The outcome

Provide predictable inference for steady or customized workloads without leaving idle resources unmanaged.

Together AI is a cloud platform for running, evaluating, fine-tuning, and deploying open generative AI models. Comparing and serving open models through serverless inference, dedicated endpoints, evaluations, fine-tuning, and managed GPU infrastructure. 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

    Measure serverless traffic, latency, errors, utilization pattern, and cost, then define the dedicated endpoint's service objective.

  2. 02

    Confirm model eligibility, license, hardware choices, regional needs, expected replicas, and hourly cost under each scaling scenario.

  3. 03

    Create a staging endpoint with credentials stored in a secret manager and exercise it through the same inference API.

  4. 04

    Load-test realistic concurrency, cold starts, scaling, timeouts, failures, and output quality before routing production traffic.

  5. 05

    Roll out gradually, monitor utilization and spend, and stop or resize unused endpoints using a documented rollback path.

Working standard

What good use looks like.

  • Justify dedicated capacity with measurements.
  • Load-test before cutover.
  • Stop idle endpoints deliberately.

Model availability, behavior, licensing, rate limits, and price vary. Protect credentials and datasets, evaluate outputs, isolate environments, monitor spend, and stop dedicated capacity when it is not needed.

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.