The outcome
Learn the interaction loop using a small task with a clear outcome.
Replicate is a hosted API platform for running and deploying machine-learning models. Testing public generative models, integrating versioned predictions, running asynchronous media jobs, and deploying custom model versions. 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.
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.
- 01
Choose a model after reviewing its owner, README, license, schema, examples, and exact version.
- 02
Run representative low-risk inputs in the web interface and record settings, quality, time, and cost.
- 03
Create a server-side API token and reproduce the selected prediction with an explicit version when supported.
- 04
Persist the prediction ID and copy any required output files to durable storage before their API retention expires.
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.
Community models vary in quality, licensing, and safety. Protect API tokens, pin versions, validate inputs and outputs, make webhook processing idempotent, and do not assume generated files remain available permanently.
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.