From the Research Universities edition of October 17, 2026
Hyperion Research previews a cost, value and ROI model to compare on-premises, cloud and hybrid scientific computing
Hyperion Research (HPC User Forum, HLRS) · Higher education and research computing · International (HPC User Forum, October 2026)
- Publisher
- HPC User Forum
- Original publication
- October 2026
- Source retrieved
- 2026-10-18
What happened
Slides by Jaclyn Ludema and Mark Nossokoff describe an Excel-based decision-support model intended to compare TCO, societal value and ROI for on-premises, cloud and hybrid compute environments. They list hidden costs (software and licensing, power and cooling, networking, data egress, staff labour, training, floor space, overhead) and say early-stage projects lack precise workload or value figures. The model is not yet released: next steps are a name, a global survey of default costs and a spreadsheet for alpha and beta testing within 3–6 months.
Why it matters
Research computing directors and CIOs face AI-driven GPU demand and need a defensible way to compare environments. This signals an analyst-built tool in development, with no validation data yet.
Evidence and measured results
Opened the 11-slide PDF on 18 Oct 2026. Slides describe the model's structure and inputs; no results, case studies or default cost values are shown. The exact slide date is not stated beyond October 2026.
Limitations and uncertainty
Conference slides from an analyst firm that sells research to the HPC market; no outcomes, no method detail beyond a diagram, tool not yet available, default costs unspecified. Publication day unknown so publicationDate is null.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-18; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Research computing costs sit in separate budgets, so on-premises, cloud and hybrid options are hard to compare and justify.
- Stakeholders
- Chief information officer
- Research computing director
- Vice president for research
- Finance and budget officers
- Discovery questions
- Which costs for research computing are tracked, and where?
- How do you value time-to-solution for AI projects?
- Who signs off on an on-premises versus cloud decision?
- Value hypothesis
- A shared cost-and-value model may reduce disagreement among technical, budget and governance teams; the slides give no evidence of that effect yet.
- Potential engagement
- A short cost-baselining workshop using the customer's own CPU, GPU and storage figures.
- Unsupported claims
- That the Hyperion model improves decisions
- Any savings or ROI figure
- That the tool is available or validated
Pre-sales engineering
Role takeaway
- Fit
- Fits early-stage AI infrastructure planning where workload estimates are uncertain; the tool itself is not yet usable.
- Architecture and integration
- Use the slide's cost categories as the template for a project-level TCO comparison across on-premises, cloud and hybrid.
- Prerequisites
- Workload estimates in CPU and GPU hours and storage
- Software licensing and support costs
- Power, networking and egress figures
- Constraints
- Default cost values not published
- Early-stage workload uncertainty
- Model release months away
- Security
- Not covered by the source; add data residency and access requirements separately when comparing environments.
- Proof of value
- Rebuild one recent project's total cost with the listed hidden-cost categories and compare to the budget as booked
- Test sensitivity of the comparison to workload uncertainty
- Check whether hidden costs change the preferred environment
Delivery
Role takeaway
- Work
- Collect cost inputs across budgets and agree a single comparison method among technical, finance and governance teams.
- Dependencies
- Finance data access
- Workload estimates
- Executive sponsor
- Ownership
- Research computing director with a finance partner.
- Skills and adoption
- Researchers and project managers need to describe workload characteristics and value in terms the model can use.
- Governance checkpoints
- Agree the cost categories and assumptions
- Review inputs at project milestones
- Record how value was estimated
- Acceptance criteria
- Proposed: cost comparison includes all listed hidden-cost categories
- Proposed: assumptions are documented and signed off by finance
- Proposed: workload uncertainty ranges are stated
- Risks
- Treating an unvalidated tool as authoritative
- Subjective societal value figures dominate the result
- Cost inputs left stale
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Frames on-premises versus cloud versus hybrid as a project-level decision depending on workload, software and data access, rather than a single institutional answer.
Governance
Who approves, reviews and stays accountable for outcomes?
Slides note technical, budget and governance teams view TCO differently; a shared model is proposed as a common basis. Cost value claims remain unvalidated.
Security and privacy
What data, permissions and controls need testing?
Not covered; data egress and data access appear only as cost or requirement items.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Staff labour, help desk and training are listed as hidden costs, which affects how research-support workloads are budgeted.
Procurement
What should contracts, pricing and exit terms secure?
Supports comparing hardware purchase, cloud and managed services on a full-cost basis; no price data supplied.
Operating model
Which teams own the service once it runs?
Positions the compute-environment choice as a shared technical, budget and governance decision.
Publication history
- 2026-10-17Research Universities · Issue 396 resources
Stable resource ID: hyperion-scientific-computing-cost-value-roi-model-202610