AI adoption is accelerating across every industry. Enterprises embed large language models (LLMs) into products, workflows, and customer experiences at a rapid pace. They do it for good reason: AI brings faster decision-making, richer customer interactions, and new opportunities to create value.
In September 2025, Capgemini reported that 82% of executives experienced significant increases in GenAI costs, with 68% stating they exceeded their budgets by more than 10%. This level of investment underscores both the urgency and the risks of adoption.
All three major cloud providers now offer managed LLM services inside their ecosystems, making it simple for companies to build with these advanced models. This convenience fuels innovation but also drives costs higher. The challenge goes beyond tracking spend; it requires rethinking when and how financial considerations shape AI development decisions. Industry data shows that AI and LLM-related spend are now among the fastest-growing categories of cloud infrastructure.
"The shift from reactive cost control to proactive financial engineering is essential. We've seen firsthand how early cloud adopters struggled with visibility and accountability. GenAI presents an opportunity to apply those lessons and build FinOps into the design phase, not just the review cycle."
— Vikram Rajan, VP, Cloud Transformation, Capgemini UK
What does this mean for FinOps?
Traditional FinOps practices cannot keep up with this shift. They were built for the slightly more predictable nature of compute and storage workloads, not for the volatile, token-driven nature of LLMs. Teams need a framework that connects technical choices with financial outcomes in real time. Cloud Efficiency Posture Management (CEPM) provides that framework by exposing hidden inefficiencies, aligning engineering and finance, and enabling proactive cost management.
The Cost Challenge of LLMs
LLMs introduce new complexities into cloud economics that map directly to three organizational blind spots: unclear optimization priorities, limited understanding of usage behavior, and opaque billing data. Teams need to recognize how each technical decision feeds one or more of these blind spots to regain control of unit economics. Mike Bradley, Senior Manager of AI Economics at Capgemini UK, says, "In GenAI, spend often precedes revenue. Without granular visibility, organizations risk scaling features that erode margins. Introducing a virtual cost layer gives teams the clarity to tie spend to business outcomes, turning opaque cost items into actionable insights."
Token Economics and Prompt Optimization
Tokens form the foundation of LLM economics. Input tokens include prompts, instructions, and context an application sends to the model. Output tokens represent the model's responses. Cached input tokens provide discounted rates when prompts reuse static context, but caching rules differ by provider. For example, AWS Bedrock charges extra for cache writes while Azure and GCP only discount reused tokens.
These caching rule variations directly affect unit economics. A team that tunes prompts to maximize cache hits reduces recurring costs while maintaining accuracy. A team that ignores cache mechanics risks paying higher rates for nearly identical workloads. Tokens are not just a billing detail; they are the lever that decides whether an AI feature scales profitably.
To make this concrete, consider a customer-support chatbot that sends a 500‑token prompt and receives a 200‑token response. If 400 tokens in the prompt are static and cached, Azure and GCP typically apply discounts on the reused portion, materially reducing per‑request cost. On AWS, cache reuse also helps, but teams must account for cache‑write charges, which can offset savings at lower volumes. These mechanics make prompt design and cache strategy a first‑order economic decision, not a back‑office detail.
Provider caching differences (high-level):
- AWS Bedrock: cache writes charged; reused tokens discounted.
- Azure OpenAI: reused tokens discounted; no separate cache‑write fee.
- Google Vertex AI: reused tokens discounted; labeling and metadata are important for visibility.
Bradley adds, "Prompt engineering is no longer just a UX concern, it's a financial lever. CEPM helps teams quantify the impact of cost factors such as prompt length, cache effectiveness, and model selection, transforming design decisions into economic strategy."
Deployment Locality and Compliance Tradeoffs
Every LLM deployment includes a locality choice, the region or compliance zone where inference runs, and that choice carries economic as well as regulatory and latency implications. Azure OpenAI charges different rates for Global, Data Zone, and Regional deployments. Global is the cheapest, while Regional carries a premium for compliance. In AWS and GCP, locality mainly influences compliance and latency, not price.
The impact is more than technical. A compliance-driven decision can double infrastructure costs if chosen without economic analysis. Finance leaders often approve compliance premiums as fixed requirements, but in reality many workloads don't need the most expensive configuration. Organizations that evaluate compliance requirements against cost impact avoid unnecessary premiums while still meeting regulatory standards.
Capacity Planning in a Volatile World
Provisioned throughput guarantees token capacity per second. Azure uses Provisioned Throughput Units (PTUs), AWS offers Model Units (MUs), and GCP provides Generative AI Scale Units (GSUs). These options improve performance and reliability, but they introduce financial risk.
