Revenium is included in Forrester’s The Cloud Cost Management And Optimization Solutions Landscape, Q3 2026, and we’re glad to be there. What stood out to us most was where the report puts AI. Forrester lists AI cost management among the use cases buyers now bring to cost management vendors. That tells you AI spend has moved out of the experiment budget and onto the desks of FinOps teams who are expected to own it.
The problem Forrester names
Forrester describes the core challenge this way:
“Technology leaders struggle to allocate, forecast, and optimize AI costs, but their biggest challenge is connecting spending to business value. Unlike traditional cloud services, AI introduces new consumption models and pricing structures that make visibility and attribution difficult.”
That matches what we hear from buyers. When we analyzed all of our enterprise AI buyer conversations, the value question came up every single month.
Part of the reason is that AI costs behave differently from cloud costs. With cloud, you provision a resource and pay for the time it runs, so cost follows capacity. With AI, cost follows behavior. A single user request can fan out into many model calls, tool calls, and retries, and the price of each one depends on the model, the token type, and how much context it carries. It gets harder when one team’s agent calls another team’s agent and the bill lands on whoever owns the API key. Who pays the bill when one agent calls another doesn’t have a default answer.
All of this strains the attribution model FinOps was built on, and it’s happening faster than it did with cloud. As we’ve written before, cloud spend took years to get out of control, and AI spend will do it in months.
Cheaper tokens don’t answer the value question
Forrester lists the optimization methods FinOps teams are adopting: model routing, KV caching, prompt guardrails, capacity discounting, and license rightsizing. They all work. We recommend most of them.
Each one lowers the price of a unit of AI work. None of them tells you whether that unit was worth paying for in the first place. A router can pick the cheapest capable model for a request, but it can’t tell you who the request was for, which workflow it served, or whether it should have run at all. We made that case in more detail in Why Your AI Router Isn’t Your AI Cost Strategy.
The report also notes that vendors are building AI cost capabilities around visibility, allocation, and forecasting. Those capabilities matter. But they look backward. A dashboard shows you last month, and a forecast projects last month forward. With autonomous agents, the money is usually spent before anyone opens the report.
Why agents make this urgent
We call the result Agent Debt. Agent Debt is what happens when an autonomous workflow runs without an owner, a pre-execution budget, or a stop condition. Every run adds a little exposure, and nobody sees the total until the invoice arrives.
Timing is the heart of it. People set budgets once a quarter, while agents spend in milliseconds with no approval step in between. A spending review that happens after the fact is really an audit. It can explain a loss. It can’t prevent one.
That’s why connecting spend to value becomes an operating problem. You need to know which agent ran, for which customer, toward what outcome, and at what cost per result. And you need to know it while the work is still happening, so you can decide whether it should keep running.
How Revenium approaches it
We built Revenium as an AI Economic Control System. It works in three layers that map to the problem Forrester describes.
Economic Observability ties every model call, agent action, and tool invocation to the customer, workflow, feature, and team that triggered it. That’s the foundation for closing the visibility and attribution gap, and The Four Fields That Make Your LLM Dashboard Actually Useful walks through the data model behind it.
Economic Intelligence puts the result next to the cost. AI Outcomes links each agent execution to its business result, such as a conversion, a deflection, or an escalation, and calculates ROI at the workflow level. This is where spend finally connects to value.
Economic Control watches economic behavior as it happens. It applies budgets and guardrails and stops unprofitable usage before it hits the P&L. We explain why that enforcement has to live at the transaction level in The Guardrails Economists Want Have to Run Somewhere.
Our team built cost management infrastructure for the cloud era, and we watched how long it took enterprises to get cloud spend under control. AI is moving much faster, and the controls need to keep up.
What this means for FinOps teams
Because AI spend is committed at runtime, governance has to run at runtime too. Teams that only report on AI costs will keep explaining variances they had no way to stop. Teams that tie cost to outcomes and enforce limits as the work happens will be able to answer the question Forrester calls the biggest challenge: what did the spend actually deliver?
Forrester clients can read the full report, The Cloud Cost Management And Optimization Solutions Landscape, Q3 2026 [ADD FORRESTER LINK]. To see how Revenium connects AI spend to business value, book a demo or try it yourself for free.
Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications.
