Platform migration
Move AI workloads onto Kubernetes without stopping other work
Off SageMaker to reduce cost. Off bespoke scripts onto something maintainable. From rented GPUs onto hardware you own. These migrations stall because teams cannot freeze their roadmap to do them. We plan and run the move incrementally, keeping the existing path working until the new one is proven for each workload class.
What we deliver
A migration that completes
The hard part of a migration is not building the new platform. It is moving real workloads and real users onto it without disrupting work in flight. We sequence the move so each step delivers value and the riskiest pieces move last, with the old environment available as a fallback the whole way through.
Workload and dependency inventory
What runs today: training jobs, notebooks, inference endpoints, pipelines, and the data paths and IAM each depends on. Including the workflows nobody documented, which every migration encounters.
Target architecture
A Kubernetes-based GPU infrastructure design that fits your workloads and your team's operational capacity. See cluster buildout and architecture for what we build.
Incremental cutover
Migrate by workload class, not all at once. Low-risk batch jobs first, then interactive workloads, then production inference. Each class moves after it is proven on the new platform. The old environment stays live until then.
Workflow preservation
Users will not adopt a platform that breaks their existing workflow regardless of what it improves. We carry over what they depend on: their notebooks, job submission, data access paths, so the migration is not also a retraining exercise.
Cost model before and after
For cost-driven moves (SageMaker to your own hardware is the common one), we model the real before-and-after, including the operational cost of running the infrastructure yourself. The decision is made on numbers, not projections.
Rollback at each step
Each cutover has a way back. If a workload class does not behave on the new platform, it returns to the old one while we fix the problem, rather than blocking the rest of the migration.
Common migrations
Where teams are moving from, and why
SageMaker to your own GPU cluster
Usually cost-driven. SageMaker is convenient and expensive. At a certain usage level the managed premium and per-hour markup justify running your own cluster. We move training and inference workloads onto Kubernetes and model the savings honestly, ongoing operational cost included.
Bespoke scripts to a managed platform
Teams that grew on SSH and hand-rolled bash hit a wall: nothing is reproducible, onboarding is slow, and one person holds the cluster configuration in their head. We move them to a platform with reproducible environments and self-service job submission without discarding what worked.
Rented GPUs to owned hardware
When usage is large and predictable, owning beats renting. We move workloads from a cloud provider or neocloud onto your own GPU hardware and stand up the operations to run it.
One cloud to another
Moving between providers to chase GPU availability or pricing, where Kubernetes is the portability layer that keeps workloads from being rewritten for each provider.
How we handle the common objections
Why these migrations complete
| Concern | How we address it |
|---|---|
| "We cannot stop shipping to migrate" | Incremental cutover by workload class. The old environment runs until the new one is proven for each class. No freeze required. |
| "Our last migration failed because users rejected the new platform" | We preserve the workflows users depend on, so the new platform matches the old on day one before it improves on it. |
| "We do not know everything that runs" | The inventory phase finds the undocumented workloads before they become a surprise mid-cutover. |
| "The cost savings might not materialize" | A before-and-after cost model that includes operational overhead. The move is justified or reconsidered on real numbers. |
| "What if the new platform has problems?" | Rollback at every step. A workload class that misbehaves returns to the old environment while we resolve it. |