Cloud Migration

Why Most Migrations Stall

The pitch is always the same: move to the cloud, save money, scale faster. The reality is that migration projects are where timelines go to die. Teams underestimate data gravity, overestimate their test coverage, and discover on cutover night that three services depend on a Windows-only library from 2014.

We've migrated production systems handling millions of daily transactions. The pattern that works isn't a big-bang replatforming — it's a methodical, service-by-service approach with clear rollback plans at every stage.

Our Migration Framework

Discovery & Assessment

Before touching any infrastructure, we map the full dependency graph. Not just the services your team knows about — the cron jobs, the batch processes, the SSH tunnels someone set up two years ago. We document:

• Service dependencies — what talks to what, over which ports, with what latency expectations
• Data flows — where data lives, how it moves, what compliance requirements apply
• Performance baselines — current response times, throughput, and resource utilization so we can validate post-migration

This phase typically takes 2-3 weeks and saves months of surprises later.

Strategy Design

Not everything should move at once, and not everything should move to the same service. We design migration waves based on:

• Risk tolerance — stateless services move first, databases move last
• Business impact — low-traffic internal apps before customer-facing APIs
• Technical coupling — tightly coupled services migrate together to avoid cross-cloud latency

We also define clear success criteria for each wave. If response times increase by more than 15% or error rates spike above baseline, we roll back and investigate.

Execution

Migration execution follows the strangler fig pattern where possible. New traffic routes to cloud infrastructure while legacy systems continue handling existing requests. Over days or weeks, traffic shifts gradually until the legacy system handles zero requests and can be decommissioned.

For databases, we use change data capture (CDC) pipelines to keep source and target in sync during the transition window. This gives your team time to validate data integrity without rushing the cutover.

Validation & Optimization

Post-migration, we spend 2-4 weeks in a stabilization phase:

• Performance tuning — right-sizing instances, adjusting auto-scaling thresholds, optimizing network paths
• Cost analysis — comparing actual cloud spend against projections and identifying quick wins
• Runbook creation — documenting operational procedures so your team can operate the new infrastructure independently

What We Don't Do

We don't lift-and-shift VMs into the cloud and call it done. That approach preserves every inefficiency from your on-premise setup while adding cloud billing complexity on top. If you're going to migrate, you should get real benefits: elastic scaling, managed services, better observability.

We also don't lock you into proprietary cloud services without discussing the trade-offs. Managed Kubernetes is usually worth it. A cloud-specific workflow orchestrator that makes multi-cloud impossible? That conversation needs to happen before you commit.

Typical Engagement

Most cloud migrations run 8-16 weeks depending on the number of services and data complexity. We embed with your engineering team — pairing on architecture decisions, writing migration tooling, and transferring knowledge so you're not dependent on us after the project wraps.