How is a migration factory different from cloud migration waves?

Cloud Migration Waves is the execution of a specific set of workload moves. Migration Factory is the repeatable operating model — governance, pods, playbooks, tooling and evidence — used to deliver many waves consistently at scale.

How many workloads can move in parallel?

Throughput depends on backlog composition, dependency depth, cutover-window availability and pod capacity. Sizing is agreed during factory design, and refined as retrospectives show sustainable cadence.

Is hypercare a fixed period?

No. Hypercare is scoped per wave against agreed exit criteria — defect thresholds, monitoring readiness and run-team acceptance — rather than a fixed calendar period.

How is a migration factory sized against workload volume and timeline?

Factory throughput — parallel waves, engineer squads, tooling licences and cutover windows — is sized from the workload inventory, dependency map and business freeze calendar produced during discovery. Sizing is revisited at each wave boundary rather than fixed at kickoff and left to drift.

How are application dependencies discovered before waves are planned?

Dependency discovery uses agent-based or agentless telemetry across servers, storage, network flows and database links, cross-checked with application-owner interviews. Waves are only committed once cross-application dependencies are visible so a move does not accidentally strand a downstream consumer.

How is the factory handed over to internal operations once migration completes?

Factory tooling, patterns, runbooks and residual backlog are documented and transferred to the customer's platform team, along with training sessions on the automation used. The intent is that internal teams can run future waves or steady-state operations without a factory retainer.

Cloud Migration & FinOps

Migration Factory

A repeatable operating model for delivering migration waves at scale.

Design and operations cover the migration factory that stands behind cloud programs — governance, migration pods, standardized playbooks, automated tracking and evidence-based change control — so successive waves ship at consistent quality and cadence.

Repeatability

Standardized playbooks

Discovery, build, migrate, validate and cutover playbooks reused and refined wave after wave.

Delivery Capacity

Repeatable delivery pods

Migration pods sized to the wave backlog and workload complexity, with defined roles and steering cadence.

Controls & Evidence

Traceable governance

Runbooks, checklists, test results and cutover evidence captured for audit and continuous improvement.

Platform ecosystem

Multi-vendor by design — hyperscaler-native services combined with best-in-class discovery, migration and FinOps tooling.

Wave dashboards
Atlassian Jira · Confluence
ServiceNow ITSM
Ansible · Terraform
GitHub Actions · Azure DevOps
Backstage
Runbook automation
Cutover evidence stores

Scope in detail

Factory Governance & Delivery Model
Program-level governance covering intake criteria, RACI, steering cadence, risk register, decision log and evidence — the operating model that turns individual waves into a repeatable delivery capability.
Standardized Discovery & Wave Intake
Standard discovery templates, dependency capture and disposition confirmation feed a controlled migration backlog and wave pipeline, so each wave enters delivery with consistent inputs.
Migration Pods & Role Definition
Dedicated migration pods staffed with architect, cloud engineers, migration engineers, testers and delivery lead — with clear responsibilities across discovery, build, migration, cutover and hypercare.
Automated Tracking, Runbooks & Evidence
Wave dashboards, runbook templates, checklist automation and cutover evidence stored for audit — status, risks and dependencies visible to sponsors, engineers and change-control boards.
Testing, Cutover & Change Control
Standard test approach across smoke, integration, performance and rollback validation, aligned to change approval, communication plans and business-freeze calendars.
Hypercare, Handover & Continuous Improvement
Structured hypercare exit criteria, knowledge transfer, retrospective feedback into playbooks and enhancement of the factory model as complexity and wave volume grow.

Where cloud programmes get stuck

Every migration wave starts from scratch
Reinventing the plan, runbook and test approach per wave slows throughput and increases execution risk.
No shared evidence trail across waves
Cutover decisions, test results and change approvals are scattered across email and shared drives, making audit and retrospective learning difficult.
Pod capacity mismatched to backlog
Wave volume, workload complexity and dependency depth are not translated into a realistic delivery capacity plan.
Hypercare exit that never quite completes
Waves stay in hypercare indefinitely because exit criteria, run-team readiness and defect handover were never defined up front.

Engagement approach

A factory lifecycle that industrializes wave delivery — from intake through cutover, hypercare exit and continuous improvement.

Assess
Review the migration backlog, wave intake criteria, existing tooling and delivery capacity. Identify governance, automation and evidence gaps.
Design
Define the factory operating model — pods, RACI, playbooks, runbook templates, tooling stack, dashboards and hypercare exit criteria.
Operate
Run waves through the factory pipeline with automated tracking, change control, testing and cutover evidence captured against each workload.
Improve
Retrospective feedback into playbooks, tooling and pod composition — scaling capacity and improving predictability as the program matures.

Deliverables & artefacts

Factory operating model, RACI and steering cadence
Standard discovery, build, migrate and cutover playbooks
Migration pod definitions and capacity plan
Wave dashboards and evidence stores
Change-control, test and cutover templates
Retrospective loop and playbook refresh cadence

End-to-End IT Integration and Support

From assessment and solution design through implementation, remediation, ongoing support, and lifecycle optimization.

Fix What's Broken — Remediation Sprints

Migration stuck mid-wave, workloads over-provisioned in cloud, DevOps pipelines flaky, or FinOps spend running away? We run fixed-scope remediation sprints against a defined cloud outcome — no open-ended consulting hours.

Landing-zone, IAM and network baseline fixes; migration wave unblocking
Right-sizing, reserved/savings-plan coverage and FinOps waste elimination
CI/CD pipeline hardening, IaC drift correction and release-process cleanup

Request a remediation scope

Support & Maintenance — AMC-Backed

Keep cloud estates efficient and reliable after migration — cost governance, platform hygiene and vendor escalation under a Jiva managed cloud service.

Managed cloud operations across AWS, Azure and Google Cloud with SLAs
Continuous FinOps: budget alerts, right-sizing and commitment management
Patching, backup verification, IaC/DevOps pipeline care and quarterly reviews

Explore AMC & support contracts