AI FinOps: Managing LLM, GPU and Agentic AI Costs

As organisations adopt large language models (LLMs), GPU-based infrastructure and agentic AI systems, managing AI-related costs is becoming an important responsibility for engineering, cloud operations and finance teams.

AI expenditure can vary according to token consumption, model selection, inference workloads, GPU capacity, application architecture and the number of actions performed by AI agents.

This NobleProg hybrid event explores AI FinOps, focusing on approaches to measuring, analysing, allocating and optimising AI-related spending while balancing cost, performance and reliability.

Participants can attend onsite in Andheri East, Mumbai, or join live online, with both attendance options running simultaneously.

Why Attend?

AI workloads introduce cost-management challenges that may differ from traditional cloud infrastructure.

This event explores how organisations can improve cost visibility and accountability across AI systems.

Participants will examine approaches to:

• Understand the main cost drivers of LLM and agentic AI workloads.
• Analyse token usage, model selection and inference expenditure.
• Evaluate GPU utilisation and infrastructure spending.
• Identify potential inefficiencies in AI application workflows.
• Explore budgeting, forecasting and cost allocation methods.
• Consider trade-offs between AI performance, response quality and operating costs.
• Understand governance practices for monitoring AI expenditure.

Key Topics

1. Introduction to AI FinOps

Explore how FinOps principles apply to AI infrastructure and applications.

Consider cost visibility, accountability, resource efficiency and collaboration between engineering, operations and finance teams.

2. LLM Usage and Inference Costs

Examine the main factors influencing LLM application costs, including:

• Input and output token consumption.
• Model selection and pricing structures.
• Context window usage.
• Repeated model calls and retries.
• Caching and request optimisation.
• Cost versus response quality considerations.

3. GPU Infrastructure Cost Management

Explore how GPU resources contribute to AI infrastructure expenditure.

Potential areas include:

• GPU utilisation and idle capacity.
• Cloud GPU provisioning.
• Workload scheduling.
• Infrastructure sizing.
• Shared versus dedicated GPU resources.
• Cost and performance trade-offs.

4. Agentic AI Cost Management

Understand how agentic AI applications may generate additional costs through multi-step reasoning, repeated model calls, tool execution and interactions with external services.

Explore opportunities to monitor agent activity, define execution limits and reduce unnecessary resource consumption.

5. AI Cost Monitoring and Allocation

Examine approaches to tracking AI spending across applications, departments, projects and business units.

Consider usage metrics, tagging, reporting and cost attribution.

6. AI Budgeting and Forecasting

Explore methods for estimating AI workload expenditure and planning budgets based on expected usage, infrastructure requirements and application growth.

7. AI Cost Optimisation Strategies

Consider approaches such as:

• Selecting cost-appropriate AI models.
• Reducing unnecessary token consumption.
• Applying caching and batching where suitable.
• Improving GPU resource utilisation.
• Reviewing agent workflow efficiency.
• Balancing application performance, quality and cost.

8. AI Financial Governance

Explore organisational controls for AI expenditure, including budget thresholds, spending alerts, usage reviews and accountability for AI resource consumption.

These are proposed topic areas based on the event title. Final curriculum coverage, technical demonstrations, platforms and practical exercises require confirmation against the approved course agenda.

Who Should Attend?

This event is relevant to:

• FinOps practitioners
• Cloud infrastructure engineers
• AI and machine learning engineers
• MLOps and LLMOps professionals
• Platform engineering teams
• Cloud architects
• IT operations managers
• Technology finance professionals
• Technical leaders responsible for AI infrastructure budgets

Delivery Format Hybrid — Onsite and Live Online

Participants can attend physically at the Mumbai venue or join the same event remotely through live online participation.

About NobleProg

NobleProg provides professional training in technical and business-related subjects, supporting individuals and organisations in developing relevant knowledge and skills.

Registration

Register your interest in AI FinOps Training in Mumbai.

Choose onsite participation in Mumbai or live online attendance when the event schedule and registration options are confirmed. Contact us at training@nobleprog.in