Traditional FinOps, with its rigid cadences, daily anomaly checks, weekly cost reviews, quarterly budget reconciliations, and annual reports, has worked well in relatively static single-cloud environments. That is all changing.
In the new era of ephemeral infrastructure and agentic AI, where resources exist for a matter of minutes or seconds, FinOps is becoming less relevant. With agentic AI, decisions about provisioning, scaling, and deprovisioning are all being made by autonomous agents, not humans.
In this article we’ll explain why FinOps is failing and propose a reimagined FinOps paradigm:
infrastructure platform engineering (IPE).
IPE proactively embeds cost governance into infrastructure deployment and automates policy-driven cloud economics.
Why does FinOps flounder when faced with ephemeral and AI-based infrastructure?
FinOps was designed as a fix-it framework for the huge cloud waste problem (nearly a third of enterprise cloud spend). But studies show that instead of abating, cloud waste is rising.
Conservative reports indicate an alarming $225.9 billion in wasted spend for 2024. That’s despite a growing FinOps adoption rate: The FinOps Foundation’s report shows 76% uptake. Clearly, the problem isn’t the idea behind FinOps, but its implementation.
Let’s take a quick look at what’s collectively pushing FinOps towards early retirement (if radical transformations aren’t made):
- Ephemeral infrastructure are extremely short-lived computing resources, with lifespans controlled by automated cloud systems. Unlike long-running servers, ephemeral resources (containers that spin up for a task and vanish after completion, serverless functions that run only when triggered, and inference pods that scale to zero), are largely invisible to weekly or monthly utilization reviews. Fleeting by nature, these resources invalidate the FinOps holy grail of preventing cloud waste by providing cost visibility after resources are deployed.
- Agentic AI involves the use of AI agents, designed to pursue goals and generate output without human input. From AI-powered DevOps co-pilots, to customer service AI agents, and reinforcement learning models powering autonomous workload/cost management, these intelligent systems perceive their environments and take action at speeds that defy human intervention. So, a foundational FinOps concept such as tagging—who deployed what, for what project, at what time, etc. falls flat when left to manual processes in such architectures.
Practical demonstrations of FinOps limitations in autonomous infrastructure
50% of cloud waste happens the instant resources are spun up. That number takes on new weight with short-lived and autonomous resources. Unfortunately, the current FinOps optimization approach is built around anomaly detection and response, which tackles waste a little too late.
Consider a scenario where GPU and token averages shatter FinOps ideals: A global firm deploys a customer service agent fine-tuned on an AWS-hosted LLM. The inference endpoint is fronted by a cluster of NVIDIA H100 GPUs, expected to cost roughly $30/hr. The initial budget also puts average token per query (input and output) at about 1000 tokens, estimating a total of roughly 1.2 million tokens/day.
However, an unexpected traffic spike increases GPU utilization, just as multi-step reasoning multiplies token usage. The result is, instead of the anticipated $30/hr GPU costs and 1.2 million tokens, the autonomous workload gobbled up a whopping $100/hr and 10 million tokens.
Because autoscale flapping disrupts performance, scale-in events are configured to happen much more slowly than scale-out events, leaving the extra GPU capacity to drain funds well after demand has ebbed.
By the time FinOps teams spot the spend anomaly, the workload has exceeded one-third of its monthly budget in a single day! The problem here isn’t careless engineering; the inference endpoint worked exactly as designed, autoscaling and responding to complex conditions.
Here’s another example, this time looking at model fine-tuning. FinOps faces a similar problem with this workload, where model convergence, basic hyperparameter adjustments, and unexpected restarts can send cloud costs careening past budget limits.
A company’s Finance team, following the FinOps best practice of quarterly budgeting, estimates a 15–20% error margin. But the Software Engineering team never makes an effort to check spend. Engineering is frustrated trying to explain that the choice is between staying within static budgets while systems fail, or absorbing unexpected expenses for operational survival.
The problem is that in the autonomous, rapidly changing infrastructures of today, FinOps’ competitive advantage is only in telling, in gory detail, how much of cloud spend went down the drain.
As a cost governance model, FinOps lags far behind ephemeral and autonomous systems. So what’s the way forward?
A new operating model for FinOps and autonomous infrastructure: Infrastructure platform engineering
The solution is operational optimization: FinOps can no longer remain a downstream framework, isolated from the infrastructure, and implemented after upstream resources are up and running.
When FinOps is implemented in a way as to surface waste after it has occurred, AI-driven infrastructure changes, idle GPUs, and shadow AI experiments will chip away at margins until there’s no profit to speak of.
But the solution isn’t abandoning FinOps; it’s replatforming it with infrastructure platform engineering (IPE). IPE is a paradigm that weaves FinOps processes, such as cost control, cost visibility, and tagging, directly into infrastructure through policies enforced autonomously at pre-provisioning.
Here’s how it works:
1. Autonomous cost governance pre-provisioning
True cost governance starts before any resource is provisioned. IPE makes this possible by automating every aspect of infrastructure management, including cost controls.
If you’re adopting IPE for the first time, this is how it would instantly operationalize your cost management:
- Intelligent discovery: IPE inventories all provisioned resources, sanctioned, shadow, and orphaned, and maps their dependency relationships. This exposes cost inefficiencies, such as redundant and untracked resources, for immediate resolution.
- Governed provisioning: Engineering and Finance jointly make decisions about cloud expenses. These are codified into policies that control who (machine or human) can deploy what, under what conditions, and to what volume. For example, a policy may allow autoscaling to 50 GPUs for workload: recommendation-engine-ml, but deny the same for workload: fraud-detection-ml-research if the former is more critical to business operation.
- Self-service provisioning with cost as a first-class input: Developers and AI engineers can provision on demand, with cost controls built in—ensuring oversight without stifling innovation.
- Automated lifecycle management: IPE assigns expiration dates to resources. Unlike traditional IaC, where idle environments persist, IPE enforces auto-expiry, policy-based decommissioning, and scheduled runtimes.
2. Automated tagging and cost attribution
IPE tagging goes far beyond traditional IaC or FinOps tools. Instead of relying on engineers to tag (and tag correctly), IPE enforces tagging through policies.
- Default, codified tagging: Tagging is defined and enforced using policy as code. Once defined, IPE:
- 1. Blocks provisioning when baseline tags are missing or misapplied.
- 2. Applies tags automatically from authoritative sources (e.g., SSO ID, pipeline stage).
- Granular attribution: Tagging identifies “who,” attribution explains “why.” IPE understands asset context, tracing, for example, which machine identity spun up a GPU for what project, even after the resource is gone.
3. Monitoring, anomaly detection, and automated response
IPE enhances traditional FinOps monitoring with live visibility, cost inference, and real-time remediation.
- Continuous cost inference: Forecasts and reallocates budget in real time, based on live performance—not static budgets.
- Full visibility and auto-remediation: IPE detects anomalies and fixes issues before costs spiral out of control.
4. Agentic, context-aware optimization
AI isn’t the enemy of FinOps, it’s part of the solution. IPE uses agentic AI to continuously and autonomously optimize infrastructure.
Where FinOps flags waste too late, IPE systems proactively rightsize, apply guardrails, adjust resource selection, and tune decisions based on live business context.
The takeaway: Moving from retrospective FinOps to proactive cloud economics
While many businesses are still focused on retroactive spend visibility, forward-looking organizations are adopting IPE to embed cost governance into infrastructure automation.
Quali Torque is one of the leading IPE platforms, offering cost-aware self-service provisioning, AI-driven lifecycle management, and real-time optimization.
Watch the demo or start a free trial to see it in action.