Every week brings a new breakthrough in AI, and a new strain on infrastructure. One day, you’re fine-tuning a small model on a local machine. The next, you’re trying to schedule workloads that consume dozens of GPUs across multiple locations. And that doesn’t include the pace of new hardware, which increases what you can do. When the pace of innovation outstrips infrastructure planning, the ability to run your workloads wherever makes the most sense can determine the pace at which you’re innovating.
The question isn’t what AI can do anymore — it’s where you can run it, and how fast you can move. You need the freedom to start small and scale as you go, to shift between on-prem and cloud as needs or budgets change, or mix both as part of a hybrid setup, and to do all that without any downtime and teaching everyone (again) how to use the new interface.
In this blog post, we’ll explore the notion of environment lift-and-shift — the ability to run your workloads anywhere and achieve cost savings, without any impact on developer productivity. We’ll discuss resource abstraction and how it helps AI builders mind the research and not the infrastructure.
Bursting to the Cloud
The most common lift-and-shift use case is bursting to the cloud. It’s the well-known model: you maximize your local compute capacity, then offload jobs to the cloud when demand spikes.
But while that sounds simple, it rarely is. Continuity is key — no one wants to build an entirely new environment for temporary capacity. Even when containerizing workloads, ad hoc bursting isn’t worry-free. It often requires manual DevOps work to provision resources, set up networking, replicate data, and align underlying execution environments.
Containerization helps ensure consistency, but it’s not a silver bullet. It doesn’t solve OS-level mismatches, secrets management, or storage consistency. And unless you’re automating this with a scheduler that understands both environments, you’re bound to introduce friction.
Visibility suffers too. If you don’t have unified monitoring across both cloud and on-prem resources, you lose sight of who’s using what, where jobs are running, and how much capacity is actually available. And when it comes time to shift a workload mid-run – say, aborting a cloud job and rerunning it locally – that transition is rarely seamless. It often involves a series of manual steps and some debugging to replicate the environment with all the dependencies.
You can designate a cloud environment for specific projects with their own set of configurations, but that again, limits flexibility and undermines what you’re trying to accomplish, which is the ability to lift anytime.
A seamless hybrid infrastructure is possible, but it only works if it’s operationalized. That means consistent tooling, unified observability, policy-based scheduling, and minimal human intervention. ClearML supports Cloud Spillover for just that: bursting into the cloud only as needed, supported with rules and logic that control when and how much cloud compute should be used.
From Cloud Back to On-Prem
On the other end of the spectrum, many teams start in the cloud because it’s fast and there is no large investment needed to get started. No waiting for hardware orders, no facility prep, just spin up and go. For a lot of early-stage teams, it’s the only option that makes sense.
But over time, cloud spend grows. Steady workloads become predictable costs, and at some point, the math flips. On-prem begins to look more attractive, especially if you know your AI needs are only going to grow.
Still, shifting to on-prem isn’t trivial. Many on-prem environments weren’t built with high-density GPU workloads in mind. Power delivery becomes a problem. Cooling becomes a problem. And those are just the physical constraints.
Operationally, you’re now managing user access, network segmentation, secure data access, and integration with cloud-native tooling. You’re trying to make sure that whatever worked for you on the cloud, works the same on-prem – and that requires a lot of manual configurations.
You also don’t want to lose the option of going back to the cloud. You may need it for burst capacity, for testing hardware you don’t have on-prem, or for quick tests for which setting up on-prem resources doesn’t make sense.
And, most importantly, you want to ensure that when time comes to move to on-prem, it doesn’t mean starting from scratch or halting research or commercial operations for a substantial amount of time in order to perform the migration process. ClearML’s support for hybrid setups make it easy for admins to control the compute and prioritize resources without affecting AI teams or requiring downtime.
Effortless Multi Cloud
Multi-cloud architectures are increasingly common, and for good reason. Whether you’re using free credits from a new provider to control costs, trying out a managed Kubernetes service, sourcing GPUs your main cloud vendor doesn’t offer, or simply chasing more competitive pricing, the motivations are solid. The execution, however, is rarely simple.
Each cloud comes with its own control plane, quirks, and constraints. Moving workloads across them isn’t just a matter of shifting containers. You’re dealing with different provisioning systems, access controls, networking rules, and monitoring tools. Managing all this through multiple dashboards quickly becomes unmanageable. And while “single pane of glass” sounds great in theory, it’s still out of reach for most teams in practice.
What makes multi-cloud viable is a layer of abstraction across your infrastructure, which lets you submit jobs through a single interface, route them intelligently based on policies or availability, and maintain consistency across execution environments without manual intervention. This also unlocks the ability to move workloads from one cloud to another, or even schedule them across providers, without downtime or developer overhead.
When done right, this approach not only simplifies operations, but can drive significant cost savings by making better use of available resources. True multi-cloud isn’t about having options – it’s about being able to use them without friction. Mix and match your cloud compute behind the scenes using ClearML, and capture cost savings without additional burdensome overhead.
Recommendation: Build for Flexibility, Plan for Change
You can plan where you start. You can guess where you’re going. But growth isn’t linear, and infrastructure needs change faster than most budgets or procurement processes can keep up. That’s why flexibility matters.
Being able to lift and shift workloads – between cloud and on-prem, or across different clusters – gives your team options. And when your team has options, they move faster, ensure continuity, and can deliver more results reliably.
Whether hybrid is your long-term strategy or a temporary bridge, make sure your systems can support it. Don’t just think about compute; think about the control plane that makes it all usable and the scheduler that can route workloads to the best-fitting infrastructure. Because in AI, the only certainty is change.
ClearML enables lift-and-shift in a multi-cloud and hybrid environment by abstracting the infrastructure layer. It allows users to:
- Seamlessly migrate workloads
- Burst to cloud on-demand
- Preserve workload configuration
- Enforce resource policies across heterogenous resources
ClearML’s AI Platform provides a centralized and unified control plane for managing your entire infrastructure, even heterogeneous clusters. AI workloads can run on any available resource, whether on-prem or in the cloud. It’s also simple to manage resources without impacting AI builders – letting IT teams add, update, or remove machines behind the scenes. Want to see it in action? Request a demo at clear.ml/demo.