Cost Optimization
15 articles about cost optimization.
Logging Is Slowly Bankrupting Me - Debug Logging in AI Agent Systems
When debug logging becomes a cost problem in AI agent systems - how verbose logs eat tokens, inflate context windows, and silently drain your budget.
The Five Logs Every Cron-Scheduled AI Agent Needs
Actions, rejections, handoffs, costs, and verification - the five essential logs for cron-scheduled AI agents. How a cost log exposed 40% waste in our agent
GTC 2026: Inference Is Eating the World
Inference is a recurring cost, not a one-time expense. Every agent action costs tokens. Minimizing LLM round trips is the key to sustainable agent economics.
Why Token Limits Never Add Up When Running Parallel AI Agents
Running parallel agents on a macOS app build reveals that token math is misleading. Context overhead, compiler loops, and shared file reads consume far more
Multi-LLM Agent Routing - Using Different Models for Different Subtasks
How AI agents route between multiple LLMs - using Claude for orchestration, smaller models for classification, and specialized models for code generation or
Claude Orchestrates GPT and Gemini - Multi-Model Routing for Desktop Automation
Use Claude for planning and reasoning, route execution tasks to cheaper models like GPT or Gemini. Multi-model orchestration cuts costs without sacrificing
Opus vs Sonnet for Claude Code - Choosing the Right Model for Each Command
When to use Claude Opus vs Sonnet for different Claude Code tasks. Save Opus for implementation, use Sonnet for init, planning, and routine operations.
When Sonnet Outperforms Opus - Choosing the Right AI Model Tier
Sonnet vs Opus for coding tasks - when the cheaper, faster model produces better results. Benchmarks, cost comparison, and a practical routing guide for daily AI coding work.
When Cheaper AI Models Are Good Enough for Daily Development
Sonnet handles Python wrappers and routine coding just fine. Opus shines for architecture decisions. How to route AI model usage by task complexity and save
Tips for Secondary Models - When to Use Haiku vs Opus in AI Agents
Choosing the right model tier for different AI agent tasks saves money without sacrificing quality. Learn when to use cheap models like Haiku and when to
Wonder Behind a Load Balancer - Routing Models by Task Complexity
Load balancing between AI models by task complexity cuts costs without sacrificing quality. Route simple tasks to cheap models and complex tasks to capable
Claude Code Burned All My Tokens in 30 Minutes - Why Narrow Scoping Fixes This
Running 5 agents in parallel on your codebase without narrow scoping burns through tokens in minutes. Each agent needs a very specific scope to be
LLM Pricing: How Personal Cost Awareness Changes Model Selection
When you pay for LLM usage out of pocket, you develop a sharp sense for which tasks justify Opus vs Sonnet. Here is how personal cost awareness changes
Optimizing 23 AI Agent Cron Jobs from $14/Day to $3/Day
Practical cost reduction for AI agent cron jobs - how we cut daily spend from $14 to $3 by optimizing prompts, routing models, and batching tasks.
Using Opus as Orchestrator, Delegating to Sonnet and Haiku
The real win of using Opus as an orchestrator that delegates to Sonnet and Haiku is not cost savings - it is context window management. Opus burns through