On-Prem to Cloud Migration Without Downtime¶

“We’ll move it to the cloud” sounds simple. For core systems, it’s an operational change, not just an infrastructure one. This is a blueprint for migrating without big bang, without downtime, and without data loss.

5 Principles¶

1 Stabilize and Measure First¶

Don’t migrate an unstable system. If you don’t have monitoring, you don’t know how the system works now — and you won’t be able to tell if it works worse after migration. The first step is observability, not Terraform.

Measure the baseline: latency, throughput, error rate, dependencies between components. Map bottlenecks. Identify single points of failure. You need to know this before you move anything.

• Monitoring and alerting on all key components (APM, logs, metrics)
• Dependency map: what depends on what, who calls whom, what the critical paths are
• Baseline metrics: P50/P95 latency, requests/s, error rate, availability
• Identify bottlenecks and SPOFs before migration, not after

2 A Series of Small Switches, Not Big Bang¶

Big bang migration is a gamble. If you move everything at once and something fails, you have nowhere to go back. The right approach: an integration layer between on-prem and cloud, gradual component migration, canary rollout.

Each switch is small, reversible, and measurable. Move one service. Watch metrics. Compare with baseline. If everything is OK, continue. If not, rollback.

• Integration layer (API gateway, service mesh) enables traffic routing
• Canary: 5% of traffic to cloud, 95% on-prem → gradually increase
• Each component has its own migration plan and rollback procedure
• Never migrate two dependent components simultaneously

3 Data Is the Hardest Part¶

Code is easy to move. Data is not. Data consistency between on-prem and cloud is the hardest problem of the entire migration — and the most common cause of failure.

Dual-write (writing to both environments simultaneously) sounds like a solution, but brings its own problems: conflict resolution, eventual consistency, rollback. You need an audit trail and a clear decision tree for every data conflict.

• Define “source of truth” for each data object in each migration phase
• Dual-write: clear strategy for conflict resolution
• Audit trail: every write logged with timestamp and source
• Data rollback: how to return data to a consistent state
• Test data migration on a copy of production data, not on mocks

4 The Release Process Is Part of the Migration¶

Migration is not a one-time project — it’s a series of releases. And every release needs: a staging environment, automated tests, canary deploy, and a rollback plan. If you don’t have a CI/CD pipeline, build one before migration — not during.

• CI/CD pipeline covering both environments (on-prem and cloud)
• Staging: cloud test environment mirroring production
• Canary deploy: new version on a small % of traffic
• Automated smoke tests after each deploy
• Rollback: one-click return to the previous state (infra + data)

5 DR and Incident Process Are Not “Later”¶

Disaster recovery and incident response must be in place from day one of hybrid operations. You have two environments, two sets of components, and new failure modes — and you need to know what to do when something goes down.

Define RTO (Recovery Time Objective) and RPO (Recovery Point Objective) for each component. Write runbooks. Test them. A runbook nobody has tested is just a document.

• RTO: how quickly must the service be restored (minutes? hours?)
• RPO: how much data can you afford to lose (zero? an hour?)
• Runbooks for every scenario: cloud outage, on-prem outage, data inconsistency
• DR tests: regular, planned, with RTO/RPO measurement
• Escalation chain: who decides, who communicates, who fixes

4 Phases of Migration¶

Phase A: Readiness¶

Before you move the first component, you need an infrastructure foundation. Observability, CI/CD pipeline, and IAM (Identity & Access Management) in the cloud.

• Cloud account setup: networking, VPN/peering to on-prem, security groups
• Observability stack in the cloud: same dashboards as on-prem (Grafana, Datadog, ELK)
• CI/CD pipeline capable of deploying to both environments
• IAM: roles, policies, service accounts — principle of least privilege
• Baseline tests: validate that the cloud environment works before migration

Phase B: Hybrid Period¶

Connecting on-prem and cloud. First stateless components migrate. Traffic is routed through the integration layer — most still remains on-prem.

• API gateway / service mesh as the integration layer
• Migration of first stateless services (stateless API, frontend, workers)
• Canary rollout: 5 → 10 → 25 → 50 → 100% of traffic
• Monitoring comparison: cloud vs. on-prem latency, error rate
• Rollback test: verify that switching back to on-prem works

Phase C: Gradual Switching¶

Stateful services and databases. This is where migration is hardest — data, consistency, dual-write. Each component has its own plan, its own timeline, its own rollback.

• Migrate stateful services one by one — never two dependent ones at once
• Data migration: replication → dual-write → switch source of truth → cleanup
• Performance tests after each migrated component
• Load tests: simulate production load on the cloud instance
• Stakeholder communication: who knows what’s happening and when

Phase D: Consolidation¶

Everything runs in the cloud. Now comes cleanup: shutting down on-prem, cost optimization, final DR tests, and documentation.

• Decommission on-prem: gradually shut down old instances
• Cost optimization: right-sizing, reserved instances, spot instances
• Final DR test: full failover, RTO/RPO measurement
• Documentation: updated runbooks, architecture diagrams, playbooks
• Retrospective: what worked, what didn’t, lessons learned for the next migration

Conclusion¶

Cloud migration is not an IT project — it’s an operational transformation. Success depends on preparation (observability, CI/CD), incrementalism (small switches, not big bang), and data consistency. Five principles and four phases give you the framework. The details depend on your environment — and that’s why you should start with an inventory, not Terraform.