Cloud HPC vs. On-Premise: Which Is Right for Your Simulation Workload?

In 2020, Nissan Motor Company made a decision that surprised few engineers who had been watching the industry closely: it committed to moving its entire on-premise HPC fleet for performance and engineering simulations to Oracle Cloud Infrastructure. One of the world's largest automotive manufacturers, with the budget and scale to justify owning its own clusters, chose to hand off the infrastructure entirely.

The same year, a mid-size aerospace supplier was reaching the opposite conclusion. Its simulation workloads were tightly coupled, latency-sensitive, and running around the clock. The overhead of transferring large datasets to the cloud and back was eating into the time savings the platform was supposed to deliver. They expanded their on-premise cluster instead.

Both decisions were correct. That is the central point of this guide.

Cloud HPC and on-premise HPC are not competing philosophies. They are different tools optimized for different conditions. Understanding which conditions apply to your team is the only question that matters.

First, What Is HPC in the Context of Simulation?

High-Performance Computing (HPC) refers to the use of parallel processing, typically across multiple CPU or GPU nodes connected by high-speed interconnects, to solve computational problems that a single workstation cannot handle in a reasonable timeframe.

For simulation engineers, HPC is the engine that makes high-fidelity analysis possible. A mesh-resolved CFD simulation of a full vehicle at highway speed may involve 100 million cells. A nonlinear crash analysis of a vehicle body runs hundreds of load cases simultaneously. A multiphysics battery thermal runaway simulation couples fluid, structural, and electrochemical solvers across millions of elements. None of these run on a laptop.

The global HPC market was valued at $41.64 billion in 2024, with 42% of manufacturing enterprises leveraging HPC specifically for simulation and modeling workloads. The infrastructure that runs those simulations comes in two fundamental forms: hardware you own and operate (on-premise), or compute you access on demand through a provider (cloud).

The Case for On-Premise HPC

On-premise HPC is the traditional model. Your organization owns the hardware, houses it in a data center or server room, manages the software stack, and controls every aspect of the environment. For the right workloads, this model has real advantages that cloud cannot fully replicate.

Performance for Tightly Coupled Workloads

The most important advantage of on-premise infrastructure is network latency. Many tightly coupled HPC workloads require microsecond-level latency to maintain efficient node-to-node communication. Dedicated on-premise interconnects such as InfiniBand consistently deliver lower, more predictable latency than shared, virtualized cloud networks.

This matters specifically for simulation workloads where processes running on different nodes and exchange data at every solver iteration. CFD simulations, structural crash analyses, and molecular dynamics are all tightly coupled in this sense. Even small increases in inter-node latency can degrade scaling efficiency significantly, meaning that adding more compute nodes produces diminishing returns.

Consistency and Predictability

Engineering teams depend on predictable job completion times to maintain development cadence. On-premise infrastructure avoids the performance variability common in shared or multi-tenant cloud environments, providing stable throughput and reliable scheduling.

When a simulation job needs to be completed before a design review, variability in runtime is not just an inconvenience. It is a program risk. On-premise clusters deliver consistent performance because the resources are dedicated and the environment is controlled.

Data Gravity

Large, frequently updated datasets create both cost and performance friction in cloud environments due to ingress, egress, and repeated transfer cycles. On-premise HPC eliminates data gravity issues by keeping compute physically close to the data, reducing bottlenecks and accelerating iteration.

Simulation datasets are large. A full vehicle CFD case can involve hundreds of gigabytes of mesh and results data. When teams are running dozens of design variants per day, the overhead of moving that data to and from the cloud accumulates quickly.

Software Stack Control

Advanced HPC workloads often rely on highly optimized compilers, MPI libraries, drivers, and workload schedulers tailored to specific applications. On-premise environments provide full control over stack configuration and tuning, enabling deeper optimization than standard cloud images typically allow. For organizations running legacy simulation software with complex licensing models, or those requiring specific hardware-software co-optimization, on-premise control is a meaningful technical advantage.

When On-Premise Makes Sense

On-premise is likely the right choice when your simulation workloads are large, tightly coupled, and running continuously at high utilization; when dataset sizes make cloud data transfer impractical; when you require consistent, predictable runtimes; and when you have the in-house expertise to manage and optimize the environment.

