AI inference cloud

How to use
DeepInfra.

Serving language, embedding, reranking, vision, image, video, and speech models through a common API; testing model alternatives; and deploying private models at controlled scale.

Open DeepInfra ↗ Official download ↗
What it isAI inference cloud with OpenAI-compatible and native APIs, serverless models, private deployments, and GPU infrastructure Workflows2 UpdatedJuly 2026

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 covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.

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.

01

Access & setup

Find, install, and sign in to DeepInfra

Get into the official DeepInfra experience with the right account and a setup you understand.

  1. 01

    Start at https://deepinfra.com/ and confirm the domain before entering account or payment information.

  2. 02

    Availability: DeepInfra is a browser-managed cloud service accessed through OpenAI-compatible or native APIs, with serverless inference, dedicated private models, and GPU clusters.

  3. 03

    A DeepInfra account and server-side bearer token, a selected model and license, budgets and rate controls, evaluated prompts and outputs, and a reviewed data path for partner models or bulk inference.

  4. 04

    Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.

  5. 05

    Run one low-risk test task before connecting sensitive files, repositories, or workspace data.

  • Use official download pages.
  • Review permissions during setup.
  • Keep installers and applications updated.
02

First session

Your first useful DeepInfra session

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

  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.

  • State the outcome before the background.
  • Provide the real source material.
  • Review the result before expanding the task.
03

Quality control

Check the quality of DeepInfra output

Establish that a change is safe to run in production and reversible if it is not.

  1. 01

    Restate the intended end state and the blast radius before applying anything.

  2. 02

    Review the generated configuration line by line against the provider's current documentation.

  3. 03

    Apply to a non-production environment first and confirm the observed result matches the intended one.

  4. 04

    Confirm the rollback path works by actually exercising it, not by assuming it exists.

  5. 05

    Check cost, scaling limits, and network exposure before the change reaches production traffic.

  • Test the rollback, do not assume it.
  • Check what a configuration exposes to the public internet.
  • Watch cost and rate limits as closely as correctness.
04

Privacy & permissions

Use DeepInfra safely

Keep credentials, network exposure, and cost under deliberate control.

  1. 01

    Use scoped, short-lived credentials, and never paste production secrets into a prompt or config file.

  2. 02

    Confirm what each change exposes publicly before applying it, especially storage, databases, and admin endpoints.

  3. 03

    Separate environments so a mistake in development cannot reach production data.

  4. 04

    Set billing alerts and hard quotas before enabling autoscaling or usage-based services.

  5. 05

    Review audit logs and revoke access for integrations that are no longer in use.

  • 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.
  • Follow your organisation's approved-use policy.
  • Never treat fluent output as authorization to act.
05

Core workflows

Step-by-step ways to use DeepInfra for the work it does best.

Each workflow is a separate guide with its own steps and review checkpoints.

01 WorkflowBenchmark an inference model on DeepInfra

Select a model with a versioned evaluation instead of treating catalog popularity or API compatibility as evidence of fitness.

 02 WorkflowDeploy a governed OpenAI-compatible service on DeepInfra

Switch an application to DeepInfra without assuming identical model behavior, privacy paths, schemas, or operational controls.

06

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 guide.

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