Cost Optimization for AI Workloads: From Visibility to Control
AI workloads introduce unpredictable cloud spend driven by GPU utilization, token-based pricing, and distributed infrastructure. Discover the hidden cost drivers behind AI initiatives and what it takes to gain unified visibility, forecast accurately, and establish sustainable cost control across cloud environments.
ITOps teams can achieve cost management of AI workloads with an observability platform that connects AI usage and performance with cloud spend for clear visibility and predictability.
AI costs spiral when GPU usage, token consumption, and training workloads lack clear ownership and visibility.
Billing data alone can’t explain AI spend—teams need AI-specific telemetry tied directly to performance and cost.
When infrastructure, AI signals, and financial data are aligned, organizations optimize confidently, control spend, and scale AI without surprises.
Behind the buzz around artificial intelligence, or AI, many companies are discovering the hidden and compounding costs of AI adoption. The narrative portraying generative AI (GenAI) as a solution capable of reshaping industries, enhancing customer experiences, and unlocking significant returns on investment (ROI) has driven rapid adoption across organizations. As AI workloads scale, intensive compute demands, highly variable usage patterns, and complex deployment requirements are driving massive cloud bills and making cost control increasingly difficult.
Organizations are already struggling with cloud cost management, and 72% surveyed globally report exceeding their cloud budgets according to a Forrester Consulting study commissioned by Boomi. With Gartner forecasting worldwide GenAI spend to grow by more than 75% compared to 2024, the challenge of managing AI workload spend is intensifying. The FinOps Foundation’s 2025 State of FinOps Report shows that managing AI and Machine Learning (ML) spending is rapidly rising in priority, moving up four positions from last year’s report.
Without better visibility and optimization, growing AI workloads can increase risk around existing cloud cost challenges and create barriers to scale.
Why AI Workloads Make Cost Optimization More Complex
Cloud-based AI costs appear alongside other cloud charges, but month-end billing isn’t providing the visibility needed to manage spend. Unlike traditional cloud workloads, AI costs are distributed across multiple infrastructure layers because AI workloads run in distributed environments. This makes optimization more complex and requires telemetry-driven approaches tailored to each resource type.
Beyond infrastructure complexity, pricing models and deployment decisions further complicate AI cost optimization. In simple terms, AI costs are driven by usage, but the price of that usage varies widely by service. Engineering choices around model design, deployment architecture, and service selection influence the ratio of cost to value, making optimization decisions more nuanced than standard cloud workloads.
The majority of AI budgets—particularly for GenAI— are consumed by compute. The specialized graphics processing units (GPUs) required for AI workloads cost 10-20x more than the standard central processing unit-based (CPU) compute that drives cloud workloads. Optimization efforts require consistent tracking of usage patterns to discover idle or underutilized resources and opportunities for more complex engineering changes.
GenAI pricing is driven by token-based and unit-based billing models, where costs vary depending on how models are used and the type of tokens involved. Optimization can’t rely on simply reducing usage; it’s about fine-tuning models and workflows to control token consumption.
AI cost visibility is a top emerging challenge and most organizations are still in early stages of cost governance maturity for emerging AI workloads, the FinOps Foundation reports. Traditional monitoring tools are not providing visibility into token counts, vector database activity, or inference load patterns. It’s difficult to align which models and teams are driving AI spend without clear correlation between telemetry and cost data.
The Hidden Causes of AI Overspend
“Hidden causes” or cost drivers may not be considered when budgeting and forecasting, but they have an immense impact on total costs. These include:
A phenomenon referred to as the “Context Window Tax.”
The simple per-token-pricing is not as straightforward as it seems. Because of the stateless nature of most LLM APIs, conversation history is re-sent with every request. Token volume quickly multiplies with every prompt and response. Teams can pay far more than expected because of lengthy prompts or complex interactions.
Unexpected, additional AI model training
Training AI models is an expensive upfront cost, and additional training or fine-tuning is needed. Because training requires sustained GPU compute, large-scale data storage, and repeated experimentation, costs can quietly accumulate . As models grow larger and teams iterate more frequently, training becomes a recurring operational expense.
Poor workload scheduling
AI workload scheduling can have a major impact on costs. GPUs accrue cost regardless of utilization. When they sit partially idle, continue running after job completion, or have scheduling gaps, this can quietly inflate cloud spend. When teams lack visibility, they don’t know how efficiently AI workloads are using allocated resources.
