Plan and migrate applications, data, and infrastructure to AWS with workload discovery, target architecture, phased execution, security validation, and cost planning.
AWS Advanced Tier Partner★ 4.9/5 on Clutch (9 reviews)Replies within 1 business dayAn AWS cloud migration service moves applications, data, and supporting infrastructure into an AWS environment through planned waves. It combines discovery, dependency mapping, target foundation work, rehearsal, cutover, rollback readiness, validation, and operating handoff so the move is treated as a controlled change rather than a server copy exercise.
CodetoKloud selects the migration treatment by workload. The work can preserve an application, replatform it onto managed AWS services, or modernize selected components when that change has a clear operational or business reason.
Every migration artifact supports a cutover decision, validation step, or future operating responsibility.
We inventory applications, data stores, integrations, traffic paths, scheduled jobs, operational tooling, access, performance needs, recovery expectations, and owners so hidden dependencies surface before cutover.
We select a practical treatment for each workload, group systems by dependency and business risk, define wave order, and identify what must be remediated or validated before a workload moves.
We prepare the required account structure, networking, connectivity, IAM, logging, security baseline, infrastructure as code, backup, and operational access before production workloads depend on it.
We migrate a representative workload or rehearsal dataset, record timing and failure points, validate the target, and turn the findings into an owned cutover checklist with communication and decision points.
We define data synchronization, change freeze, traffic switch, health checks, acceptance criteria, roles, timing, and the conditions that trigger rollback while the source environment remains recoverable.
We verify application behavior, integrations, data, security, monitoring, backups, performance, and cost signals, then provide diagrams, runbooks, code ownership, known issues, and the next optimization backlog.
We reduce uncertainty before production cutover, learn from representative workloads, and preserve an agreed recovery path until validation is complete.
We collect technical evidence and owner input to map systems, data, integrations, operating requirements, business criticality, and migration constraints.
We build the required AWS foundation, choose the treatment for each workload, and sequence waves so shared services and tightly coupled dependencies move in a controlled order.
We rehearse the migration on a representative workload, validate function and operations, measure the steps, and refine success criteria, cutover timing, and rollback triggers.
We coordinate the production wave, synchronize data, switch traffic, run agreed health checks, and keep the source recovery path available until the acceptance criteria are met.
We confirm data and integrations, observe production behavior, resolve migration defects, verify backups and monitoring, and transfer the new environment to its named operating owners.
These results were reported for the named engagements and depend on each workload, baseline, scope, and operating model.
CodetoKloud moved the healthcare workforce platform into a private Multi-AZ AWS architecture with EKS, RDS, GitOps, observability, backup, and technical safeguards for regulated workloads.
CodetoKloud mapped dependencies, rehearsed the move, and used managed AWS services to add Multi-AZ availability, controlled releases, monitoring, identity controls, and recoverable data around the existing application.
CodetoKloud migrated four application brands onto a consistent AWS platform using ECS Fargate, Aurora MySQL Multi-AZ, Application Load Balancers, AWS Amplify, and phased production cutovers.
Bring the current workload list, known dependencies, target date, and business constraints. We will help define the discovery and validation needed for a credible migration plan.
AWS, infrastructure as code, automation, containers, and observability tools support workload discovery, target build, test migration, cutover, and validation.
Answers about scope, modernization, downtime, rollback, migration waves, and post-cutover validation.
An AWS migration can include workload discovery, dependency mapping, migration treatment and wave planning, landing zone preparation, infrastructure as code, test migration, cutover, rollback planning, post-migration validation, stabilization, documentation, and operating handoff.
Not always. Some workloads can move with limited application change, while others benefit from replatforming or selective modernization. CodetoKloud evaluates business risk, dependencies, operating burden, and the target outcome before recommending how much change belongs in the migration itself.
We map dependencies, prepare the target in advance, rehearse the migration, measure data movement and validation steps, define a controlled change window, and assign cutover roles. The appropriate approach depends on the application's state, integrations, data volume, and tolerance for interruption.
A rollback plan should define the source recovery path, data synchronization limits, decision owner, health and acceptance checks, rollback triggers, communication steps, and the latest safe point to reverse the traffic or data change. These details are tested where the workload allows it.
Workloads are grouped by dependencies, shared services, business criticality, data movement, technical similarity, owner availability, and the lessons that one wave can provide for the next. The first wave should be representative enough to validate the approach without carrying unnecessary business risk.
The team validates application behavior, integrations, data, security, performance, monitoring, alerts, backups, and cost signals. CodetoKloud documents known issues, stabilizes the environment within the agreed scope, transfers operating knowledge, and records later optimization work separately.
