Who Should You Hire for AI and Cloud Financial Management?

Several different kinds of firm solve adjacent problems in this space, and they are routinely confused for one another. This page describes the categories plainly, including the situations where we are not the right choice.

Everything below is drawn from each firm's own published positioning. If we have described your firm inaccurately, write to us and we will correct it.

Last updated August 2026

What are the options at a glance?

  
      Type of firm
      What they do
      Choose this when
      Example
    
      Managed service
      Runs the FinOps function on your behalf, ongoing
      You would rather buy the outcome than build the function
      ViaNova
    
      Specialist AI economics consultancy
      Deep platform-specific AI cost expertise
      Mid-market, single-cloud, platform-native depth matters most
      Diablo Technology Services
    
      Platforms and tooling
      Collect, normalize, allocate and visualize cost data
      You cannot see the data, or assembling it consumes your team
      Cloudability, CloudZero, Finout, Vantage
    
      Large transformation firms
      Enterprise-wide change programs, FinOps as one workstream
      You need scale across many geographies at once
      Deloitte, Accenture, IBM, PwC
    
      Practice-building consultancy
      Builds the capability alongside your teams, then hands it over
      You want the capability to exist internally afterward
      FinOptik

FinOps as a managed service

Example: ViaNova. Their published positioning is a FinOps-led managed service partner for global enterprises, connecting cloud, SaaS and AI spend to unit economics, cost-to-serve and margin. In their own words: ongoing, outcome-based, never a one-off project, run every month for the life of the contract. They describe the model as replacing the cost of building an in-house FinOps team with an outcome-based subscription tracked against a contractual baseline, delivered through forward-deployed experts, agentic AI and tooling.
Choose this category when you do not intend to build the capability internally, you would rather buy the outcome than the function, and an ongoing operating expense is easier to justify than a project plus headcount. For organizations without the appetite or the headcount to run a FinOps practice, this is a rational answer.
This is not what we do. We build the practice and hand it over. If a client still needs us to produce their reporting a year later, our engagement did not do what it was hired to do. That is a different product rather than a better one, and if the managed service model fits your organization, that is the category to look at.

Specialist AI economics consultancies

Example: Diablo Technology Services. Two co-founders, both Microsoft veterans, one a former Microsoft Cloud Solutions Architect who co-presented Microsoft's Azure Design Blueprint Series on Responsible AI, the other a former Senior Technology Strategist. They have published on the Microsoft FinOps Blog on Azure OpenAI token economics. Their work combines FinOps with AI unit economics, cost allocation using FOCUS and the Microsoft FinOps Toolkit, Cloud Centres of Excellence, and Responsible AI policy. Their stated focus is the mid-market.
Choose this category when you are Azure-centric, mid-market rather than large enterprise, and want practitioners with deep platform lineage in the cloud you actually run.
Where we differ. We work with large enterprises, typically five million dollars or more in annual cloud spend, across AWS, Azure and Google Cloud, and we are tool-agnostic because we sell none of them. Our lineage is finance rather than cloud architecture, which shapes who we are most credible in front of.
A note on this category. It is small. Searching for AI economics consultancies surfaces a great deal of crypto tokenomics work, which is an entirely different discipline concerned with blockchain token supply design rather than AI inference costs. Verifiable specialists in enterprise AI cost economics remain few, which is a fair signal of how new the field is.

Platforms and tooling

Examples: Cloudability and Apptio, CloudZero, Finout, Vantage, Ternary, Flexera. These collect, normalize, allocate and visualize cost and usage data, and several now cover AI and SaaS alongside cloud.
Choose this category when the constraint is that you cannot see the data, or that assembling it consumes your team every month.
This is complementary rather than competing. A platform answers how to collect, normalize, allocate and visualize. It does not answer what the organization should measure, who should own it, how the economics should work, or how the operating model holds together when a business unit disputes its allocation. Most of our clients run one of these platforms, and we work with whichever one is already in place.

Larger transformation firms

Examples: Deloitte, Accenture, IBM Consulting, PwC, EY, KPMG, McKinsey, BCG. Each now publishes on AI economics and tokenomics, covering cost drivers, unit economics, architecture choices and governance as part of broader transformation work.
Choose this category when FinOps is one workstream inside an enterprise-wide transformation, you need scale across many geographies simultaneously, or your procurement process favours firms of that size.
Where we differ. We take a small number of engagements at a time, and the people who sell the work do the work. That is a real constraint. If you need forty consultants deployed across six countries next quarter, we are not the answer.

Where does FinOptik fit?

We build cloud and AI financial management practices inside large enterprises, working alongside your teams, and we leave when the organization can run it without us.
The lineage is finance first. Rich Hoyer's background is FP&A and twenty-seven years at the intersection of enterprise finance and technology before crossing into cloud. He served on the FinOps Foundation Governing Board and Technical Advisory Council, co-authored the Google Cloud FinOps global operating model, and has built three FinOps practices from inception.
We are likely the right choice if the constraint is that Finance and Engineering cannot agree on what the numbers mean, you want the capability to exist internally when the engagement ends, and you need someone credible in front of a CFO and a principal architect in the same week.
We are likely the wrong choice if you want the function operated for you indefinitely, you need a platform rather than a practice, you are mid-market rather than large enterprise, or you need a large deployed team across multiple regions.

How do we start?

A thirty-minute call. If the answer is that another firm on this page fits your situation better, we will say so.

sales@finoptik.io

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