Developer Guide

Service Tier

The following service tiers are supported for each API. Service tiers define the backend that will service your API call and the infrastructure used to run your model inference.


This service tier means that your API inference will run on cpu instance. It is the default service tier for each API.


This service tier means that your API inference will run on gpu instance. You have to add the query parameter “serviceTier=gpuflex” to an API endpoint to use gpu instance, see table below.

API endpoint

Service Tier




On the model card if you click the “FlexGPU” button the API URL is updated to show the gpuflex url


You need an API Key to invoke a model API. You can generate an API key for your account from the API Keys page. You pass in this API key as a Bearer token with the Authorization HTTP header in your http request.

The following example shows how you can pass in the API key in cURL (replace ‘YOUR_API_KEY’ with your key). Our github code samples repo has examples for other programming languages for your reference.

 curl --request POST \
--url \
--header 'accept: */*' \
--header 'authorization: Bearer YOUR_API_KEY' \
--header 'content-type: application/json' \
--data '{"input": "I feel the need - the need for speed!"}'

Model Type

There are literally thousands of models available on Tiyaro. Models built using Tensorflow, PyTorch, Transformers just to name a few frameworks. Models from various github repos. We even have models from various SaaS providers. At Tiyaro we have cataloged and normalized all these various models into specific Model Types. Each model type has a specific input and output signature. What this means is that as a developer you only have to know the ‘Type’ of a model to understand how you can invoke that model (input signature) and what response you can expect from it (output signature).

Model types not only provide a uniform way to invoke models of the same type. But it also allows you to compare and run experiments on different models of the same type. Thus allowing you to quickly narrow down the model that works best for your use case and for your data. All with the assurance that if there is a newer model of the same type that comes along tomorrow, you wont have to change the API inputs or process its outputs differently. All you need to do is use the API URL of the new model.

You can find out the Model Type for a model from its Model card.

Some of the Model Types are
  • summarization

  • fill-mask

  • image-object-detection

  • image-classification

  • translation

  • text-classification

  • question-answering

  • audio-classification

  • automatic-speech-recognition


The API reference is organized by Model Types. If you are looking for the API reference for a specific model, find out its model type and then use the API reference.

Open API Specification

We have published an OpenAPI 3.0 spec for each model that is available in Tiyaro. The model card for that model has pointers on how you can download the spec.

See below the ‘API Specification’ available in the ‘Developer Toolbox’ on the model card


Code Samples

Our github code samples repo includes full working samples for invoking the inference APIs supported by Tiyaro in multiple languages. The samples are all self explanatory and are organized by the various Model Types.

Here is a python example from the repo that invokes an image-object-detection model with a local image that is converted to the base64 format as expected by this API

#!/usr/bin/env python

Sample code to run object detection with local image as input

import requests
import os
import sys
import base64

def imageToBase64(srcPath):
   with open(srcPath, 'rb') as image:
      b64Img = base64.b64encode('utf-8')
   return b64Img

def infer():
   # Get the API key for invoking Tiyaro API
   apiKey = os.getenv("TIYARO_API_KEY")
   if apiKey is None:
      print("Please set TIYARO_API_KEY environment variable. You can generate your API key from here -")

   # API endpoint
   url = ""

   # Convert binary image to base64
   imgPath = "../../testdata/object-detect-1.jpg"
   b64Img = imageToBase64(imgPath)

   payload = {"imageRef": {"Bytes": b64Img}}
   headers = {
      "accept": "*/*",
      "content-type": "application/json",
      "authorization": f"Bearer {apiKey}"

   response = requests.request("POST", url, json=payload, headers=headers)
   # Check for errors

   # Inference response

if __name__ == "__main__":