1
Measure the task
Track total cost per successful user outcome.
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.
Structured learning path
Learn in the order developers build.
Start with the decision, move into implementation and finish with production checks.
2
Choose the tier
Use premium models only where quality changes the outcome.
3
Control context
Trim retrieval and cache stable prompt prefixes.
4
Enforce budgets
Set per-request and per-workflow limits before execution.
Core guides
Build topical authority, not article islands.
Pricing01
How Token Pricing Works
Input, output, cache and tool fees explained.
Open guideSelection02
Model Selection by Workload
Match model capability to business value and failure cost.
Open guideCaching03
Prompt Caching Guide
Reduce repeated context cost with stable prefixes.
Open guideOptimization04
Reduce LLM Context Cost
Control retrieval, conversation history and document input.
Open guideCost lab05
Budgeting AI Agent Runs
Estimate multi-step tool and token costs before launch.
Open guide Use case
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
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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.
AEO-ready answers
Frequently asked questions
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 cheaper model always reduce cost?
No. A cheaper model can increase total cost when it needs more retries, longer prompts or human correction.