Before | After |
|---|---|---|
| Monthly AWS spend | $148,000 | $88,500 |
| API fleet compute cost per month | $6,600 | $1,633 |
| S3 monthly storage cost | $322 | $107 |
| On-demand compute exposure | 100% | 15% |
| API fleet avg CPU utilisation | 12% | 48% |
Key components
- AWS Compute Optimizer — Provided data-driven instance-type recommendations that guided the rightsizing decisions for both the API and worker fleets.
- Graviton3 EC2 instances (m7g, c7g) — Replaced x86 instances with ARM-based equivalents, delivering 20-40% better price-performance for stateless compute workloads.
- Compute Savings Plans — Covered 85% of the post-rightsizing compute baseline with a 1-year commitment, eliminating on-demand pricing for predictable workloads.
- S3 Lifecycle Policies — Automatically transitioned objects through Standard, Standard-IA, Glacier IR and Deep Archive tiers based on age, cutting storage costs by 67%.
- Blue-green weighted ALB rollout — Enabled safe, incremental traffic shifting to the new Graviton API fleet with a fast rollback path, protecting the 99.9% uptime SLA.
The Situation
The company in question is a 120-engineer B2B SaaS business selling workflow-automation tooling to mid-market enterprises. Their platform handles roughly 4 million API calls per day, ingests structured event data from customer integrations, runs nightly ML-scoring batch jobs, and stores several terabytes of customer-uploaded documents and audit logs. The engineering organisation is split into eight product squads plus a four-person platform team that owns AWS infrastructure. They run entirely on AWS in two regions (us-east-1 primary, eu-west-1 for GDPR-scoped customers), use Terraform for all infrastructure, and deploy via GitHub Actions into ECS Fargate and EC2 Auto Scaling Groups.
At the start of this story the company was spending approximately $148,000 per month on AWS. The CFO had flagged cloud spend as a line item growing faster than revenue — 34% year-on-year against 22% revenue growth — and gave the platform team a mandate to reduce it without degrading SLAs or delaying the product roadmap.
The rest of this case study — the trade-offs, the scaling numbers and the build prompt — is available to LearnXops members. Create an account or see membership.
Topics: aws, cost-optimization, graviton, s3, savings-plans, rightsizing
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