The outcome

Operate a resilient inference endpoint with least-privilege access, bounded spend, observable failures, and human control over consequences.

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

    Map every existing dependency on model names, tokenization, tools, JSON schema, streaming, errors, moderation, retries, logging, and provider-specific behavior.

  2. 02

    Create a named server-side token with allowed IPs where appropriate, separate environments, minimize submitted data, and review partner-model and bulk-retention exceptions.

  3. 03

    Change the base URL in a test environment, pin the chosen model, enforce input and output limits, timeouts, backoff, idempotency, concurrency, and budget alerts.

  4. 04

    Run contract, load, prompt-injection, privacy, outage, rate-limit, malformed-output, model-removal, and fallback tests while recording request metadata without sensitive content.

  5. 05

    Canary to limited traffic, compare production metrics, keep consequential tools behind authorization and validation, document rollback, and rotate tokens and reevaluate models regularly.

Working standard

What good use looks like.

  • Treat compatibility as interface only.
  • Use separate scoped environment tokens.
  • Canary before full traffic.

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.