FinOps for AI

Today, we hear about so many organizations (from small start-ups to large enterprises) experimenting with GenAI applications, adding GenAI components to their existing workloads, and perhaps even moving from evaluation to production.

The increased usage of GenAI services requires organizations to pay attention to the cost of using GenAI services before the high and unpredictable cost generates additional failed projects.

In this blog post, I will share some common recommendations for implementing FinOps practices as part of GenAI workloads.

Real-Time Cost Visibility, Allocation, Tagging, and Accountability

Lack of real-time visibility into cloud costs makes it difficult for organizations to track spending, identify waste, and assign accountability. Without clear, up-to-date cost allocation tied to projects or teams, overspending and inefficiencies often go unnoticed. Building transparent cost tracking and tagging practices empowers teams to monitor expenses continuously, optimize usage, and align spending with business goals.

Recommendations / Best practices

• Optimization Tools: Software that identifies inefficiencies and recommends or automates cost-saving actions in cloud environments. Common services: AWS Cost Explorer, AWS Trusted Advisor, Microsoft Cost Management, Azure Advisor, Google Cloud Billing, Google Recommendations.
• Estimate and Monitor Costs: Tools to forecast upcoming cloud expenses and continuously track actual spend against budgets. Common services: AWS Pricing Calculator, Azure Pricing Calculator, Google Cloud Pricing Calculator.
• Budgets, Alerts, and Cost Analysis: Features that allow setting spending limits, notifying on overruns, and analyzing cost trends. Common services: AWS Budgets, AWS Cost Anomaly Detection, Azure Budgets, Cost Alerts, Google Cloud Billing Budgets, and Google Cloud Billing Cost Anomaly Detection.
• Cost Visibility, Allocation, tagging: Mechanisms to attribute cloud costs accurately to applications, teams, or business units using tags and reports. Common services: AWS Cost Allocation Tags, Azure Tagging, GCP Labels.
• Token and Endpoint Cost Tracking: Monitoring and reporting on usage-driven costs specifically related to API tokens and endpoint consumption. Common services: Amazon CloudWatch, Azure Monitor, Google Cloud Observability.
• Real-Time Cost Visibility: Providing immediate, up-to-date insights into cloud spend for timely decision-making and anomaly detection. Common services: Amazon CloudWatch Metrics Insights, Azure Cost Analysis, and Google BigQuery.

Rightsizing and Resource Optimization

Rightsizing and resource Optimization ensure cloud resources are appropriately sized and efficiently used by continuously analyzing usage patterns and adjusting capacity to eliminate waste and meet actual demand, thereby reducing costs without compromising performance.

Recommendations / Best practices

• Choose Optimal Model and Inference Types: Select foundation models and inference methods that precisely match your business needs to avoid paying for unnecessary capacity. Continuously evaluate workload requirements and prefer smaller, purpose-fit models over default larger ones to save costs. Reference: Generative AI Cost Optimization Strategies
• Batching and Concurrency: Efficiently batch inference requests and manage concurrency to maximize instance utilization and reduce cost per token or operation. Reference: GenAI Cost Optimization: The Essential Guide
• Right-Sizing and Model Selection: Regularly right-size infrastructure—compute, memory, GPU—to workload demand, using autoscaling, spot, and reserved instances to balance cost and performance. Avoid defaulting to high-end hardware for all workloads. References: Identify your savings potential in Azure, Optimizing GenAI Usage.
• Leverage Cloud-Specific Cost Management Tools: Use cloud vendor cost management and advisory tools to identify and implement cost-saving recommendations. Common services: AWS Compute Optimizer, Azure Advisor, Google Recommendations.

Intelligent Pricing Strategies: Reserved, Spot, and Preemptible Instances

Reserved instances offer significant discounts for long-term, steady workloads by committing to a specific resource usage over one to three years, helping reduce costs compared to pay-as-you-go pricing. Spot and preemptible instances allow access to spare cloud capacity at substantially lower prices but with the risk of interruption, ideal for flexible or fault-tolerant tasks. Balancing these options with real-time workload needs enables cost-efficient cloud resource management while maintaining scalability and performance.

Recommendations / Best practices

• Reserved Instances and Commitment Pricing: Reserve instances or commit to savings plans for consistently running workloads to gain discounts of 30-70%. These long-term commitments reduce cost predictability and environmental stability. References: Reserved Instances for Amazon EC2 overview, Azure Reserved Virtual Machine Instances, Google Committed Use Discounts.
• Spot: Use spot for interruptible, fault-tolerant workloads like training and batch processing to save up to 90%. These resources are offered at deep discounts but can be reclaimed with short notice, requiring workload resilience and automation to manage interruptions. References: Amazon EC2 Spot Instances, Azure Spot Virtual Machines, Google Spot VMs.
• Auto-Scaling and Capacity Reservations: Pair spot and reserved instances with auto-scaling and capacity reservations to dynamically adjust resources based on workload demand, Optimizing cost and performance balance. References: Amazon EC2 Auto Scaling, Azure Virtual Machine Scale Sets, Google Autoscaling groups of instances.

