Cloud migrations fail when they’re built on assumptions. When application dependencies are mapped manually, when TCO estimates come from spreadsheets rather than instrumented infrastructure, and when migration waves are sequenced by instinct rather than connectivity data, risk accumulates quietly until it surfaces at the worst possible moment.
That’s the scenario an Ingram Micro end customer was working hard to avoid. Facing a large, highly interdependent on-premises environment and a mandate to move to AWS, the team needed more than a migration plan. They needed a plan they could actually trust.
Without that level of visibility, planning would have depended too heavily on assumptions rather than operational data.
The challenge: three gaps, one complex environment
Before a single workload could move, three critical questions needed answers, and the existing environment made each one hard to answer well.
1. COST VISIBILITY
The customer lacked a clear picture of current total cost of ownership and had no reliable projection of what the equivalent workloads would cost on AWS. Without that financial baseline, it was impossible to build a business case or validate the economics of migration.
2. APPLICATION DEPENDENCY MAPPING
The environment contained many interconnected applications and servers. Understanding which workloads were tightly coupled — and which could be decoupled safely — was essential for avoiding failures mid-migration. That understanding simply didn’t exist at the required level of detail.
3. MIGRATION SEQUENCING
Even with dependency data, organizing workloads into realistic, low-risk migration waves requires a systematic approach. The team needed a way to separate non-production from production migrations, identify which applications needed to move together, and sequence everything in an order that minimized downstream disruption.
The solution: Cloudamize across two AWS program phases
Cloudamize’s DISCOVER, ANALYZE, and PLAN capabilities were deployed across both the OLA / Cloud Viability Assessment and MAP Assess phases, turning raw environment data into actionable migration intelligence.
OLA / CLOUD VIABILITY ASSESSMENT
- Established initial TCO baseline from actual environment metrics
- Generated projected AWS cost model for future-state financial planning
- Gave the customer credible data to support the business case
MAP ASSESS PHASE
- Application grouping, R-factor analysis, and wave planning
- Real-time visualization of server-level incoming and outgoing connections
- Live dependency validation with customer stakeholders during working sessions
- Identification of migration-critical interdependencies across the estate
The PLAN feature proved especially valuable. Rather than drawing dependency maps after the fact, the team could search for specific servers during live working sessions and immediately visualize how they connected to the rest of the environment. That real-time capability meant the customer could validate assumptions on the spot, and adjust migration groupings based on what the data actually showed.
The outcome: from assessment to mobilization
Cloudamize played a direct role in making both AWS program phases successful and contributed to a smooth transition from assessment into active migration execution.
The customer is now running a multi-month migration program. Non-production workloads are moving first, with production migrations sequenced in later waves — an approach made possible by the dependency intelligence and wave planning the platform provided during assessment.
Beyond the tactical execution, the platform delivered something harder to quantify but equally important: confidence. Executives and project teams alike had a clear line of sight from discovery through assessment to mobilization. Instead of a generic migration plan, they had a phased roadmap grounded in actual workload characteristics, connectivity patterns, and modernization readiness indicators.
For a customer with highly interdependent applications, dependency visibility was critical in determining the first several migration waves.
What this means for complex migration programs
The Ingram Micro engagement illustrates something that comes up repeatedly in large-scale cloud migrations: the difference between a plan that looks right and a plan that holds up under execution pressure. The former is built on assumptions. The latter is built on data.
When applications are highly interdependent, the cost of a wrong sequencing decision compounds. A workload that moves before its dependencies are ready can cascade failures across multiple teams and timelines. Cloudamize’s value in this engagement wasn’t just efficiency, it was de-risking a program that couldn’t afford to get sequencing wrong.
That’s the case for data-driven migration planning. Not as a nice-to-have, but as the foundation that makes everything downstream more reliable.
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