DevOps-led FinOps consulting
Make cloud spend visible, allocated and predictable.
I help AWS, Azure, hybrid and AI workload teams connect cloud spend to ownership, governance, architecture and business value. The work is practical: assess where spend is going, define who owns it, build the operating model, and create an action plan engineering teams can actually use.
Cost control starts upstream
Cloud cost issues usually start before optimization.
When cloud spend increases, many teams jump straight into rightsizing or cleanup. That can help, but it rarely creates lasting control if visibility, ownership and operating cadence are weak.
Spend is increasing but ownership is unclear.
Tagging exists but allocation is not trusted.
Finance sees the bill after engineering decisions are already made.
Optimization actions happen once, then waste returns.
AI and LLM usage grows faster than budget controls.
Forecasting depends on manual spreadsheets and assumptions.
Operating model
FinOps works when it becomes an operating model.
FinOps is not just cost cutting. It is a way for engineering, finance, product and leadership to make better trade-offs between speed, cost and quality.
Services
Services
10-Day FinOps Assessment
A focused review of current visibility, allocation, governance, forecasting and optimization opportunities. Best first step when the problem is unclear or cross-functional.
View assessmentCloud Cost Visibility & Allocation Setup
Build a practical model for showback, tagging, shared costs, product/team ownership and executive reporting.
View visibility serviceAI Spend Governance Review
Create early controls for LLMs, AI APIs, GPUs, experiments and production AI workloads before usage becomes hard to explain.
View AI governanceFractional FinOps Advisory
Ongoing support for monthly cost reviews, stakeholder cadence, savings backlog, forecasting and executive reporting.
View advisorySource-backed context
Why this matters now
Cloud waste is still material.
Flexera’s 2026 State of the Cloud reporting estimates wasted IaaS/PaaS cloud spend at 29%, showing that cloud cost control remains a practical operating problem, not a one-time cleanup.
AI increases cost complexity.
AI workloads introduce new cost drivers such as tokens, API calls, GPU utilization, experiments, inference patterns and data pipelines.
Cost optimization is part of architecture.
AWS, Azure and Google Cloud all include cost optimization or cloud financial management guidance in their architecture frameworks.
Sources
FinOps Foundation Framework
FinOps operating model and Inform, Optimize, Operate phases.
View sourceFinOps Foundation Phases
Inform, Optimize and Operate are iterative phases.
View sourceWhat is FinOps?
Financial accountability through collaboration across engineering, finance and business teams.
View sourceFlexera 2026 State of the Cloud
Estimated wasted IaaS/PaaS cloud spend increased to 29%.
View sourceFlexera 2026 cloud value report
GenAI moved from experimentation toward everyday public cloud usage.
View sourceFinOps for AI Overview
AI FinOps can track metrics such as cost per API call.
View sourceUse cases
Common situations I help with
SaaS platform spend increased after product growth, but variance ownership was unclear.
Financial services Azure reporting existed, but allocation by business unit was weak.
Healthcare technology teams needed stronger tagging, governance and audit-ready cost views.
Kubernetes platform costs were difficult to map back to namespaces, services and product teams.
AI-enabled product teams moved experiments toward production without spend guardrails.
MSP or cloud consultancy teams needed white-label FinOps support for client accounts.
FAQ
Common questions
What is FinOps?
FinOps is an operating model that helps engineering, finance, product and leadership teams collaborate on technology spend, value and accountability.
Do you only focus on cost cutting?
No. The focus is visibility, allocation, governance, forecasting and better engineering decisions. Optimization is part of the work, but it comes after the team understands ownership and operating cadence.
Which cloud platforms do you support?
AWS, Azure, hybrid environments and AI workloads. The first assessment clarifies the exact scope and data available.
Do you support AI cost governance?
Yes. AI spend governance covers LLM/API usage, GPU or compute patterns, experiment controls, production usage, budget alerts, anomaly response and ownership.
Do you work with MSPs or cloud consultancies?
Yes. Partner and white-label FinOps support is available for MSPs and cloud consulting firms.
Is pricing public?
Pricing depends on scope, environment size, stakeholder needs and deliverables. The first step is a discovery call.
Start with visibility before optimization.
A short assessment can identify whether the issue is spend visibility, allocation, governance, forecasting, optimization or operating model.