Overview
The platform had outgrown on‑premises infrastructure, but “lift and hope” wasn’t an option. Payment processing workloads had strict uptime and audit constraints, so the migration to AWS had to be zero downtime, observable end-to-end, and reversible at every step.
Starting point
Before the engagement, infrastructure knowledge lived in a few heads and a collection of scripts. Dependencies between services were only partially understood, and the operational model (monitoring, on-call expectations, change approvals) hadn’t kept up with business growth. The team needed cloud migration support that reduced risk—not a migration that created a new class of incidents.
Goals & success criteria
- Maintain zero downtime for payment processing during cutovers
- Make every migration wave repeatable via playbooks and checklists
- Ensure auditability: clear change records, access controls, and evidence trails
- Improve runtime confidence with observability tied to customer impact
- Reduce ongoing cost and overhead without trading off reliability
What we did
We treated the migration as a sequence of controlled production changes rather than a one-time event.
- Discovery & dependency mapping: built an application and data-flow map, identified shared state, and clarified operational ownership so “who approves what” wasn’t a mystery during go‑live.
- Landing zone & infrastructure as code: defined a secure baseline in AWS (networking, IAM patterns, logging) and codified it with Terraform so environments stayed consistent.
- Observability baselines: aligned dashboards and alerts to the customer journey (payment success, latency, error budgets), then validated that instrumentation survived the migration.
- Migration waves: executed cutovers with rehearsals, validation gates, and progressive rollouts. Each wave had explicit exit criteria and a rollback path.
- Cost and performance tuning: right-sized compute, addressed storage and database settings, and adopted capacity commitments once usage patterns stabilized.
Key technical decisions
- Standardized infrastructure changes through Terraform to minimize drift
- Prioritized SLO-oriented monitoring (not “CPU graphs”) to detect real customer impact
- Designed cutovers around idempotent playbooks and explicit verification steps
- Used progressive traffic shifting and “safe-to-fail” increments to reduce blast radius
- Captured operational runbooks alongside changes to support onboarding and on-call rotations
Risk management
Zero downtime is mostly about disciplined risk reduction:
- Validation gates: each migration wave required health checks and business-metric checks before proceeding
- Rollback rehearsals: rollbacks were executed in controlled conditions before being relied on
- Change windows: aligned cutovers to real traffic patterns and stakeholder availability
- Audit trail: ensured changes were traceable and access controls were consistently applied
Outcomes
The migration completed with zero downtime, and the new operating model made ongoing change safer. The team reduced infrastructure costs by 35% while improving their ability to detect and respond to customer-impacting issues.
Handoff & operating model
We left behind more than cloud resources—we left a way of working:
- Migration playbooks and validation checklists the team could reuse
- Runbooks and ownership boundaries for routine operations
- A monitoring and alerting baseline aligned to customer outcomes
- A repeatable change process suitable for audited environments
If you’re facing a similar challenge
If you need a zero-downtime cloud migration with a clear plan, rehearsals, and measurable risk controls, start with Migration Delivery.