The outcome

Learn the interaction loop using a small task with a clear outcome.

DeepInfra is a AI inference cloud with OpenAI-compatible and native APIs, serverless models, private deployments, and GPU infrastructure. Serving language, embedding, reranking, vision, image, video, and speech models through a common API; testing model alternatives; and deploying private models at controlled scale. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Define the task, data class, quality metrics, latency, context, output schema, region, model license, safety policy, cost ceiling, and rollback criteria.

  2. 02

    Choose a small candidate set from the current catalog, noting deprecation, partner routing, context limits, pricing, and whether OpenAI-compatible or native endpoints fit.

  3. 03

    Run a versioned evaluation set with server-side credentials, deterministic settings where possible, usage logging, retries, and no automatic production actions.

  4. 04

    Compare quality, safety, privacy path, latency, failures, and cost, then pin the selected model, monitor drift and deprecation, and retain a tested fallback.

Working standard

What good use looks like.

  • State the outcome before the background.
  • Provide the real source material.
  • Review the result before expanding the task.

A compatible API does not make models interchangeable: schemas, licenses, moderation, tool behavior, context, pricing, and output quality vary. DeepInfra generally keeps inference input and output in memory, but image, bulk, debugging, Google, Anthropic, and other stated partner paths can differ. Keep tokens server-side, minimize personal data, verify the selected model's provider policy, and never route model output directly into consequential actions.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.