Inference
Running a trained model to produce outputs.
/ quick answer
Inference is the act of using a model (as opposed to training it). Latency, throughput, and cost per token are the three inference metrics that matter in production. Running a trained model to produce outputs.
What is Inference?
Inference is the act of using a model (as opposed to training it). Latency, throughput, and cost per token are the three inference metrics that matter in production.
What is an example of Inference?
An API call to Claude is one inference request.
Why does Inference matter for AI and automation?
Running a trained model to produce outputs. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
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Related concepts
The vocabulary this page depends on.
- →AI Cost Control
AI cost control is the practice of monitoring, analyzing, and managing the financial expenditures associated with developing, deploying, and operating artificial intelligence systems.
- →AI Router
A layer that picks the cheapest capable model for each request, saving cost and latency.
- →Quantization
Shrinking a model by lowering weight precision.
- →Model Routing
Sending each request to the cheapest model that can handle it.
Related workflows
Turn this into a repeatable process.
- →How to Create a Website with AI
Go from idea to a live, custom-domain website in one afternoon using AI builders.
- →How to Build an AI Content System
A repeatable pipeline that turns one input into publish-ready content across every channel.
- →How to Start a Niche Website with AI
Pick a niche, validate demand, build the site, and publish ranking content using AI end-to-end.
- →Personal Research Assistant Workflow
A repeatable system to research any topic deeply in under 30 minutes.
Related tool stacks
The tools that run it in production.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Comparisons & alternatives
Pick between the options.
- →Groq vs Together AI
Fast open-model inference: throughput vs breadth.
- →Chroma vs Qdrant vs Pinecone
Open-source local vs managed cloud vector databases.
- →ChatGPT vs Claude
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.