Monolith to AWS: 62% cost reduction with zero downtime.
A SaaS platform running on bare-metal servers with no auto-scaling was bleeding money. We migrated to containerized AWS with auto-scaling groups, cutting monthly infrastructure from $18K to $6.8K while improving uptime to 99.99%.
The short version.
A B2B SaaS platform with 500+ enterprise clients was running their entire stack on three bare-metal servers in a colocation facility. No auto-scaling, no redundancy, and monthly costs of $18K that increased linearly with every new customer. Their biggest client was threatening to leave over uptime issues.
Prior to migration, the platform underwent a structured engineering audit to identify architectural bottlenecks. We then executed a zero-downtime migration to AWS through our cloud infrastructure practice with containerized services, auto-scaling groups, multi-AZ deployment, and comprehensive cost optimization. Infrastructure costs dropped 62% while handling 4× more traffic capacity, and uptime improved from 99.5% to 99.99%.
Bare-metal can’t keep up with SaaS growth.
The platform had outgrown its infrastructure. Every new enterprise client meant manually provisioning more resources, and traffic spikes regularly caused outages:
- $18K/month on bare metal: three dedicated servers running at 20% average utilization but 100% during peaks; paying for capacity they rarely used
- No auto-scaling: traffic spikes during business hours caused slow responses; manual scaling took days of provisioning
- 99.5% uptime: 3.6 hours of downtime per month; enterprise clients with 99.9% SLA requirements were threatening to leave
- Single region: all servers in one data center; a single network issue took everything offline
- Manual deployments: SSH into each server, pull code, restart services; deployments took 45 minutes and sometimes failed
- No cost visibility: couldn’t attribute infrastructure costs to specific services or customers
They needed to migrate without any downtime. Their clients relied on the platform during business hours across multiple time zones.
Containerized AWS with intelligent auto-scaling.
We designed the migration to be incremental, no big-bang cutover. Our server migration methodology informed the approach: services were containerized and moved one at a time, with traffic gradually shifted using weighted DNS:
- Containerization: monolithic app decomposed into 6 services running in Docker containers on ECS
- Auto-scaling groups: CPU and request-based scaling; scales from 2 to 20 instances automatically during traffic spikes
- Multi-AZ deployment: services distributed across 3 availability zones; survives an entire AZ failure
- RDS with read replicas: database migrated to RDS with automatic failover and read replicas for reporting queries
- Cost optimization: reserved instances for baseline, spot instances for burst; savings plans for predictable workloads
- Infrastructure as Code: entire infrastructure defined in Terraform; reproducible, auditable, version-controlled
ECS with multi-AZ and intelligent cost management.
The architecture uses ECS for compute, RDS Multi-AZ for the database, ElastiCache for session and cache, and CloudFront for static assets. Auto-scaling responds to both CPU metrics and custom application metrics.
Multi-AZ | 62% cost reduction with 4× capacity
The cost optimization strategy uses a mix of reserved instances (65% baseline), spot instances (25% burst capacity at 70% discount), and on-demand (10% overflow). Combined with right-sized instances based on actual utilization data, monthly costs dropped from $18K to $6.8K while handling 4× the previous traffic capacity.
“We went from dreading traffic spikes to welcoming them. The infrastructure handles everything automatically now, our costs are lower, and we haven’t had a single outage since the migration. Our enterprise clients renewed because we finally meet their SLA requirements.”
CTO, B2B SaaS platform
Overpaying for
infrastructure that doesn’t scale?
We’ll audit your current setup and design a migration plan that cuts costs and improves reliability.