Use the API
Create an API key
To obtain an API key to use with your Fastino model, log in to the Fastino platform. Once logged in, navigate to the "Keys" section on the sidebar. From this section, you can create keys as needed to suit your requirements.
The ID visible in your account is not the API key itself; it is merely an identifier. Store your keys in a safe place, you will not be able to retrieve them again after creating.
When generating a new API key please wait for 60 seconds for the key to propagate

Call the API
Fastino's inference API is designed to provide a seamless integration for deploying and running AI models with ease. With a simple POST
request to the endpoint https://api.fastino.com/run
, users can efficiently execute their AI models in real-time. The API requires standard authentication, utilizing a bearer token for security, and operates with a straightforward JSON payload that includes essential user details. Suitable for a wide range of AI applications, Fastino's platform ensures quick and reliable model inference, making it an ideal solution for developers looking to leverage powerful AI capabilities into their applications effortlessly.
Endpoint
POST
https://api.fastino.com/run
Headers
x-api-key
Your API key generated on the platform (see Create an API key)
Content-Type
application/json
Body
model_id
string
Unique identifier for the model. This value can be copied from the Models
screen on the platform UI.
input
array of objects
An array where each object represents a single model inference call. For PRO and TEAM plans, multiple objects can be included to perform batch inference in a single request.
input > text
string
The text being processed by the language model
input > parameters
object
The parameters specifying how the model should process the text. Parameters vary depending on the expected input format of the specific model being used. If not specified, the inference parameters revert to defaults.
{
"model_id": "fastino-pii",
"input": [
{
"text": "9 AM for a Sedan for Jamie Derran",
"parameters": {
"entity_types": [
"full_name",
"car",
"time"
],
"threshold": 0.3
}
}
]
}
Response
input
string
Original user-provided string.
latency_ms
number
Model inference time (excludes network and API gateway latency)
output
object
The output of the model. Structure varies depending on the model being used.
status
string
Overall request status (e.g., success, error)
[{
"input": "9 AM for a Sedan for Jamie Derran",
"latency_ms": 77.31,
"message": "Responses from endpoint.",
"output": {
"entities": [
{
"start": 0,
"end": 4,
"label": "time",
"text": "9 AM",
"score": 0.5893954038619995
},
{
"start": 11,
"end": 16,
"label": "car",
"text": "Sedan",
"score": 0.8889331817626953
},
{
"start": 21,
"end": 33,
"label": "full_name",
"text": "Jamie Derran",
"score": 0.9932531714439392
}
],
"redacted_text": "<TIME> for a <CAR> for <FULL_NAME>"
},
"status": "success"
}]
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