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Posted on • Originally published at binadit.com

How a fintech startup cut cloud costs 65% with an open-source sovereign stack

#opensource #costoptimization #sovereigncloud #kubernetes

How we slashed a fintech's AWS bill by 65% with open source infrastructure

A European fintech was hemorrhaging €28,000 monthly on AWS for processing 2.3M transactions. Six months later, they were spending €9,800 for the same workload with better performance. Here's the engineering breakdown.

The problem: classic cloud cost spiral

The fintech ran 40 microservices across AWS with PCI DSS and GDPR requirements. Their architecture looked standard on paper, but the monthly bills told a different story.

Compute waste everywhere:

  • 60 EC2 instances running 24/7
  • CPU utilization: 23% peak, 8% overnight
  • Only 30% reserved instances (paying on-demand for predictable workloads)

Storage bleeding money:

  • 2.4TB monthly PostgreSQL logs with no retention
  • 800GB application logs stored indefinitely
  • 15TB of accumulated EBS snapshots

Network transfer costs:

  • €3,200/month in cross-AZ microservices chatter
  • NAT gateway charges for external API calls

The kicker? Their workloads were completely predictable. Payment processing peaked 9 AM to 6 PM weekdays. Fraud detection ran nightly batches. Customer onboarding spiked during monthly marketing campaigns.

The solution: sovereign open source stack

Instead of AWS optimization theater, we built a dedicated stack using:

  • Proxmox: Virtualization and cluster management
  • Ceph: Distributed storage with built-in redundancy
  • OpenStack: Cloud APIs without vendor lock-in
  • Kubernetes: Efficient resource sharing

Implementation highlights

Hardware foundation:
6 bare-metal servers in Frankfurt: 64 cores, 256GB RAM, 4TB NVMe each.

Smart Ceph storage tiering:

# Hot transaction data on NVMe
ceph osd pool create transactions 128 128 replicated
ceph osd pool set transactions size 3

# Cold analytics data with erasure coding
ceph osd pool create analytics 64 64 erasure
ceph osd erasure-code-profile set ec-profile k=4 m=2
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Resource-aware Kubernetes scheduling:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: payment-api-hpa
spec:
  minReplicas: 2
  maxReplicas: 12
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
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Migration strategy:
Built in parallel, migrated non-critical services first, then payment processing during a 47-minute maintenance window using PostgreSQL logical replication.

Results that matter

Performance improvements:

  • API response times: 180ms → 95ms average
  • Same 99.95% uptime SLA maintained
  • Sub-200ms latency requirements exceeded

Cost breakdown:

  • Before: €28,000/month on AWS
  • After: €9,800/month total (€4,200 hardware + €3,200 managed services)
  • 65% cost reduction

Operational wins:

  • No vendor lock-in
  • Full EU data residency
  • Predictable monthly costs
  • Better resource utilization (65% average vs 23%)

Key takeaways for engineers

  1. Audit first: Most "scaling" problems are resource waste problems
  2. Predictable workloads don't need cloud premium: If you can forecast it, you can right-size it
  3. Open source infrastructure scales: Proxmox + Ceph + K8s handles enterprise workloads
  4. Migration risk is manageable: Parallel builds beat big-bang deployments

The real lesson? Sometimes the best cloud optimization is leaving the cloud entirely.

Originally published on binadit.com

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