Automation and Dynamic Scaling

Automation and dynamic scaling enable cloud resources to automatically adjust in real time to changing workload demands, ensuring efficient performance during peak times while minimizing costs by scaling down when demand is low. This approach reduces manual intervention, optimizes resource use, improves reliability, and supports business agility by maintaining responsiveness under fluctuating traffic conditions.

Recommendations / Best practices

• Automation and Idle Shutdown: Implement automated policies that stop, pause, or scale down AI model endpoints and compute resources during idle or low-traffic periods to avoid unnecessary costs. This dynamic management prevents paying for unused capacity, especially in development and batch workloads. References: AWS Compute Optimizer, Best Practice for Azure AI service cost configuration, GCP AI and ML perspective: Cost Optimization.
• Serverless and Event-Driven Compute: For variable or unpredictable inference workloads, leverage serverless compute options to pay strictly for consumed resources and scale automatically. This approach reduces operational overhead and costs. References: GenAI Accelerator Starter Package, Azure OpenAI Extension for Function Apps Hands-on Experience, Run AI inference on Cloud Run with GPUs.
• Dynamic Scaling and GPU Pooling: Use autoscaling and GPU pooling techniques (e.g., multi-instance GPU technologies) to maximize hardware utilization, reducing idle time and enabling more efficient processing of batch or concurrent inference tasks. This can significantly improve utilization from typical 25%+ levels to over 60%. Reference: Optimizing GenAI Usage

Cost-Aware Model and Workflow Design

Adopting a cost-aware approach to model and workflow design ensures financial insights are embedded in every step of the development lifecycle. By prioritizing real-time cost visibility, proactive forecasting, and iterative policy refinement, teams can anticipate spend early, align resource usage with business intent, and implement rapid adjustments as requirements evolve. This mindset promotes conscious decision-making, enabling organizations to balance performance and efficiency from the ground up.

Recommendations / Best practices

• Optimize prompt design and token usage: Design applications with cost-aware prompting by minimizing prompt size and engineering efficient prompts. This reduces model invocations and token consumption, directly controlling costs. References: Generative AI Lens - Cost Optimization, Effect of Optimization on AI Forecasting.
• Use prompt routing, caching, and inference Optimization: Route requests to the most cost-effective models and cache frequent prompts to reduce expensive token processing. This approach can cut inference costs by 40-70%, according to FinOps guidance. Target inference workloads for Optimization since they account for 80-90% of GenAI spending. Reference: Optimizing GenAI Usage
• Monitor and apply governance per FinOps best practices: Incorporate real-time cost monitoring, forecasting, and governance aligned with FinOps principles to drive iterative cost improvements during the AI model lifecycle. Reference: Effect of Optimization on AI Forecasting

Quotas, Monitoring, and Anomaly Detection

Monitoring quotas and detecting anomalies with alerts ensures cloud resources are managed proactively. Setting alerts before limits are reached helps prevent service disruptions and enables timely capacity planning. This practice keeps cloud workloads reliable and cost-effective across environments.

Recommendations / Best practices

• Granular Monitoring and Cost Tracking: Utilize advanced cost management tools with customizable dashboards to monitor usage and spending trends closely. Implement automated alerts and anomaly detection powered by machine learning to identify unexpected cost spikes and deviations early, enabling proactive cost control. References: AWS Cost Anomaly Detection, Identify anomalies and unexpected changes in cost, View and manage cost anomalies, Cloud Cost Management.
• Utilization and Quotas Management: Continuously monitor resource use across all clouds and set quotas to prevent overruns and runaway costs. Identify idle or low-traffic endpoints to shut down or consolidate, which reduces unnecessary spend. Apply quota management on large AI model endpoints to enforce cost limits during experimentation. References: Automate quota management, Azure subscription and service limits, Google Cloud Quotas overview.
• Usage Pattern Analysis and Feedback: Establish continuous monitoring solutions to detect idle or under-utilized resources and optimize workflow efficiency. Encourage feedback loops between teams to align cost reduction with operational needs, following FinOps best practices. Reference: Cost Estimation of AI Workloads

Storage and Data Lifecycle Management

Efficient storage and data lifecycle management are key to controlling cloud costs. Implementing automated lifecycle policies helps transition data across storage tiers based on access patterns and retention needs, while regularly auditing for orphaned or stale data prevents unnecessary spending. Embedding these practices early in the provisioning process ensures cost Optimization throughout the data lifecycle.