The Journey From AI Chaos to Cost Clarity
Stage 1: Integration of AI capabilities
Gartner’s prediction that AI workloads will account for roughly half of all cloud compute resources by the end of the decade suggests that AI adoption is an ongoing trend. GenAI adoption rates according to the 2024 Flexera State of the Cloud Report, show that nearly half of organizations are actively using GenAI services in the public cloud, while another 38% are experimenting. As AI adoption grows, teams move from experimentation to production faster than their cost visibility can keep up.
Stage 2: The side effects emerge
As AI usage grows, surprising costs show up on bills. Compute costs are at all time highs and inference workloads are exceeding forecasted token usage. The distributed infrastructure makes it difficult to associate costs to resources and current monitoring tools aren’t delivering the needed visibility. Bills continue to grow without clear direction on how to stay in budget.
Stage 3: The turning point—visibility
The turning point comes when organizations bring together infrastructure telemetry, AI-specific signals, and financial data. FinOps research shows that meaningful cost reduction starts with unified visibility and collaborative practices. When teams measure the entire use case, rather than isolated resources, they begin to understand the true total cost of ownership and are able to identify the cost to value ratio. Optimization efforts can be successful and validated with unified visibility. With a new framework implemented, they can shift to proactive management and impactful optimization.
Read about Cost-Intelligent Observability—the framework that enables optimization through collaboration and visibility.
Sustainable cost control requires ongoing coordination between engineering, operations, and finance. Real-time monitoring and continuous optimization allow ops teams to catch anomalies early, adapt to usage changes, and maintain predictable spend as AI usage evolves. Intentional collaboration allows organizations to scale AI confidently and ensure that it remains both operationally effective and financially viable.
Getting AI cost clarity with hybrid observability
Shift from reactivity to visibility and finally, to sustainable cost control. Infrastructure telemetry, AI specific data, and financial details need to be integrated into a single view. Effective optimization requires that all stakeholders work from the same data and understand their specific roles and responsibilities. This shift results in shared accountability and collaborative decision-making. With integrated metrics, teams can correlate telemetry to dollars and implement best practices. With a Cost-Intelligent Observability platform, reduce wasteful spend, stop surprise bills, and create accurate forecasts.
Visibility looks like:
Multi-cloud spend visualized together
Multi-cloud FinOps FOCUS spend normalization
Token usage tracking
Model-level cost attribution
Real-time anomaly detection for spend spikes
Operational dashboards that correlate performance and cost
Sustainable Cost Management Looks Like:
Full visibility in one platform
Efficient workflow from spend spike or anomaly to proposed solution to resolution
Trustworthy forecasts
Continuous monitoring and optimization
LogicMonitor Tackles Cloud Cost Optimization
LogicMonitor’s Cost Optimization is built on four key pillars—Integrate, Inform, Optimize, and Operate—and aligns with FinOps best practices. It embeds financial accountability into daily ITOps workflows, turning cost management into an embedded habit. By unifying distributed costs, resources, usage, and performance into a single story, you gain the visibility needed for collaborative, value-driven decision making. Real-time telemetry highlights AI workload cost trends, token consumption, compute utilization, and database health, making it easy to identify cost drivers, anomalies, and waste.
With unified visibility in place, tailored, data-driven recommendations help you rightsize instances, deallocate idle GPUs, adjust storage tiers, and validate savings outcomes with confidence. Continuous monitoring ensures GenAI workloads remain optimized as usage patterns shift, with clear insight into inference latency, token usage, and overall spend. As workloads evolve, you can validate the impact of scaling and workload placement decisions, detect anomalous spikes in spend, and remediate issues before costs escalate. The result is sustained governance that reduces waste, avoids surprises, and enables organizations to scale AI workloads while balancing performance and business impact.
Ready to go deeper?
Read How LogicMonitor Delivers AI Cost Optimization to see the capabilities, workflows, and recommendations that turn visibility into measurable savings.
Teia Jensen is a Product Marketing Specialist at LogicMonitor, where she spends her time turning powerful platform capabilities into clear, compelling stories—basically, helping customers understand not just what the platform does, but why it matters. She started her LogicMonitor journey as a BDR working with enterprise customers before moving into product marketing, with a strong focus on education and enablement. She’s driven by making complex problems and solutions feel approachable, especially across observability, cost optimization, product announcements, and platform packages. Outside of work, she plays padel and is chasing the perfect bandeja.
Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.