Implementation in a dedicated data center is only worthwhile if operation is ensured over an extended period with high utilization. The economics of on-premise hardware only work when the cluster is busy. If utilization drops, you are paying for idle capacity.

The Case for Cloud HPC

Cloud HPC flips the ownership model. Compute resources are provisioned on demand from providers like AWS, Microsoft Azure, Google Cloud, or simulation-focused platforms like Rescale or SimScale. You pay for what you use, access the latest hardware without capital expenditure, and scale to any job size in minutes.

Elasticity and On-Demand Scale

The single most compelling advantage of cloud HPC is the ability to scale without constraint. An on-premise cluster has a fixed ceiling. Cloud has no practical ceiling for most engineering workloads.

This matters most in two scenarios: peak demand and design exploration. When a product development cycle hits a critical deadline and the team needs to run 500 simulation variants overnight, cloud HPC absorbs that spike without requiring additional hardware investment. Rescale reported a customer case where the largest job processed showed a 5 to 10 times increase in speed of computing by accessing cloud HPC on demand.

No Capital Expenditure

On-premise HPC clusters are expensive to acquire, expensive to maintain, and depreciate over time. Cloud HPC converts that capital expenditure into an operational one, paying only for compute as it is consumed.

A detailed TCO analysis for a manufacturing customer comparing a 512-node on-premise cluster against an equivalent cloud HPC environment, including Azure consumption for an average of 70,000 core-hours per week and an annual platform subscription for 15 users, resulted in nearly identical three-year totals: 681,000 euros for cloud versus 685,000 euros for on-premise. At comparable utilization levels, the cost difference is often smaller than expected. Where cloud wins on cost is at variable or lower utilization, where you are not paying for idle hardware.

Access to Latest Hardware

Cloud providers continuously refresh their hardware with the latest CPU and GPU generations. On-premise clusters are typically refreshed every three to five years. For simulation workloads that benefit from GPU acceleration, particularly those adopting AI-assisted solvers, cloud access to NVIDIA H100 or future GPU generations without a procurement cycle is a real advantage.

In March 2025, Microsoft announced a broad expansion of Azure AI and HPC capabilities through a multi-year collaboration with NVIDIA to deploy larger GPU-accelerated clusters across the Azure cloud for research and enterprise workloads.

Lower Barrier to Entry

Cloud HPC has fundamentally changed who can access simulation at scale. A startup, a university research group, or a mid-size engineering consultancy can run a 10,000-core CFD job without owning a single server. In 2024, 72% of Fortune 500 companies had adopted cloud-based HPC for advanced analytics and engineering simulations.

The barrier is no longer hardware. It is knowing how to set the workload up correctly, which is precisely the skill gap that SimOps-aligned practices are designed to address.

When Cloud HPC Makes Sense

Cloud is likely the right choice when workloads are variable or bursty, when utilization of an on-premise cluster would be too low to justify the investment, when the team needs to scale quickly to meet project deadlines, when workloads are loosely coupled and less sensitive to inter-node latency, and when access to the latest hardware matters more than stack optimization. In most cases, companies plan calculations on a temporary basis. Therefore, operation in the cloud is usually more cost-effective.

The Hybrid Model: The Practical Reality

The most common architecture among mature simulation organizations is neither pure cloud nor pure on-premise. It is hybrid.

A typical hybrid model works like this: a stable, continuously utilized on-premise cluster handles the baseline workload, the daily simulation runs, the tightly coupled jobs that need low latency and consistent performance. Cloud bursting handles the peaks: deadline crunches, large-scale design of experiments, parallel variant studies that temporarily need more cores than the on-premise cluster can provide.

Cloud bursting has allowed companies to leverage cloud resources during peak demand periods, mitigating the need to invest in expensive, idle on-premise hardware. This approach ensures scalability, flexibility, and cost-efficiency, enabling organizations to handle spikes in workload without compromising performance or incurring prohibitive costs.

The hybrid model has become the dominant pattern. In 2024, 49% of organizations were utilizing a combination of on-premise and cloud HPC solutions. The on-premise cluster provides the performance floor; the cloud provides the ceiling.

A Decision Framework: Cloud HPC vs on-premise

Rather than asking "cloud or on-premise," experienced HPC buyers frame the question differently. Smart organizations increasingly assess HPC investments with a view toward workload, focusing on measurable outcomes such as cost per job, cost per simulation, time to results, and overall throughput.