Provisioned throughput costs more than on-demand usage. Underutilized capacity wastes money, and overflows either trigger on-demand pricing or throttle requests. Without visibility into workload patterns, teams guess at throughput sizing. That guess often leads to inflated bills or degraded user experience. By connecting throughput utilization to cost, CEPM lets organizations size capacity to actual demand, ensuring reliability without overspending.
In practice, CEPM also surfaces mismatches between environments, for example, dev/test stacks running on provisioned capacity when on‑demand would suffice, and quantifies the savings from right‑sizing or shifting bursty phases away from reserved throughput.
Visibility Gaps in Billing and Usage
LLM costs are difficult to attribute with native billing tools because they were not originally designed for token‑level granularity. Azure aggregates spend at the account level, AWS lumps usage unless inference profiles are configured, and GCP requires manual labeling to unlock granularity. These are maturity and configuration limitations rather than shortcomings — but they still constrain the ability to tie spend to specific applications or outcomes.
The result is organizational misalignment. Finance sees a growing bill with no clear driver. Engineering sees workloads expanding but cannot explain the financial impact. Without visibility, optimization becomes reactive. By creating a virtual cost layer that maps technical metrics to billing data, organizations can track cost per deployment, per application, or per outcome. That visibility creates accountability and builds trust between finance and engineering.
The Business Impact
LLM workloads scale directly with customer adoption. Every new feature release drives usage, which increases token consumption and infrastructure spend. Many organizations experience cost growth before they see revenue growth, creating a squeeze on margins.
Three blind spots make the problem worse:
- Unclear optimization priorities: Teams lack clarity on which workloads matter most for cost reduction.
- Limited understanding of usage behavior: Volatile demand makes capacity planning risky.
- Opaque billing data: Native tools typically lack the application‑level detail needed for token economics, which obscures cause‑and‑effect.
These blind spots erode margins. When costs grow faster than revenue, even successful products become financial risks. To stay profitable, organizations need to move beyond token counts and measure efficiency in terms of business outcomes. For example, cost per resolved customer ticket or cost per generated design draft provides a more accurate measure of value than raw token pricing.
The Virtual Cost Layer
The virtual cost layer lies at the heart of CEPM. It combines raw billing exports with configuration data and technical metrics such as token usage, context length, throughput, and deployment type. This layer translates opaque bills into actionable insights. Instead of knowing only that a bill increased, teams can see that cache effectiveness dropped, throughput utilization declined, or prompt length inflated costs.
With this visibility, organizations stop reacting to surprises and start planning efficiency into every design decision. The virtual cost layer also enables tracking of cost per outcome, creating a reliable benchmark for both finance and engineering.
Continuing the earlier chatbot example: CEPM might reveal that provisioned throughput was idle 40–60% of the time, average prompt size crept up 15–20%, and a newer model could deliver the same resolution rate at materially lower token cost, turning a monthly billing spike into a concrete set of actions.
Optimization Opportunities
The following levers directly address the blind spots identified earlier by turning technical choices into measurable economic outcomes.
Workload Commitments in the LLM Era
As GenAI features move from pilots to core product experiences, usage patterns stabilize. At that stage, cloud providers offer discounts tied to LLM capacity (e.g., reserved Model Units or analogous commitments). These are not generic compute reservations; they are model‑capacity commitments that trade flexibility for lower unit cost. CEPM provides the visibility needed to distinguish stable workloads that justify reservations from volatile ones that require on-demand. This avoids two costly mistakes: over‑committing to spiky early‑stage demand, and missing discounts once traffic becomes predictable.
Throughput Tuning for Elastic Demand
Provisioned throughput provides reliability but at a premium. Right-sizing deployments prevents waste by aligning capacity with real workload patterns. Development and testing workloads rarely need provisioned capacity, and running them on-demand cuts unnecessary spend. CEPM highlights misaligned environments, flags underutilized capacity, and quantifies the savings of reconfiguration. The outcome is predictable performance with less waste, achieved through configuration changes rather than guesswork.
Choosing the Right Model
Model selection has as much financial impact as any billing discount. Different models within the same family vary by cost and capability. Newer releases often provide better performance at lower cost, with savings of up to 80 to 90 percent compared to older versions.
CEPM turns model selection into an economic decision. Teams compare token price, latency, and task accuracy side by side, pick the cheapest model that meets the requirements, and ship faster without losing quality. The result is the same outcome at lower costs.
