Back On Prem. Forward On Value

If your latest renewal made you rethink the math, you are not alone. Forced model changes, renewal shock and hidden upgrade costs are draining control. Selective repatriation fixes the root cause. Keep elastic on cloud. Move steady or data heavy workloads on prem. When hybrid is right we map placement across cloud and on prem for cost and performance. The aim is predictability and the freedom to adopt features the moment they ship.

What Is Driving The Shift?

Three consistent drivers tip teams back on prem.

• Forced model changes reduce flexibility and push long commitments

• Data sovereignty and repatriation?

• Performance limitations of public cloud as push towards inferior kit

Own the stack and you choose your refresh window and the lifetime value of the asset. Hammer helps you do that with the right platforms, the right configuration and a deployment process that arrives ready to run.

Make Budgets Defensible

On prem spend is planned against a known lifecycle. There is no surprise renewal and no lock in. Cloud remains the right answer for burst and experimentation. On prem takes the steady and the sensitive. The result is a budget you can defend over three and five years, backed by a clear bill of materials, SOW, warranty options and support coverage through Hammer.

Feature Access Becomes An Edge

Direct hardware access cuts delay. Use AMD Instinct with AMD ROCm and adopt capability as soon as your teams validate it. Early access compounds across model updates, tuning and deployment. With Hammer, you are not waiting on provider roadmaps, you are choosing the hardware generation and timing that matches your release cycle.

The AI Cost Curve In Plain Numbers

Cloud GPU bills stack up fast. The deck shows a three year cloud GPU cost that can be 5 to 8x an on prem build with similar accelerators. Typical outcomes show about a 14 month payback and about 247% ROI, with about 715k dollars saved by year three and about 1.5m dollars by year five. Treat these as starting points, then model your own duty cycle and power profile. Hammer runs the TCO and design work with you so the numbers reflect your workloads, utilisation and constraints.

A Reference Architecture That Travels Well

Pick platforms that match the work.

• Servers. Mitac platforms for AI, HPC and enterprise use cases

• Compute. AMD EPYC for core scale and price to performance

• GPUs. AMD Instinct accelerators sized for training or inference

• Networking and power. Design for efficiency to lower OpEx across the lifecycle

• Warranty and support. Plan coverage with clear options

Hammer sources the stack, validates fit for the workload and builds it into a deployment plan you can execute with confidence.

Make It Install Ready Before It Ships

Pre configured builds reduce risk. Burn in, cabling, labelling, documentation and QA mean racks arrive ready to install. Your teams focus on go live with fewer unknowns. Hammer delivers data centre ready servers and pre configured systems so you are not assembling outcomes on site under pressure.

How The Engagement Works

Consult to map workloads and targets. Design with a clear bill of materials and power plan. Pre configure in a controlled facility with test evidence and photos. Deliver to site with install support. Back it with warranty and services. That flow gets you from spreadsheet to running rack fast, with Hammer owning the coordination across hardware, configuration and delivery.

Back on prem is not anti cloud. It is right sizing for cost and control. If renewals are volatile or features are gated, your next step is simple.

FAQ

Why are companies moving some workloads back on-prem?
Many teams are reacting to forced pricing models, renewal shock and unexpected upgrade costs in the cloud. These factors make long-term budgeting difficult. By selectively repatriating steady or data-heavy workloads, organizations regain cost predictability and control while still keeping elastic or experimental work in the cloud.

What is selective repatriation in practice?
Selective repatriation means moving only the right workloads back on prem, not everything. Steady, predictable or sensitive workloads are good candidates. Burst, experimental or highly variable workloads typically stay in the cloud. This balanced approach avoids extremes and focuses on right-sizing infrastructure for cost and performance.

How does on-prem infrastructure make budgets more defensible?
On-prem spend is planned against a known hardware lifecycle, usually three to five years. There are no surprise renewals or forced migrations. Teams choose when to refresh assets and can clearly explain costs over time, making financial planning and internal approvals far easier. Hammer supports this with design documentation, clear warranty options and an install-ready delivery plan.

Is going back on prem anti-cloud?
No. The model described is explicitly hybrid. Cloud remains ideal for elasticity, experimentation and short-lived demand. On prem is used where predictability, data gravity or cost control matter most. The goal is flexibility and control, not replacing the cloud entirely.

How does direct hardware access create a feature advantage?
Owning the stack removes delays caused by provider roadmaps and gated features. When new capabilities become available at the hardware level, teams can validate and deploy them immediately. Over time, early access compounds across tuning, optimization and deployment cycles. Hammer helps you align hardware choices to your roadmap so you can adopt features when you are ready, not when a provider allows it.

Are the cost savings compared to cloud realistic?
The numbers shared are typical starting points, not guarantees. Many teams see cloud GPU costs multiply over multi-year horizons, while on-prem builds amortise over time. Actual results depend on workload duty cycle, utilisation, power efficiency and refresh strategy, which is why modelling is critical. Hammer can run the model with your assumptions and produce a three and five year view you can defend internally.

What kinds of workloads benefit most from on-prem AI infrastructure?
Long-running training jobs, heavy inference and data-intensive workloads often benefit most. These workloads tend to have predictable utilisation and high cloud egress or compute costs. On prem also suits sensitive data scenarios where tighter control and locality are required.

What does an “install-ready” deployment actually reduce?
Pre-configured systems reduce operational risk at delivery. Burn-in testing, cabling, labelling and documentation catch issues early. When racks arrive ready to install, internal teams spend less time troubleshooting and more time getting models into production quickly. Hammer’s rapid deployment approach is built around this install-ready standard.