The following questions help map workload characteristics to infrastructure choice:

Coupling and latency sensitivity. If your simulation requires constant inter-node communication at every solver iteration, prioritize on-premise or dedicated cloud HPC configurations with InfiniBand. If your workload is embarrassingly parallel, that is, if jobs run independently with little node-to-node communication, cloud scales efficiently.

Dataset size and transfer frequency. If you are moving hundreds of gigabytes of mesh and results data multiple times per day, cloud data transfer costs and latency will hurt you. If datasets are modest or jobs run infrequently, cloud is unlikely to have a data gravity problem.

Utilization patterns. Run the numbers. If your team will keep a cluster busy at above 70% utilization year-round, on-premise TCO is competitive. If utilization is variable, peaks around project milestones, and drops significantly between them, cloud economics will be better.

Refresh cycle requirements. If your simulation software benefits significantly from the latest GPU architectures, and you need to refresh hardware frequently, cloud removes that procurement overhead. If your workload is CPU-bound and well-optimized on existing hardware, on-premise serves you longer per hardware generation.

In-house expertise. On-premise HPC requires specialists: system administrators, HPC engineers, and Linux expertise. Skills for running HPC on Linux in a competitive labor market can be difficult to acquire and retain. Cloud platforms abstract much of that complexity, though they introduce their own operational learning curve.

What This Means for Simulation Teams

The decision between cloud HPC vs on-premise is ultimately a workload characterization problem. Get the workload characterization right, and the infrastructure decision follows naturally.

What is less straightforward is the operational side: how simulation workflows, job scheduling, data management, software licensing, and team practices and processes adapt to each environment. These are not purely infrastructure questions. They are process and culture questions.

This is exactly where the gap between DevOps and simulation engineering has been most costly. Software development built shared tooling, shared practices, and shared vocabulary for managing infrastructure decisions like these. Simulation engineering has largely solved them in isolation, team by team, organization by organization, with no common framework to draw on.

That is the problem SimOps is built to address. Not just the hardware question, but the operational practices around simulation and HPC that determine whether any infrastructure choice actually delivers results.

Key Takeaways

On-premise HPC delivers lower latency, predictable performance, and data locality advantages for tightly coupled, continuously running simulation workloads at high utilization. Cloud HPC delivers elasticity, no capital expenditure, and access to the latest hardware for variable, bursty, or loosely coupled workloads. Hybrid architectures, which combine a stable on-premise baseline with cloud bursting for peaks, represent the most common pattern among mature simulation organizations. The right answer depends on workload characterization: coupling requirements, dataset sizes, utilization patterns, and team expertise. Cost per simulation, not infrastructure cost in isolation, is the correct metric for evaluating either option.

References

1. Nor-Tech. (2025). Cloud HPC vs On-Prem HPC Performance: What Serious Buyers Must Know. nor-tech.com

2. Open Telekom Cloud. HPC Cluster: Cloud or On-Premise? open-telekom-cloud.com

3. Rescale. Cloud HPC Solutions. rescale.com

4. Nor-Tech. (2025). Cloud v. On Prem HPC: A Strong Comparison Metric. nor-tech.com

5. UberCloud / SimR. (2023). Squashing Total Cost Rumors of In-House vs. Cloud Computing. blog.simr.com

6. nAG. (2024). Revolutionising HPC: Bursting from Cloud to On-Premise. nag.com

7. Computer Weekly. How Long Until Cloud Becomes the Preferred Environment to Run HPC Workloads? computerweekly.com

8. Global Growth Insights. (2025). High-Performance Computing (HPC) Market Size, 2034. globalgrowthinsights.com

9. Market Growth Reports. (2024). Cloud High Performance Computing (HPC) Market Size. marketgrowthreports.com

10. WiseGuy Reports. (2025). Cloud HPC Market: Trends & Growth Analysis 2035. wiseguyreports.com

About SimOps

Software development was transformed when teams stopped treating infrastructure as an afterthought and started building shared practices around it. That's what DevOps did for code. SimOps is doing the same thing for simulation.

Simulation and HPC have long operated in silos: different tools, different teams, different workflows, with no shared language or common standards. SimOps exists to change that. We're building a framework and a community where simulation engineers, HPC specialists, and CAE teams can work from the same playbook, share best practices, and push the field forward together.

If that mission resonates with you, this series is a starting point. And the community is where the conversation continues.