Token Optimization
Tokens are the currency of AI - and in 2026, organizations everywhere are auditing how they spend them. This course teaches you to measure, manage, and dramatically reduce token usage across chatbots, RAG pipelines, and agentic workloads - the engineering discipline that decides whether AI scales with your business or gets cancelled by your CFO.
Course Lessons
From the economics to the engineering to the governance - follow in order or jump to any topic.
1. The Token Reckoning
Why AI costs became a board-level topic, why bills surprise organizations, and why token optimization is now a core engineering skill.
2. Token Economics
Subscriptions vs. API vs. enterprise agreements. How token pricing really works, and how to compare models on total cost.
3. The Agentic Token Explosion
Why agents burn 10-100× the tokens of simple chat, the quadratic context trap, and the five patterns that tame agent spend.
4. Context Engineering
The context window is prime real estate. Context budgets, history management, and the RAG vs. long-context decision.
5. Caching, Batching & Routing
The three architectural levers that cut bills 50-90% without touching a single prompt - and how they compound.
6. Output Token Control
Output tokens cost 3-5× input, yet most teams never tune them. The fastest savings in this course live here.
7. Measurement & Governance
The AI gateway pattern, the metrics that matter, budgets and circuit breakers, and chargeback that makes optimization stick.
8. The Optimization Playbook
The maturity model, the 30-day plan, the pre-cancellation checklist, and ten rules to remember.
What You Will Learn
By the end of this course, you will be able to:
Predict and Compare AI Costs
Understand how seats, API tokens, and enterprise agreements really price out - and negotiate from data.
Tame Agentic Spend
Bound agent loops, compress tool results, and right-size models inside agents before bills explode.
Stack the Big Levers
Combine caching, batching, and model routing for 70-90% reductions versus the naïve baseline.
Govern Token Spend
Build the observability, budgets, and review culture that keep costs optimized after the cleanup ends.
Go Deeper: Companion Courses
This is the strategic layer. These hands-on courses are the deep dives it builds on.
Guide: Claude API Costs
What the Claude API actually costs in June 2026 - real prices, caching math, and four worked scenarios.
Tokens in AI
Tokenizer fundamentals: how text becomes tokens, counting, context windows, and pricing mechanics.
AI Token Efficiency
Hands-on prompt-level techniques: compression before/afters, caching strategies, and routing examples.
Prompt Caching
Vendor-specific cache mechanics for Anthropic and OpenAI, implementation patterns, and cost math.
AI Cost Management
Budgeting, cost tracking, and token pricing practices for teams running AI in production.
Prompt Patterns That Survive Production
The reliability layer: output contracts, failure-mode diagnosis, and the 25-point pre-deploy checklist that pairs with this course’s cost discipline.
AI Agent Frameworks in Practice
Agents burn 10-100× the tokens of simple chat. See LangGraph, CrewAI, and OpenAI Agents SDK compared - and apply the token lessons to real framework code.
Production Readiness Runbook for LLM Systems
The operations layer that pairs with cost control: monitoring, incident response, rollback strategies, and deployment gates to keep optimized systems running reliably.
Fully Open Source AI Models
The $0/token path: model selection, licensing, local inference with Ollama and vLLM, fine-tuning, and production deployment of open source LLMs.
Go Deeper With Expert Courses
Recommended learning resources from our partners. Affiliate disclosure.
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