The outcome

Pin a fit-for-purpose model and fallback with measured operating limits.

Hyperbolic is a AI compute platform for serverless open-model inference and on-demand GPU marketplace workloads. Testing and serving open text, image, audio, and vision-language models through serverless APIs, or renting GPU capacity for controlled model and compute workloads. 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, users, data class, modalities, success and failure metrics, latency, context, output schema, safety, license, retention, cost, and rollback thresholds.

  2. 02

    Shortlist current base or instruct models and record exact IDs, versions, licenses, context, pricing, rate limits, moderation, and supported parameters.

  3. 03

    Run a frozen representative set with server-side keys, equal prompts and settings, constrained output, captured usage, errors, latency, and repeat trials where nondeterminism matters.

  4. 04

    Score correctness, safety, refusals, bias, schema validity, hallucination, tail latency, failure rate, and total task cost, investigating item-level regressions.

  5. 05

    Pin the winner, configure rate and budget limits plus a tested fallback, canary it, and rerun the benchmark before any model or parameter change.

Working standard

What good use looks like.

  • Record exact model IDs and licenses.
  • Measure tail latency and task cost.
  • Pin a tested fallback.

OpenAI compatibility covers interface shape, not model behavior, moderation, licensing, context, or reliability. Zero-retention serverless claims do not automatically govern software and data placed on a rented GPU. Treat marketplace machines as untrusted infrastructure until verified, use SSH keys and least privilege, encrypt and delete data, pin dependencies and models, control spend, and never expose credentials or management ports.

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.