Cost Estimation, Optimization, and Management for Generative AI Workloads

Content Level: 200

Once the business benefit of a GenAI application has been established, cost effective operations of the application become a key driver to deriving business value. Especially at scale, GenAI application can be expensive to operate, so choosing the right architecture and tools can make the difference in the success of a product. Additionally, given the resource constraints for the hardware that powers the GenAI applications, an efficient architecture might be the only way to scale up to meet customer demand.

Suggested Pre-Reading

GenAI cost optimization on AWS

• Optimizing Costs

• Estimating Costs

TL;DR

The journey of implementing generative AI begins with Business Strategy and Value Assessment, progressing through stages of Quick Wins, Process Reshaping, and Business Reinvention while aligning with core pillars of Growth/Innovation, Cost/Efficiency, and Customer Risk Management. Cost Estimation follows, requiring detailed understanding of model inference costs, multimodal data considerations, and various pricing models, with cost tracking and monitoring becoming essential as applications mature. Throughout implementation, organizations must balance optimization strategies and supporting infrastructure costs while considering multi-tenant cost models for effective management and scaling of AI workloads across departments.

Table of Contents

This project provides tools and strategies for estimating, optimizing, and managing costs associated with generative AI workloads.

1. Business case justification and ROI calculation

2. Cost Estimation

3. Cost Optimization Strategies

4. Real-time cost tracking, monitoring, and Multi-tenant cost model

Get Hands-On

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Further Reading

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Contributors

Author: Neelam Koshiya - Principal Applied AI Architect

Primary Reviewer: Randy Defauw - Senior Principal Solutions Architect

Additional Reviewer: Mike Gillespie - Principal Solutions Architect