Inference
Inference
The process of running input through a trained model to get a prediction or output.
In depth
The process of running input through a trained model to get a prediction or output. When you send a prompt to an API and get a response, that is inference. Inference cost, speed, and latency are key factors when choosing between AI providers and models.
Example
In practice, developers reach for Inference when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
Common questions
What is Inference?
The process of running input through a trained model to get a prediction or output.
Why does Inference matter for AI developers?
Inference sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Inference?
Developers Digest publishes tutorials and videos that cover Inference topics including Inference. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Input vs Output Token Pricing
The split pricing model where LLM APIs charge one rate per million tokens you send (input) and a higher rate per million tokens the model generates (output).
Inference
In-Context Learning
The ability of a language model to learn new tasks from examples or instructions provided in the prompt, without any weight updates or training.
Inference
Speculative Decoding
An inference technique where a small, fast draft model proposes several tokens ahead and the large target model checks them all in one parallel forward pass.
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