The outcome

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

FriendliAI is a AI inference platform with serverless model APIs, dedicated GPU endpoints, private containers, observability, and OpenAI-compatible interfaces. Serving open models through instant APIs, deploying pinned Hugging Face models on dedicated GPUs, running private inference containers, and operating production inference with explicit scaling and observability. 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 workload, data class, model and revision, license, throughput, latency, context, GPU, scaling, logging, privacy, availability, budget, and rollback targets.

  2. 02

    Use serverless for evaluation, dedicated endpoints for predictable managed capacity, or containers for stronger infrastructure control, documenting the shared-responsibility boundary.

  3. 03

    Deploy with a scoped server-side key, pinned model revision, conservative token and batch limits, content logging disabled unless approved, and minimum safe replicas.

  4. 04

    Load-test quality, latency, concurrency, cold start, autoscaling, failure, privacy, and cost, then canary the version, monitor it, and retain termination and rollback procedures.

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.

OpenAI compatibility does not guarantee identical tokenization, tools, errors, moderation, or output. Dedicated endpoint management APIs are marked beta, content logging is configurable, and a model repository can change unless its revision is pinned. Verify model rights, keep keys server-side, minimize logged content, separate environments and teams, cap autoscaling spend, test sleeping and failure states, and never update a production endpoint without an evaluated version and rollback.

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.