Inference tutorial
If you are learning how to use baseten inference, start with a model you can access and one small test input. This guide follows the request from preparation through response checks, without assuming a particular model's input format.
Follow this sequence for one model and one representative input before connecting inference to a larger application.
Identify the model you intend to call and read its current input and output instructions. A text model, image model, and embedding model may expect different request fields.
Use the access method shown for that model, provide a minimal valid payload, and keep credentials out of source files and browser-exposed code.
Check whether the request succeeded, then examine the returned data before writing code that assumes a particular response shape.
Check these requirements before your first Baseten inference request; model-specific documentation takes precedence over a generic example.
Identify an accessible model and its current inference instructions. — Record the model identifier or endpoint exactly as provided for your chosen model.
Prepare the authentication method required by the service you are calling. — Store any secret outside committed code and client-side applications.
Create one input that follows the model's documented schema. — Use a small, representative example rather than a full batch.
Choose a tool that can send a request and display its full response. — An API client or a short server-side script can help you inspect status and returned fields.
Prepare a second input for comparison after the first request works.optional — A contrasting example can expose assumptions in your response-handling code.
These related guides provide context if you are still deciding where inference fits or what to check before deployment.
The first successful request should prove that your model, input, and response handler agree—not merely that a connection exists.
1
Open the instructions for the specific model you selected. Note where the input belongs, which fields are mandatory, and what kind of output is expected. Do not copy a payload from a different model simply because it also runs on Baseten: identical transport does not imply identical model inputs. Keep a short local record of the model and payload you tested so later changes are easier to trace.
2
Send a minimal, valid input through the supported interface for that model. Keep the first Baseten inference test deliberately small: a short text sample for a text task, or an appropriately prepared asset for a model that accepts other media. Capture the request status and any error message without logging secrets. If the call fails, change one variable at a time instead of rewriting the entire integration.
3
Inspect the complete returned structure, not only the field you hoped to receive. Check that the output corresponds to your input and that empty or unexpected results have a safe path through your application. Only after this check should you map the response into your own types or user interface. Repeat the test with a second input to catch hard-coded assumptions.
A generic guide cannot diagnose every model, but these checks narrow down the most common classes of inference failure.
A request cannot succeed if its credentials, model reference, or access permissions are wrong. Do not assume an error proves the model itself is unavailable.
What to do instead
Verify the configured access method and model reference against their current instructions. Rotate an exposed secret rather than reusing it.
Baseten cannot make a model interpret fields or media that do not match that model's documented schema. A valid network request may still contain an unusable payload.
What to do instead
Reduce the payload to a documented example, confirm field names and data types, then restore your own input piece by piece.
Different models can return different structures, and a successful call does not guarantee the field your code expects will be present.
What to do instead
Inspect the full response, validate required fields before reading them, and handle missing or unexpected values explicitly.
This tutorial cannot establish a latency guarantee or identify the cause of every timeout from the client side alone.
What to do instead
Record request timing and non-sensitive error details, test a smaller input, and consult the model's current operational guidance.
Once a single Baseten inference call works, adapt the same verified contract to the environment where you will use it.
Application developers
Keep model-specific request construction in one server-side module. Validate incoming application data before converting it into the model payload, and return a predictable application-level error when inference fails. This makes it possible to change a model or its request mapping without scattering assumptions throughout the codebase.
Model evaluators
Build a small set of representative cases, including one awkward or borderline input. Record which model and request configuration produced each result. When assessing Baseten inference behavior, compare the outputs against your task criteria rather than treating a successful response status as a quality score.
Operations teams
Decide which non-sensitive request details are useful for debugging, and avoid storing credentials or private inputs in routine logs. Track failure categories and response timing in the context of your own application. Recheck the integration when you change the model, payload, or application-side timeout settings.
Use the verified-request approach: choose a model, follow its current instructions, test a small input, and inspect the result before integrating it into an application. The destination may have its own access requirements and model-specific guidance.
Choose a model you can access and read its current inference instructions. Prepare a minimal input that matches its schema, send one request through the supported interface, and inspect the complete response before connecting application logic.
Confirm the model reference, required access method, and documented input format. Keep any credentials out of client-side code and use a request tool that lets you inspect errors as well as successful output.
Start by distinguishing an access problem from an invalid payload or a response-handling problem. Compare the model reference and input fields with the current instructions, then retry with the smallest documented input you can use.
First inspect the response from a working test request and identify the fields your application actually needs. Validate those fields before using them, handle missing or unexpected values, and keep model-specific parsing in one place so it can be updated.