Short answer: which one is better?

It depends on the workload and requirements.

Which option does Mevasis recommend?

The Mevasis expert team conducts a needs analysis and recommends the most suitable option.

On-Premises vs Cloud HPC: Cost and Performance Analysis

Q: What should I do to decide?

Contact us for a free technical assessment.

Introduction: Two Different HPC Approaches

When setting up High Performance Computing (HPC) infrastructure, organizations have two fundamental options: on-premises HPC and cloud-based HPC. In on-premises solutions, computing resources are physically deployed at the organization’s own facility and all control belongs to the IT team. In cloud HPC, workloads are run remotely on virtual or bare-metal servers in data centers of providers like AWS, Azure, or Google Cloud.

These two approaches differ significantly in terms of capital cost, operating expenses, latency, data security, and scalability. The right choice depends on the organization’s workload profile, budget structure, and strategic priorities rather than a single “universal answer.”

Key Concepts

On-Premises HPC: Servers, network equipment, and storage units are installed in the organization’s data center or server room. Hardware investment is made upfront (CapEx model); maintenance and upgrade costs belong to the organization.

Cloud HPC: Computing power is rented by selecting on-demand or reserved instance types on the provider’s infrastructure. Payment is made only for resources used (OpEx model). Job schedulers like SLURM, PBS Pro, or LSF can also be run in cloud environments.

Comprehensive Comparison Table

Criterion	On-Premises HPC	Cloud HPC
Initial Cost (CapEx)	High — hardware, license, facility investment required	Low — no upfront cost, pay-as-you-go
5-Year Total Cost of Ownership (TCO)	Generally advantageous with continuous high utilization	Competitive for variable load profiles; can be costly with intensive continuous use
Latency	Very low — MPI communication at microsecond scale on local network	Higher — internet or VPN connection adds latency; InfiniBand cannot be fully matched
Scalability	Limited — hardware capacity is fixed, new investment needed for expansion	Near-unlimited — thousands of cores can be deployed within minutes
Data Security and Compliance	Strong — data never leaves the facility; GDPR, ITAR, classified project compliance is easier	Provider-dependent; encrypted transfer and storage mandatory; some regulatory requirements can become complex
System Control and Customization	Full control — OS, firmware, network topology, cooling preference belong to the organization	Limited — constrained by instance types and configurations offered by the provider
Maintenance and Operational Burden	High — hardware failures, updates, capacity planning are the organization’s responsibility	Low — physical maintenance belongs to the provider; but cloud management expertise is needed
Readiness Time	Long — procurement, installation, and configuration can take weeks to months	Short — new resources can be deployed within minutes
Spot/Preemptible Computing	Not available	Offers significant cost advantage (60–90% discount); workflow must be tolerant of interruptions
Network Bandwidth (MPI Workloads)	Up to 200 Gb/s with InfiniBand HDR/NDR; ultra-low latency	With some providers’ EFA (Elastic Fabric Adapter) up to 100 Gb/s; InfiniBand performance generally not achievable

On-Premises HPC: Strengths

Low Latency, High Bandwidth: InfiniBand networking is critically important for tightly-coupled MPI workloads (CFD simulations, quantum chemistry calculations, seismic imaging). This infrastructure is directly under control in on-premises environments.

Long-Term Cost Advantage: If clusters are running heavily most of the time (>70% utilization rate), the total cost of ownership over 3–5 years falls below cloud rental costs. This difference is particularly noticeable for GPU-intensive workloads.

Full Data Sovereignty: In defense, pharmaceutical R&D, finance, and energy sectors, data must not cross borders. On-premises most naturally meets this requirement.

Customizable Hardware: When special FPGA cards, cooling solutions, or unconventional network topologies are needed, direct intervention on hardware is possible.

On-Premises HPC: Weaknesses

Initial capital investment is high and wrong sizing leads to serious losses. Capacity cannot be expanded during sudden workload spikes; spare capacity sits idle. Employing expert system administrators creates additional operational cost. Hardware refresh cycles (typically 4–6 years) can lead to technology debt.

Cloud HPC: Strengths

Flexible Scaling: Meeting computing peaks that occur a few times a year (e.g., climate model runs, periodic simulations) requires overcapacity on-premises. The cloud meets these peaks within minutes.

Low Entry Barrier: New research groups or start-ups gain immediate access to HPC resources without large CapEx; pilot projects can be quickly launched.

Managed Services: Kubernetes-based job schedulers, parallel file systems (cloud versions of Lustre), and machine learning platforms are offered as ready-made services.

Geographic Distribution: The ability to send jobs to data centers in different regions based on data locality principles is available.

Cloud HPC: Weaknesses

Monthly bills in situations of continuous high utilization can exceed on-premises amortization. Provider dependency (vendor lock-in) creates a strategic risk. Network latencies cause performance loss in tightly-coupled workloads. Data transfer fees (egress fee) can create non-negligible costs for large datasets.

When to Use Which?

Choose On-Premises HPC if:

Your workload is continuous and predictable (cluster occupancy rate >65%)
You run MPI-based tightly-coupled simulations (CFD, FEA, quantum chemistry)
Your data is classified, subject to sector regulation, or cannot leave the country
You have hardware customization requirements (special accelerators, cooling, network topology)
You can plan a 5-year budget and prioritize long-term cost optimization

Choose Cloud HPC if:

Your workload has sudden spikes or is seasonal/periodic in nature
You are developing quick prototypes or starting research projects
Your IT staff is limited and you cannot allocate resources for system maintenance
You are at the pilot stage of a project where computing requirements are not yet clear
Geographic distribution or global access is a strategic priority

Hybrid Approach

For many organizations, the optimal solution is combining both: core and continuous workloads run on the on-premises cluster, while cloud bursting is used to meet peak demands. SLURM’s cloud plugins and tools like AWS ParallelCluster / Azure CycleCloud can automatically manage this hybrid scenario.

5-Year TCO: Example Scenario

A representative comparison for a mid-scale engineering firm:

On-premises: 64-core, 512 GB RAM cluster with InfiniBand network — total 5-year cost including hardware, installation, maintenance, energy, and cooling approximately varies significantly by cluster size and location
Cloud (continuous use): Equivalent capacity with AWS hf6i or Azure HBv4 instances — with reserved instances annual cost is in similar range, can be 2–3x more expensive at on-demand pricing if spot usage is not possible
Cloud (peak usage, 200 hours/month): Well below on-premises — significant advantage since payment is made only for time used

These figures are indicative only; actual costs vary significantly based on workload profile, geography, and contract terms.

Conclusion

The choice between on-premises and cloud HPC is not one-dimensional. On-premises stands out from the perspective of latency sensitivity, data sovereignty, and long-term cost; cloud stands out for flexibility, fast deployment, and variable workloads. Most mature HPC environments are evolving toward a hybrid architecture that combines the advantages of both models.

Make the Right Decision with Mevasis

Mevasis HPC experts identify the most suitable infrastructure model for you by analyzing your workload profile. Contact us for a free technical assessment on on-premises cluster design, cloud HPC integration, or hybrid architecture planning.