• views: 204
• date: 20 August 2026
• posted by: 0nelove

Ai Cost Optimization: Reduce Llm And Api Spending
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 56m | Size: 3.37 GB
Control token usage, reduce API calls, use caching, model routing, RAG, and batch processing to lower AI costs
What you'll learn
Identify the major sources of unnecessary AI and large language model costs.
Measure AI usage and establish a clear baseline for cost, quality, and performance.
Apply prompt, model, caching, and workflow optimizations to reduce AI spending
Build a practical AI cost-reduction plan without compromising business outcomes
Select cost-effective AI models based on task complexity and business requirements.
Monitor AI costs using meaningful metrics, budgets, and governance controls.
Requirements
No advanced AI, programming, or data science experience is required.
A basic understanding of generative AI tools and business workflows is helpful.
An interest in reducing AI costs while maintaining quality and performance.
Description
This course contains the use of artificial intelligence.
Generative AI can deliver enormous business value, but inefficient prompts, unnecessary context, repeated API calls, and inappropriate model selection can quickly increase operating costs.
This practical course teaches you how to understand, measure, and reduce the cost of applications powered by large language models. You will explore four layers of AI cost optimization and learn techniques that can be applied to real-world AI products and workflows.
First, you will understand how token-based pricing works, how input and output tokens affect your bill, and how the roles of AI consumers and AI developers differ. You will then learn how to estimate token costs across common large language model services.
Next, you will discover how to control output costs using precise prompt instructions and maximum-token limits. You will also learn how to reduce input costs through sliding context windows, conversation summarization, prompt compression, and retrieval-augmented generation.
The course then explains how exact-match caching, semantic caching, and per-user rate limits can reduce unnecessary API calls. Finally, you will learn how model routing and batch APIs can lower the cost per token while maintaining suitable quality and performance.
By the end of this course, you will be able to
• Identify the main drivers of generative AI costs• Calculate and estimate token-based spending• Reduce unnecessary input and output tokens• Implement caching and rate-limiting strategies• Route tasks to cost-effective AI models• Use batch processing for eligible asynchronous workloads• Build a practical, layered AI cost-reduction strategy
This course is designed for developers, AI practitioners, product managers, technical leaders, entrepreneurs, consultants, and business professionals who want to control generative AI spending without sacrificing useful outcomes.
Basic familiarity with generative AI tools is helpful, but advanced AI or data science experience is not required.
Who this course is for
Business leaders, managers, and decision-makers responsible for AI budgets or strategy
Product managers, AI practitioners, developers, and technical team leaders.
Entrepreneurs and consultants seeking to deliver cost-effective AI solutions
Finance, operations, and procurement professionals overseeing AI investments.



   
   
   

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