Your cloud ships, heals and scales itself. You hold the keys.

Everything a production platform needs, from the first migration to the running system. Cloud migration, landing zones and infrastructure as code, Kubernetes, CI/CD with GitOps and security scanning, observability and SRE, disaster recovery, FinOps and the infrastructure AI workloads run on. It is built in your own cloud accounts or data center, as code in your repositories, and either handed to your team with runbooks or kept running by ours.

They write the code that runs your platform. Our engineers put the infrastructure under version control, build the pipelines every change goes through, set the SLOs and alerts, and fix what the audit finds, working in your repositories beside your team so the practice stays when the engagement ends.

Not always. For a handful of services, managed containers such as Cloud Run, ECS on Fargate or Azure Container Apps cost less to run and less to learn. Kubernetes earns its keep with many services, fine-grained scaling, GPU workloads or the need to run the same way on more than one cloud. We recommend one in writing, per workload.

DevOps is the practice: the people who build software also ship and run it, through automation. Platform engineering is how that practice scales across many teams. A platform team builds an internal developer platform with golden paths, so a developer gets a new service, its pipeline, its dashboards and its guardrails from one template instead of assembling them each time.

No, it is changing how the work gets done. AI now writes pipeline and infrastructure code, triages alerts and drafts fixes, and the major clouds ship their own SRE agents. Google's DORA research for 2025 found that AI raises delivery throughput but can hurt stability, so we run it inside the same gates as any engineer: every change through the pipeline, and every production action approved by a person.

Seven routes an application can take: retire, retain, rehost, relocate, replatform, repurchase and refactor. Retire switches off what nobody uses, retain keeps what should stay, rehost lifts it unchanged, relocate moves whole VMware estates, replatform makes a few changes for a large gain, repurchase replaces it with SaaS, and refactor rebuilds it as cloud-native. The assessment picks a route for every application, and the wave plan records it.

For most systems, yes. Data is replicated ahead of the move, both sides run in parallel, and traffic shifts in steps through weighted DNS or the load balancer, so moving back is one step at any point. Where a short write freeze is unavoidable, for some databases, it is planned into your quietest window and rehearsed before the day.

It depends on six drivers: the number of applications and servers, the route each one takes (rehost costs least, refactor most), the volume of data to move and keep in sync, how long both sides must run in parallel, your compliance requirements, and how much of the running platform your team will take over. The assessment sizes each wave against those drivers, and each phase is scoped and approved in writing before it starts.

The wave plan dates it. The assessment inventories every application and its dependencies and ends in a plan with a date for each wave, approved before anything moves. The number of applications, how tangled they are, the data volumes and your change windows set those dates, and the least risky wave goes first so the method is proven early.

Yes, by the route that suits each workload. Some move as they are to the provider's VMware service to leave the data center quickly, some are rehosted as native virtual machines, and the ones worth it become containers or managed services. The assessment compares the routes for your estate, including the licence position after the changes to VMware's pricing.

The one that fits your workloads, your skills and your contracts. AWS has the widest range of services, Azure fits estates built on Microsoft, and Google Cloud is strong in data and AI, and many enterprises run more than one. We write the comparison for your workloads, and the infrastructure code keeps you portable where portability matters.

Yes, and the monthly cost note shows it against the baseline. Idle and oversized resources, missing schedules, storage on the wrong tier, commitments that do not match the steady load, and data moving between zones or out of the cloud are the usual sources. Each saving is ranked by size and risk, shipped as a reviewed change, and measured after.

FinOps is the practice of making cloud spend a shared, measured responsibility of engineering and finance: every resource has an owner, every team sees its unit cost, and commitments are bought against real usage. FinOps for AI applies the same discipline to GPU hours and model tokens, which the FinOps Foundation's 2026 survey found 98% of practitioners now manage. We report both on the FOCUS standard that AWS, Azure and Google Cloud export.

You do. Everything is built in your cloud accounts, under your billing, and our engineers sign in through your identity provider with roles scoped to the task. Changes reach production through the pipeline, anything outside it needs your approver and expires by itself, every session is logged in your audit trail, and one change in your identity provider removes all of our access.

It is built to. Controls are mapped to the CIS Benchmarks, your provider's security baseline and the frameworks your auditors use, such as ISO 27001, SOC 2, DORA or NIS2. Policy as code enforces them on every change, and your reviewers receive the architecture, the data flows, the access model and the policy set before any build is approved.

The exit is written before the first commit. The repository, the state files, the runbooks and the dashboards are already yours, and the exit plan in the repository lists what else returns to you, in what format, and how every identity of ours is removed. Your team or your next provider starts from a platform that is documented and running.

Yes. The engineers who would lead the work join the first technical calls and write the assessment plan, and they are named in the statement of work.

The alert reaches a person with the runbook for it. Every alert is tied to an SLO and a runbook step, incidents follow a written severity and escalation path, and each one ends in a blameless postmortem whose fixes are tracked to done. Recovery plans are drilled on a schedule you approve, and the record of every drill sits in the repository.

Your choice. We hand it over to your team with runbooks, dashboards and working sessions until they run it alone, or we keep running it under Managed Services: monitoring, on-call engineers, upgrades, cost reviews and a monthly report, with response times written into the contract.