AWS FINOPS KUBERNETES

Case Study: $240K/Year AWS Savings for a Healthcare SaaS

Max Moretti | February 19, 2026 | 6 min read

A healthcare SaaS company came to us with a simple problem: their AWS bill had grown from $12K/month to $38K/month in 18 months, but their user base had only doubled. Something was scaling linearly when it should have been sublinear.

Their VP of Engineering put it bluntly: "We have 50,000 users and we're spending $38K/month. Our competitor has 200,000 users and spends less than us. What are we doing wrong?"

After a 2-week audit and 6 weeks of implementation, we brought their bill down to $18,200/month — a 52% reduction, saving $237,600/year. Here's every single thing we changed.

The Audit

We started by categorizing spend by AWS service:

Service Monthly Cost % of Total
EC2 (EKS nodes) $16,400 43%
RDS (PostgreSQL) $7,200 19%
ElastiCache (Redis) $3,100 8%
S3 + CloudFront $2,800 7%
NAT Gateway $2,600 7%
Data Transfer $2,400 6%
EBS Volumes $1,800 5%
Other $1,700 5%
Total $38,000 100%

Every single line item had optimization potential. Let's go through them.

1. EC2/EKS: Right-Size + Spot + Karpenter ($16,400 → $6,800)

This was the biggest win. Their EKS cluster was running on m5.2xlarge On-Demand instances because "that's what the AWS Quick Start guide suggested."

Changes:

We wrote about the Karpenter setup in detail in our Karpenter + Spot + Scale-to-Zero post.

Savings: $9,600/month (58%)

2. RDS: Reserved Instances + Read Replicas ($7,200 → $3,600)

They were running a db.r6g.2xlarge PostgreSQL RDS instance — On-Demand, Multi-AZ. The database was at 15% CPU utilization on average.

Changes:

-- Before: analytics queries on primary
SELECT date_trunc('day', created_at), count(*)
FROM patient_records 
WHERE created_at > now() - interval '90 days'
GROUP BY 1;

-- After: same query routed to read replica via connection string
-- analytics_db_url = postgres://replica-endpoint:5432/healthdb

Savings: $3,600/month (50%)

3. ElastiCache: Right-Size + Reserved ($3,100 → $1,400)

Running cache.r6g.xlarge with 3% memory utilization. They were caching session data for 50K users — that fits in a cache.r6g.large with room to spare.

Changes:

Savings: $1,700/month (55%)

4. NAT Gateway: The Silent Budget Killer ($2,600 → $800)

This one surprised everyone. NAT Gateway charges $0.045/GB for data processing — and their pods were pulling Docker images through NAT on every deploy.

Changes:

# VPC Endpoints we added
aws ec2 create-vpc-endpoint \
  --vpc-id vpc-xxx \
  --service-name com.amazonaws.us-east-1.ecr.api \
  --vpc-endpoint-type Interface

aws ec2 create-vpc-endpoint \
  --vpc-id vpc-xxx \
  --service-name com.amazonaws.us-east-1.s3 \
  --vpc-endpoint-type Gateway

Savings: $1,800/month (69%)

NAT Gateway costs are one of the most overlooked line items in AWS bills. Every company we audit is overpaying for NAT.

5. S3 + CloudFront: Lifecycle Policies + Compression ($2,800 → $1,600)

They were storing every version of every file forever. Medical document uploads from 3 years ago were still in S3 Standard.

Changes:

{
  "Rules": [
    {
      "ID": "ArchiveOldDocuments",
      "Status": "Enabled",
      "Transitions": [
        {
          "Days": 90,
          "StorageClass": "STANDARD_IA"
        },
        {
          "Days": 365,
          "StorageClass": "GLACIER"
        }
      ]
    }
  ]
}

Savings: $1,200/month (43%)

6. Data Transfer: Keep Traffic Inside the VPC ($2,400 → $1,200)

Cross-AZ data transfer charges were eating them alive. Services in us-east-1a were talking to services in us-east-1c, paying $0.01/GB each way.

Changes:

# Topology-aware routing
apiVersion: v1
kind: Service
metadata:
  name: user-service
  annotations:
    service.kubernetes.io/topology-mode: Auto

Savings: $1,200/month (50%)

7. EBS Volumes: Delete Orphans + Change Types ($1,800 → $800)

22 unattached EBS volumes sitting there doing nothing. PersistentVolumes from deleted pods that nobody cleaned up. Classic.

Changes:

# Find orphaned volumes
aws ec2 describe-volumes \
  --filters Name=status,Values=available \
  --query 'Volumes[*].{ID:VolumeId,Size:Size,Created:CreateTime}' \
  --output table

Savings: $1,000/month (56%)

The Final Scorecard

Service Before After Savings %
EC2/EKS $16,400 $6,800 $9,600 58%
RDS $7,200 $3,600 $3,600 50%
ElastiCache $3,100 $1,400 $1,700 55%
NAT Gateway $2,600 $800 $1,800 69%
S3/CloudFront $2,800 $1,600 $1,200 43%
Data Transfer $2,400 $1,200 $1,200 50%
EBS $1,800 $800 $1,000 56%
Other $1,700 $2,000 -$300 -18%
Total $38,000 $18,200 $19,800 52%

"Other" went up slightly because we added monitoring tools (Kubecost, custom exporters) that have a small compute cost. Worth every penny.

Annual savings: $237,600

The entire project — audit, implementation, testing, documentation — took 8 weeks and cost them a fraction of one month's savings.

The Most Important Change

The technical optimizations were important, but the cultural change mattered more. We installed our FinOps dashboard on day one of the project, so the team could see costs in real-time from the start.

By week 3, engineers were coming to us with optimization ideas we hadn't thought of. One developer noticed their service was making 10x more S3 API calls than necessary due to a missing cache layer. Another found a cron job that was spinning up a large instance for 2 minutes every hour.

When you make costs visible, engineers optimize naturally. They just need the data.

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