Recommendations / Best practices

• Lifecycle and Storage Policies: Implement automated data lifecycle management for model training datasets by shifting data to lower-cost storage tiers as access patterns change and removing obsolete or redundant data to reduce storage costs. This reduces provisioning waste and aligns storage use with business needs. References: AWS Data Lifecycle Management, Azure Data Lifecycle Management, Google Cloud Storage Lifecycle Management.
• Efficient Storage and Data Handling: Optimize data pipelines and storage choices by selecting cost-effective storage classes and managing data flow to minimize expensive resource usage during data processing steps that do not require high performance. References: AWS Cost Optimization, Cost Estimation of AI Workloads

Team Enablement, Training, and Cost Ownership

Empowering teams with clear cost ownership and targeted training fosters accountability and cost-conscious decision-making. Embedding cost awareness into daily workflows and providing role-specific education helps teams balance innovation and budget, driving a culture of shared responsibility for cloud spending.

Recommendations / Best practices

• Team Accountability: Assign cost owners and embed cost awareness into engineering workflows, training, and planning. Empower teams to make model design and usage decisions with full visibility of financial impact. References: AWS Cost optimization, How to optimize your cloud investment with Cost Management, Guide to Cloud Billing, Resource Organization and Access Management, FinOps Education & Enablement.

Forecasting, Budgeting, and Predictive Insights

Accurate forecasting, budgeting, and predictive insights enable organizations to anticipate cloud costs, align spending with business goals, and prevent budget overruns. Leveraging historical data, driver-based forecasting, and machine learning models helps create dynamic, actionable forecasts that drive financial accountability and proactive cost management.

Recommendations / Best practices

• Accountability, Budget Control, and Forecasting: Assign cost ownership to workload teams and integrate showback or chargeback mechanisms to increase cost visibility and accountability. Use continuous forecasting tools that leverage historical data and growth plans to dynamically adjust budgets and commitments, aligning spending with business objectives. References: AWS Practice Cloud Financial Management, Cost Optimization design principles, Google Cost Optimization pillar, Exploring Cloud Cost Forecasting, Cost Estimation of AI Workloads.

Governance, Policy, and Tooling Automation

Automating governance policies ensures consistent compliance, security, and cost control in the cloud. By embedding policies into infrastructure workflows and deployment pipelines, organizations reduce manual errors and enforce rules proactively. This approach enables scalable, reliable oversight and quick remediation across diverse cloud environments.

Recommendations / Best practices

• Governance and Automation: Use Optimization tools to recommend rightsizing, automatically terminate idle workloads, and enforce cost policies at scale for efficient cloud resource management. References: AWS Cost Optimization Pillar – Governance, Cloud Adoption Framework for Azure - Build a cloud governance team, Guide to Cloud Billing Resource Organization and Access Management, Optimize Usage & Cost.

Summary

In this long blog post, I have shared recommendations from various aspects for embedding FinOps practices as part of the design, deployment and maintenance of modern applications containing GenAI services.

Any organization must have proper design and visibility into the cost aspects of any application using GenAI components to avoid high cost, or at least be able to track expected costs as soon as possible.

I encourage the readers to review the hyper-scale cloud providers' documentation, understand service cost, and learn about best practices for cost Optimization.

I also encourage the readers to learn from the FinOps Foundation's official documentation and best practices as they deploy GenAI services.

Disclaimer: AI tools were used to research and edit this article. Graphics are created using AI.

Additional references

• AWS Well-Architected Framework - Cost Optimization Pillar
• Amazon Bedrock -Cost Optimization
• Guidance for Cost Analysis and Optimization with Amazon Bedrock Agents
• Amazon SageMaker - Inference cost Optimization best practices
• Azure Well-Architected Framework - Cost Optimization
• Microsoft Foundry - Cost management for fine-tuning
• Cost Optimization of Azure AI Services
• Google Well-Architected Framework - AI and ML perspective: Cost Optimization
• Google Well-Architected Framework - Cost Optimization pillar
• Three proven strategies for Optimizing AI costs

About the author

Eyal Estrin is a seasoned cloud and information security architect, AWS Community Builder, and author of Cloud Security Handbook and Security for Cloud Native Applications. With over 25 years of experience in the IT industry, he brings deep expertise to his work.

Connect with Eyal on social media: https://linktr.ee/eyalestrin.

The opinions expressed here are his own and do not reflect those of his employer.