AI Cost Optimization

Introduction

AI costs scale with users — fast. A viral feature can rack up $50k bills overnight. Treat AI cost like cloud cost — measure, attribute, alert.

Beginner analogy: Like AWS bills in 2010 — most teams discover the cost too late.

Understanding the topic

Core concepts to understand:

• Per-user, per-feature cost tracking.
• Hard caps (budget) per user / org.
• Cheaper models when accuracy allows.
• Cache + batch + prompt-cache aggressively.
• Off-peak inference (some providers offer batch APIs at 50% off).

Syntax reference

Visual workflow / architecture:

Cost = (input_tok × in_price) + (output_tok × out_price)
                       │
              ┌────────┴────────┐
              ▼                 ▼
       Reduce tokens      Reduce price
       - prompt cache     - cheaper model
       - shorter ctx      - batch API
       - dedupe           - open-source

Real-world use

OpenAI Batch API at 50% off for non-urgent work. Anthropic prompt caching for 90% off repeated context. Many startups cut OpenAI bills 80% by switching to Llama on Together AI.

Best practices

• Track cost per user per feature.
• Set hard budgets (kill switch).
• Re-evaluate model choice quarterly — prices keep dropping.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. How do you reduce LLM costs?
• Q2. What's the OpenAI Batch API discount?
• Q3. How do you prevent abuse-driven cost spikes?