AI Cloud Migration, Done Deliberately.
Migrating AI to the cloud is not a lift-and-shift like a web app — models, data gravity, GPU economics and managed services all change the calculus. We migrate your models, data and ML workloads deliberately, choosing rehost, replatform or refactor per workload, so you gain scale and managed services without surprises on the bill.
Why Teams Move AI Workloads to the Cloud
AI workloads have a way of outgrowing wherever they started. A model trained on a workstation needs more GPUs; a pipeline running on a single server needs to scale; an on-prem cluster becomes a bottleneck and a procurement headache. The cloud answers these with elastic compute, managed AI services that remove undifferentiated work, and the ability to pay for GPUs by the hour rather than buying them outright. For most growing AI initiatives, the cloud is where they eventually need to live.
But migrating AI is not the same as migrating a typical application. Data gravity makes moving large training sets slow and expensive. GPU pricing in the cloud rewards careful architecture and punishes naivety. Managed services like Bedrock, SageMaker or Vertex AI can replace home-grown infrastructure entirely — or lock you in if chosen carelessly. A thoughtless lift-and-shift of an AI workload often lands in the cloud more expensive and no more capable than where it started.
We migrate AI workloads with these realities front of mind. We assess each workload and choose deliberately between rehosting it as-is, replatforming it onto managed services or refactoring it to be cloud-native, based on the value at stake rather than a blanket strategy. The result is a migration that actually delivers the scale, cost efficiency and capability the cloud promises, instead of just relocating your problems to a more expensive address.
What We Migrate and How
Our Cloud Migration Process
1. Workload Assessment
We inventory your AI workloads — models, data, pipelines, training jobs — and assess each for migration value, complexity and the right strategy, so we move the high-value, low-risk workloads first and plan the hard ones properly.
2. Strategy Per Workload
We decide rehost, replatform or refactor for each workload individually. Some are best moved as-is for speed; others are worth replatforming onto managed services or refactoring to be cloud-native — and we make that call on evidence, not dogma.
3. Cost & Architecture Design
We design the target architecture with cloud economics built in — GPU choices, spot strategies, autoscaling, storage tiers — and model the expected cost, so there are no nasty surprises when the first bill arrives.
4. Phased Migration
We migrate in phases with parallel running, moving data carefully around its gravity, validating that the cloud system produces identical results, and keeping the old system available until the new one is proven.
5. Optimize & Hand Over
After cutover we tune cost and performance against real cloud usage, document the new architecture and hand over runbooks, so your team can operate and continue to optimize the migrated system confidently.
The Cloud Is Only Cheaper If You Architect It That Way
The biggest myth about cloud migration is that the cloud is automatically cheaper. For AI workloads in particular, a careless migration can be dramatically more expensive than what it replaced — GPU instances left running idle, training jobs on on-demand pricing that should be on spot, data egress charges nobody anticipated, oversized endpoints serving trickles of traffic. The cloud rewards good architecture and punishes naive lift-and-shift, and AI workloads, with their hungry compute, amplify both effects.
We engineer cost into the migration from the start. That means using spot and reserved pricing for training, autoscaling inference so you pay for traffic you actually serve, tiering storage so cold data costs little, and designing data flows to avoid needless egress. It also means being honest about which workloads should move at all — some are cheaper and simpler left where they are, and a good migration strategy knows where to stop.
The payoff of doing this properly is real: elastic scale when you need it, no capital tied up in idle GPUs, and managed services that remove whole categories of operational toil. But that payoff only materializes when the migration is architected with cloud economics in mind. We make sure the cloud actually delivers on its promise rather than just becoming a more flexible way to overspend.
Migration as an Opportunity to Improve
A migration is a rare chance to fix what was wrong, not just relocate it. Workloads that were fragile on-prem can be made resilient; pipelines that were hand-fed can be properly orchestrated; serving that was a single point of failure can become autoscaling and redundant. We treat each migration as an opportunity to leave the workload better than we found it, capturing improvements that would be hard to justify as standalone projects.
That said, we are disciplined about scope. Trying to perfect every workload during migration is how migrations stall for years. We separate the must-fix from the nice-to-have, deliver the migration on a realistic timeline, and leave a clear backlog of further optimizations the team can pursue afterward. The goal is to land safely in the cloud with the most valuable improvements captured, not to gold-plate everything en route.
Whether you are moving off straining on-prem hardware, consolidating scattered cloud usage or modernizing onto managed AI services, we bring a migration approach built specifically for the realities of AI workloads. You get the scale and capability the cloud offers, at a cost you designed deliberately, with a cutover that does not put your production AI at risk.
Frequently Asked Questions
Moving your models, data, pipelines and training workloads to the cloud — choosing for each whether to rehost it as-is, replatform it onto managed services, or refactor it to be cloud-native. It includes architecture design, cost engineering, careful data transfer and a phased cutover that validates the new system before retiring the old.
Only if you architect it that way. A naive lift-and-shift of GPU workloads can cost more than on-prem. Done right — with spot pricing, autoscaling, storage tiering and avoiding needless egress — the cloud is both cheaper and far more flexible. We engineer cost in from the start rather than discovering it on the bill.
Rehost moves a workload as-is for speed. Replatform adapts it to use managed services, removing operational burden. Refactor reworks it to be properly cloud-native for maximum benefit. We choose per workload based on the value at stake, rather than applying one strategy to everything.
Data gravity makes big transfers slow and costly, so we plan them deliberately — staging transfers, using appropriate transfer services, validating integrity, and sometimes moving compute to the data rather than the reverse. We keep pipelines running through the migration so there is no gap in your AI's data supply.
It shouldn't. We migrate in phases with parallel running, so the cloud system is validated against the existing one before any cutover. The old system stays available until the new one is proven correct, eliminating big-bang risk and giving you a safe rollback at every step.
No. Some workloads are cheaper, simpler or more compliant left where they are. A good migration strategy knows where to stop. We assess each workload honestly and recommend against moving the ones that gain nothing from it, rather than migrating for migration's sake.
Largely, yes — by being deliberate about where you adopt proprietary managed services versus portable, open approaches. We make the trade-off explicit per workload, so you adopt lock-in only where the productivity gain clearly justifies it, and keep the rest portable across clouds.
Ready to Get Started with AI Cloud Migration?
150+ D2C brands scaled. $500 Mn+ in tracked revenue. Since 2004.