Interaction Design
The way applications interact with LLMs directly affects cost. Prompts that reuse static context reduce token counts through caching. Streamlined system messages and context windows prevent unnecessary input tokens. Designing prompts with efficiency in mind lowers spend without sacrificing accuracy. CEPM measures token waste per request, cache effectiveness over time, and cost‑per‑outcome across prompt variants, giving engineering a tight feedback loop to tune for both accuracy and unit economics. CEPM makes these tradeoffs measurable so engineering can tune prompts with financial impact in mind.
From Framework to Action: How to Achieve Ongoing Efficiency
CEPM delivers more than visibility. It provides a structured process for making tradeoffs, measuring efficiency, and turning insight into action. Organizations can approach this in three layers: decision framework, key metrics, and a staged roadmap.
Decision Framework
Teams start by defining the business outcome they want to measure, such as cost per resolved support ticket or cost per generated draft. They then benchmark cost per interaction across different models, simulate the economics of on-demand versus provisioned throughput, and measure cache effectiveness and prompt length. The final step is aligning with finance on acceptable cost per outcome. This framework forces decisions about architecture, model selection, and deployment type to happen with financial implications in plain view.
Key Metrics
Clear metrics translate LLM economics into actionable measures:
- Effective cost per request: total spend divided by requests, adjusted for cache.
- Utilization rate of provisioned units: proportion of reserved capacity actually consumed.
- Cost per business outcome: cost tied directly to a business result, such as a resolved ticket or generated draft.
These measures replace raw token counts. Finance sees cost in terms of business value, and engineering sees how technical choices map to financial results. Together, they form a common language.
Roadmap for Adoption
Applying CEPM does not require a full rebuild. Organizations can stage adoption with quick wins first:
- Establish tagging, labeling, or inference profiles to capture application-level usage.
- Build a virtual cost layer that links billing exports with configuration data.
- Define outcome-based metrics that reflect business value.
- Run model benchmarks to uncover modernization opportunities.
- Simulate throughput tradeoffs with CEPM tools.
This roadmap gives teams a clear starting point. They gain visibility quickly, prove value with early wins, and then advance toward proactive optimization.
The CEPM Framework
Cloud Efficiency Posture Management (CEPM) delivers the structure organizations need to measure efficiency in this outcome-based way. CEPM extends FinOps by linking business outcomes to the technical metrics that actually drive spend, such as tokens, context length, and throughput choices. By integrating financial data with cloud usage patterns, CEPM turns raw billing into a clear picture of unit economics and enables proactive efficiency management and decision making.
Core Principles
CEPM rests on three core principles that translate abstract cloud bills into ongoing efficiency improvements through actionable guidance for engineering and finance.
- Visibility at the right level: CEPM surfaces cost at the workload level rather than at the account or subscription level. This provides the unit economics needed to make meaningful optimization decisions.
- Continuous alignment: CEPM establishes a shared language between engineering and finance, linking technical choices to financial outcomes.
- Proactive optimization: CEPM shifts organizations from reacting to bills after the fact to simulating tradeoffs and making informed decisions before costs escalate.
CEPM provides more than visibility; it enables a structured process for making tradeoffs:
- Define the business outcome you need to measure.
- Benchmark cost per interaction using different models.
- Simulate on-demand and provisioned throughput economics.
- Measure cache effectiveness and prompt length.
- Align with finance on acceptable cost per outcome.
"PointFive's CEPM framework is a necessary evolution of FinOps for the AI era. It bridges the critical gap between technical choices and financial outcomes. It provides the structure to move teams from simple cost reporting to proactive, outcome-based efficiency management."
— Alison McIntyre, Director of Cloud Economics, Capgemini UK
This framework helps organizations evaluate compliance, performance, and financial tradeoffs side by side. Decisions about architecture, model selection, or deployment type no longer happen in isolation. They happen with clear financial implications in view.
Conclusion
LLM economics challenge traditional FinOps practices. Token variability, throughput tradeoffs, and opaque billing make cost unpredictable and difficult to manage. Margins erode when costs rise faster than revenue, and organizations lose confidence in scaling AI.
CEPM provides the framework needed to manage this new reality. By exposing hidden inefficiencies, connecting usage to financial outcomes, and enabling proactive optimization, CEPM transforms AI economics from a source of risk into a source of advantage.
Organizations that adopt CEPM scale AI with confidence. They align engineering and finance, protect margins, and deliver the responsiveness and accuracy their customers expect.
This whitepaper was developed in collaboration with Capgemini UK, with contributions from Vikram Rajan (VP, Cloud Transformation), Mike Bradley (Senior Manager, AI Economics), and Alison McIntyre (Director, Cloud Economics).