Services
What FinOptik Does
FinOptik builds cloud and AI financial management practices inside large enterprises. Our core work is the people and process side: the operating models, governance structures, and accountability frameworks that get Finance and Engineering working from the same numbers.
We do not sell software, resell cloud, or take rebates. Engagements run as fixed scope or monthly retainer, available through AWS Marketplace or direct.
Our business can be broken into 4 Key Services.
FinOps Strategy for Cloud and AI
Cloud or AI business case for Enterprises
Ongoing Cloud and AI Consumption Management
Financial Diligence for AI Infrastructure Decisions
1. FinOps Strategy for Cloud and AI
FinOps Strategy for Cloud and AI.
This is the majority of our work.
Most organizations do not have a data problem. They have a translation problem. Engineering talks in instances, tokens, and utilization. Finance talks in accruals, variance, materiality, and forecast confidence. Both are describing the same spend and neither fully trusts the other's version of it. No dashboard closes that gap, because the gap is cultural rather than informational.
We sit between the two organizations and build the structure that closes it, then teach each side enough of the other's language that they stop needing a translator.
Timeline. Roughly twenty-six weeks for a working foundation. Full maturity across a very large enterprise takes years, frequently two or three, because extending real cost accountability across dozens of business units is organizational work and organizational work does not compress.
→ FinOps for AI for the AI-specific version of this work.
What gets built:
The work spans five layers of our Cloud + AI Financial Management Architecture. Where an engagement starts depends on what already exists.
Foundational and Roadmap
Defined objectives and desired FinOps CCOE accountability structure
Cross-collaboration enablement between Finance and the Office of the CTO
Assessments, executive prioritization, and socialization guidance
Architecting for Reportability
Allocation, segmentation, and chargeback of cloud and AI spend, shared costs, and SaaS
Standardization and governance
Minimizing post-processing of financial reports
Cost Efficiency and TCO
Scoping workloads to enable migrations or decommissioning
Token and consumption optimization
Cross-company culture of cost consideration during architecture design
Planning, Budgeting and Forecasting
Business cases for migrations, modernizations, and AI
Forecast variance analysis and segment forecasting
Optimized configurations of CFM platforms
Corporate Value Impact
Ongoing cloud and AI consumption management
Unit metrics and business value insight
Positive cultural adoption, real-time decisioning, and accountability
Custom playbook creation, workload management, and automation including anomaly alerting
2. cloud or AI business case for enterprises
Cloud or AI business case for Enterprises
We build financial cases for migrations, modernizations, GenAI deployments, and contractual commitments.
NPV-based TCO comparison, migration versus no migration
Cost assessment of the solutioned reference architecture
Cash flow model, including the timing of when cost avoidance actually occurs in cash
Projection of migration costs and the double bubble period
Accrual P&L and EBITDA analysis
Projections of cost avoidance and credits gained from cancelled contracts
Consumption business cases built against contractual EDP terms
Sensitivity analysis and break-even thresholds
For AI specifically: full-iceberg cost modeling covering tokens, compute, data preparation, fine-tuning, retraining, and integration effort
The output is a board-ready model and presentation your finance organization can defend without rework.
3. Ongoing Consumption Management
Ongoing Consumption Management
The continuous work with no natural end point, on a monthly retainer.
Unit economics tracking, forecast variance analysis, budgeting governance, anomaly alerting, commitment and discount coverage across EDPs, RIs, Savings Plans and CUDs, model selection trade-off analysis, rightsizing and workload consolidation, license expense analysis, board-ready monthly reporting.
Monthly, cancellable.
4. Financial Diligence for AI Infrastructure Decisions
Financial Diligence for AI Infrastructure Decisions
A different engagement for a different buyer.
As AI companies commit to multi-year, multi-million dollar data center and GPU infrastructure agreements, the financial stakes of a single site decision now rival a Series B round. FinOptik brings cloud financial operations rigor to physical infrastructure decisions, helping AI companies and their investors underwrite site selection the way they would underwrite any major capital commitment.
Total cost of infrastructure modeling. Translating dollar-per-kilowatt pricing, power pass-through, PUE, and escalators into a real multi-year cost forecast, comparable across sites and against cloud and on-demand alternatives.
Covenant and counterparty risk assessment. Evaluating operator financial strength, lease structure, and site delivery risk from a buyer's perspective.
Capacity versus burn alignment. Right-sizing committed infrastructure against the actual training and inference roadmap and runway, so companies do not over-commit ahead of revenue or under-provision ahead of a raise.
Board and investor-ready summaries. Translating technical site specs into the financial narrative a board or investor needs in order to approve a five to ten year infrastructure commitment.
An independent, numbers-first view of what an organization is actually signing up for, before it is signed.