How Cost Actually Works
A layer that picks the cheapest capable model for each request, saving cost and latency. An AI router inspects the incoming prompt and dispatches it to the smallest model that can handle it — a small local model for classification, a mid-tier for drafting, a frontier model only for hard reasoning. This guide pulls together everything on Onexial tagged cost — 12 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind Cost
Before wiring anything together, the vocabulary has to be precise. These 6 definitions cover the terms that show up in almost every Cost discussion — each one links to a full entry with an example and its own connections inside the graph.
AI Router
A layer that picks the cheapest capable model for each request, saving cost and latency.
Agent Cost Control
Agent cost control is the practice of budgeting tokens, steps and model tiers per task so autonomous systems stay economically viable at scale.
Small Language Model (SLM)
A language model with roughly 1–15B parameters, small enough to run on a laptop, phone or single GPU while handling narrow tasks well.
Prompt Caching
Reusing the processed form of a repeated prompt prefix so later requests are cheaper and faster.
Semantic Cache
A cache that returns a stored LLM answer when a new question means the same thing as an earlier one, even if worded differently.
Knowledge Distillation
Training a small model to imitate the outputs of a large one, keeping most of the quality on a target task at a fraction of the cost.
Workflows: how Cost runs end to end
Concepts only matter once they become a repeatable process. Below are 3 documented workflows that apply Cost to a concrete problem, with the steps, the tools involved and the variations worth testing.
Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
Cut LLM Inference Costs
A step-by-step method to reduce model spend without degrading output quality.
Route Requests Between Models
Send each request to the cheapest model that can handle it, escalating only when needed.
The Cost tool stack
A stack is a set of tools chosen for one job, not a list of favourites. These 1 stacks show which combinations hold up in production for Cost, and what each layer is actually responsible for.
Prompts you can reuse
Prompts are reusable components. Each of these 1 prompts is written to be dropped into a Cost workflow with minimal editing, including the context it expects and an example output.
Real applications of Cost
Finally, 1 applied use cases: the situation, the system used to solve it, and the outcome. This is the layer that turns Cost from an idea into leverage.
Frequently asked questions
- What is AI Router?
- An AI router inspects the incoming prompt and dispatches it to the smallest model that can handle it — a small local model for classification, a mid-tier for drafting, a frontier model only for hard reasoning. Products: OpenRouter, Portkey, LiteLLM.
- What is an example of AI Router?
- A support agent routes 80% of tickets to GPT-4o mini, 15% to GPT-4o, and 5% to Claude Opus, cutting bill by ~70% with no quality drop.
- Why does AI Router matter?
- A layer that picks the cheapest capable model for each request, saving cost and latency.
- What should I learn after AI Router?
- Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
- What is Agent Cost Control?
- Agents fail commercially before they fail technically. Cost control means: routing cheap steps to small models, capping steps per run, caching stable context, trimming tool outputs before they re-enter the prompt, and tracking cost-per-completed-task rather than cost-per-token.
- What is an example of Agent Cost Control?
- Routing classification to a small model and reserving the frontier model for final synthesis cut a research agent from $0.42 to $0.07 per report.
- Why does Agent Cost Control matter?
- Agent cost control is the practice of budgeting tokens, steps and model tiers per task so autonomous systems stay economically viable at scale.
- What should I learn after Agent Cost Control?
- Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.