Cloud Migration Services

Migrations Fail When They’re Treated as a Lift-and-Drop

Moving workloads to AWS is not a weekend project. It’s a business transformation that touches every system, every team, and every process you rely on.

The companies that get it right treat migration as an engineering discipline — with detailed planning, systematic execution, and rigorous validation at every stage. The ones that rush? They end up with higher costs, worse performance, and months of firefighting.

We’ve guided teams through migrations ranging from a handful of applications to full data center exits. The scale changes, but the fundamentals stay the same: understand what you have, decide what each workload needs, plan the sequence carefully, and validate everything before you call it done.

Common Problems We Solve

Whether you’re early in planning or recovering from a stalled migration, the challenges we tackle are pretty predictable:

• No clear inventory. You don’t have a complete picture of what’s running, how systems depend on each other, or which workloads are candidates for migration versus retirement.
• Stalled decision-making. Teams debate lift-and-shift versus re-architecture endlessly without a framework for evaluating the trade-offs for each workload.
• Underestimated dependencies. Applications that seemed standalone turn out to share databases, message queues, or network paths with other systems. You move one thing, three others break.
• Unrealistic timelines. Leadership expects everything migrated in a quarter. Your team knows it’ll take longer but can’t articulate why in business terms.
• Downtime anxiety. The business can’t afford extended outages, but the migration plan doesn’t account for cutover complexity or rollback scenarios.
• Data migration bottlenecks. Large databases and storage volumes take far longer to transfer than anyone estimated. Synchronization during cutover is poorly planned.
• Post-migration drift. Workloads land in AWS but nobody optimizes them for the new environment. They run on oversized instances with on-prem configs, costing way more than they should.

Our Approach

We follow a phased approach built around the industry-standard 6R framework — Rehost, Replatform, Refactor, Repurchase, Retire, and Retain. But the framework alone isn’t a plan. The real value is in how you apply it to your specific environment and constraints.

Phase 1: Discovery and Assessment

Before moving anything, we need the full picture. We deploy discovery tooling — augmented by AI-driven analysis — to inventory your servers, applications, databases, and network flows. The output is a detailed dependency map showing which systems talk to each other, how much data moves between them, and where the coupling is tight enough to require coordinated migration.

We also assess each workload against business criteria: criticality, compliance requirements, performance sensitivity, and your team’s capacity to manage change. This drives the migration strategy for every application — not a blanket decision applied across the board.

Phase 2: Strategy and Sequencing

With the inventory and dependency map in hand, we assign each workload a migration strategy:

• Rehost (lift-and-shift) for workloads that need to move quickly with minimal change
• Replatform for workloads that benefit from managed services (e.g., moving a self-managed database to RDS) without a full rewrite
• Refactor for workloads where the migration is a chance to modernize — typically moving to containers or serverless
• Repurchase for applications better replaced by SaaS alternatives
• Retire for systems that are no longer needed
• Retain for workloads that should stay on-premises for now

Then we build a migration wave plan that sequences workloads into groups. Each wave accounts for dependencies, risk tolerance, and team capacity. Low-risk, low-dependency workloads go first — they build confidence and let us refine the process. Critical systems with complex dependencies migrate later, once your team has solid operational experience in AWS.

Phase 3: Execution and Cutover

Each wave follows a repeatable process: provision the target environment, replicate data, configure networking, test functionality, validate performance, and execute cutover. We build detailed runbooks for every cutover — including rollback procedures — so nothing is left to improvisation during the migration window.

For database migrations, we establish continuous replication well before cutover to keep the switchover window as tight as possible. For applications, we use DNS-based traffic shifting or load balancer routing to control the transition and enable rapid rollback if issues come up.

Phase 4: Validation and Optimization

Migration isn’t done when the workload is running in AWS. We validate that performance meets or exceeds your on-prem baselines, that monitoring and alerting are in place, and that operational runbooks are updated.

Then we optimize — right-sizing instances, adjusting storage tiers, and enabling AWS-native features that weren’t available to you before.

What You Get

• Application inventory and dependency map — a complete catalog of workloads, their interdependencies, and data flows
• Migration strategy per workload — a documented 6R decision for every application with clear rationale
• Wave plan — a sequenced migration schedule organized into execution waves with timelines and resource requirements
• Cutover runbooks — step-by-step procedures for each migration wave, including validation checkpoints and rollback instructions
• Landing zone configuration — a properly structured AWS environment with networking, security, and governance ready for your workloads
• Post-migration optimization report — right-sizing recommendations and cost projections for the newly migrated environment

AWS Services We Use

• AWS Migration Hub — centralized tracking of migration progress across workloads
• AWS Application Discovery Service — automated inventory and dependency mapping for on-prem environments
• AWS Database Migration Service (DMS) — continuous database replication with minimal downtime cutover
• AWS Server Migration Service — automated server replication for lift-and-shift workloads
• AWS Transfer Family — secure file transfer for large data migrations
• Amazon Route 53 — DNS-based traffic management for controlled cutover and rollback