Unit economics

AI API Cost Optimization

Reduce token spend with model selection, prompt caching, context control, workload routing and cost-per-task measurement.

Lower cost without hiding quality or reliability trade-offs.

Learn in the order developers build.

Start with the decision, move into implementation and finish with production checks.

Structured learning path

Measure the task

Track total cost per successful user outcome.

Choose the tier

Use premium models only where quality changes the outcome.

Control context

Trim retrieval and cache stable prompt prefixes.

Enforce budgets

Set per-request and per-workflow limits before execution.

Build topical authority, not article islands.

Core guides

5 guided starting points

Pricing01

How Token Pricing Works

Input, output, cache and tool fees explained.

Open guide

Selection02

Model Selection by Workload

Match model capability to business value and failure cost.

Open guide

Caching03

Prompt Caching Guide

Reduce repeated context cost with stable prefixes.

Open guide

Optimization04

Reduce LLM Context Cost

Control retrieval, conversation history and document input.

Open guide

Cost lab05

Budgeting AI Agent Runs

Estimate multi-step tool and token costs before launch.

Open guide

Reduce support automation cost

Route simple classification to a lightweight model and reserve premium reasoning for escalated cases.

Measure cost per resolved ticket

Cache policy and product context

Route by task complexity

Monitor correction rate

Turn the guide into a request.

Try a model in the Playground, then use the Quickstart to move the same pattern into Python or Node.js.

Try without coding Open Quickstart

Explore every growth topic.

AI API Guides

Go from model shortlist to a working production request.

LLM Infrastructure

Build an AI request layer that survives provider and model changes.

AI Agent

Move from a prompt demo to an observable agent workflow.

Cost Optimization

Lower cost without hiding quality or reliability trade-offs.

Model Comparison

Select a model based on the job, not the leaderboard headline.

Tutorials

Copy a working pattern, understand it and adapt it to your application.

Frequently asked questions

AEO-ready answers

What is the best metric for LLM cost?

Cost per successful task is more useful than price per million tokens because it includes retries, output length and quality failures.

Does a lower-priced model always reduce cost?

No. A lower-priced model can increase total cost when it needs more retries, longer prompts, or human correction.