LLM Cost Optimization: Reduce AI Spending by 30–70%

Did you know that long AI responses can significantly increase your LLM costs?

In this episode, you'll learn how well-designed system prompts can control output length, reduce unnecessary tokens, and optimize AI spending without sacrificing response quality.

Topics Covered:

• What are output tokens?

• Why output tokens impact cost

• Using system prompts to control response length

• Practical examples with LLM APIs

Did you know that output tokens are often the most expensive part of an LLM API call?

In this episode, you'll learn how the max_tokens parameter controls response length, reduces unnecessary token generation, and helps lower AI costs without affecting answer quality.

• What max_tokens really does

• How output tokens impact API cost

• Cost comparison: with vs without max_tokens

• Measuring token usage from the usage block

• Finding the optimal max_tokens value using real data and percentiles

• Best practices for production AI applications

By the end of this episode, you'll know how to prevent runaway responses and optimize your LLM spending using a simple but powerful technique.

Did you know that every time your chatbot sends the entire conversation history to an LLM, you're paying for all those old messages again?

In this episode, we explore one of the most effective AI cost optimization techniques: the Sliding Window approach. You'll learn:

• Why input token costs grow over time

• The difference between naive conversation history and sliding windows

• How sliding windows reduce input tokens and save money

• When old messages are dropped and why that matters

• The trade-off between cost savings and conversational memory

• Real Python examples and visual demonstrations

? Key Takeaway: Stop paying for old messages. Keep only the most relevant context and significantly reduce your AI costs.

Did you know that sending your entire conversation history with every request can dramatically increase your AI costs?

In this episode, we'll explore Conversation History Summarization, a simple but powerful technique to reduce input tokens while preserving important context.

? In this video, you'll learn:

• Why chat history increases token usage

• How conversation summarization works

• When to replace old messages with a summary

• The difference between full history vs summarized history

• How summarization reduces AI costs and improves scalability

• A simple Python example using Claude API

Did you know that many prompts contain unnecessary words that increase token usage and cost without improving the model's answer?

In this episode, you'll learn how Prompt Compression works by removing filler words, redundant instructions, and unnecessary context while preserving the original meaning.

• What are filler words?

• How prompt compression reduces input tokens

• Real before-and-after prompt examples

• Token savings comparison

• Lower AI costs without sacrificing quality A small prompt improvement can lead to significant savings when running AI applications at scale.

Many AI applications send large amounts of information to the model with every request.

This increases token usage, slows response times, and significantly raises costs. Instead of sending an entire knowledge base, RAG retrieves only the most relevant information and sends that small context to the model.

In this video you'll learn:

• What is Retrieval-Augmented Generation (RAG)

• Why sending entire documents is expensive

• How document chunking works

• How embeddings convert text into vectors

• How ChromaDB stores vectors in a vector database

• How semantic similarity search retrieves relevant chunks

• How augmented prompts are built

• How RAG reduces token usage dramatically

• Why RAG improves accuracy and reduces hallucinations

Learn how exact-match caching reuses responses for identical requests, reducing repeated model calls, response time, and operating costs.

In this episode, you'll learn how Semantic Caching reduces LLM costs by reusing answers for semantically similar questions instead of calling the model every time. Unlike exact caching, semantic caching understands meaning, allowing applications to answer rephrased questions instantly while avoiding unnecessary API calls.

? What you'll learn

• What semantic caching is

• Exact Cache vs Semantic Cache

• Embeddings explained simply

• Cosine Similarity with numerical examples

• Similarity Threshold selection (0.70 vs 0.85 vs 0.95)

• Building a semantic cache using ChromaDB

• Using all-MiniLM-L6-v2 embeddings

• Cache HIT vs Cache MISS workflow

• ChromaDB record structure (Embedding, Document, Metadata)

• Cost savings calculation with real production numbers

• Best practices for deploying semantic caching in production

You’ll learn how rate limiting works, why unrestricted users can quickly increase your token and API expenses, and how two common strategies compare:

1. Fixed Window Rate Limiting Allows a specific number of requests during a fixed period, such as 10 requests per minute. It is simple to implement, but users may briefly exceed the intended rate near the boundary between two windows.

2. Token Bucket Rate Limiting Gives each user a bucket of tokens that refills gradually over time. Each request consumes a token, creating smoother traffic control while still allowing reasonable bursts. In this episode, we also discuss what happens when users exceed their limit, how applications can display clear retry messages, and how per-user limits protect the system without blocking everyone.

By the end, you’ll understand how rate limiting helps you:

• Control the number of AI API calls

• Prevent one user from consuming excessive resources

• Reduce unexpected token costs

• Protect your application from abuse

• Improve stability during high traffic

Instead of sending every request to the most expensive model, the system first analyzes the request and routes it to the most suitable model:

1. Simple requests → fast, low-cost model

2. Moderate requests → balanced model

3. Complex requests → advanced reasoning model

In this episode, you’ll learn:

• How rule-based classification detects query complexity

• How keywords, code patterns, and query length affect routing

• How model routing can reduce monthly AI costs at scale

• Why routing works best together with caching, token limits, and rate limiting

The main idea is simple: Use the cheapest model capable of completing the task correctly, while reserving expensive models for requests that genuinely need deeper reasoning.

In this episode, you will learn:

• What the Batch API is and when to use it

• The difference between synchronous and batch processing

• How to create multiple requests inside one batch

• The purpose of custom_id for matching requests and results

• How to check batch status using retrieve()

• How to list previously created batches

• How to retrieve individual results

• How to cancel and delete a batch

• The meaning of in_progress, canceling, and ended

• Why time.sleep(60) is used when polling the batch status