Amazon SageMaker Alternatives | Fritz AI

Amazon SageMaker vs. Fritz AI Studio

Mobile Machine Learning Technology Comparison

Many engineers who are looking for an ML provider for their mobile project consider Amazon SageMaker. It’s a platform to build, train, and deploy machine learning models and is a part of the Amazon Web Services family. The platform includes a selection of tools that span the machine learning project lifecycle, from managing datasets, all the way deploying and managing models in production. Though it does have a toolkit (SageMaker Neo) for deploying models to edge devices like mobile phones, SageMaker is primarily focused on cloud-based deployment, making some of its features limited in terms of helping build and deploy mobile-ready models.

Fritz AI Studio is a machine learning platform built specifically for iOS and Android developers who may not have an ML background. Customers use it to generate and collect labeled datasets, train optimized models without code, deploy and manage on any mobile platforms, and improve models and app UX based on real-world data.

Download the Mobile Machine Learning Technology Comparison Guide

This free guide compares six different ML providers: Amazon SageMaker, Firebase ML, TensorFlow Lite, Create ML, Clarafai, and Fritz AI. See how each provider compares across data management, model training and management, deployment, model iteration, and more.

Download Now

Amazon SageMaker vs. Fritz AI Product Specs




Data Generation

✅ Labeled synthetic data generation

❌ Data generation unavailable

Data Upload

✅ COCO dataset bulk upload; zip file upload; individual image upload

✅ Bulk data upload and management

Data Labeling

✅ In-app labeler:

  • Image Labeling
  • Object Detection
  • Image Segmentation
  • Pose Estimation

✅ Access to labelers through Amazon Mechanical Turk

Data Export

✅ Export labeled dataset snapshots

✅  Export via Jupyter Notebook + RESTful API endpoint


Model Training Interface

✅ No-code

✅ Code via Jupyter Notebooks

✅ No-code via SageMaker Autopilot

Advanced Configurations

✅ Fine-tune model inputs and outputs, configure performance requirements, and more

✅ Automatic model training

Mobile-Specific Optimizations

✅ Use-case-based model variants; mobile-optimized model architectures

✅ SageMaker Neo allows models to be trained once and run on the edge


Model Export Types Supported

✅ Core ML (iOS); TensorFlow Lite (Cross-platform): SnapML (Snapchat)

✅Core ML (iOS) and  TensorFlow Lite (Cross-platform), with conversion via CLI

Model Versioning

✅ Keras/Model checkpoints;

✅ Model checkpoints

Model Protection/Encryption

✅ Model encryption available

❌ Model encryption unavailable


Model Pre- and Post-Processing

✅ Built-in processing with Fritz SDK

 ❌ Manual pre- and post-processing  

Platforms Supported

✅ iOS, Android, Snapchat

✅ iOS, Android


Ground-truth Data Collection

✅ Collect and label real-world data, and retrain models on it

✅ Build and manage accurate training sets using Amazon SageMaker Ground Truth

Model Iteration

✅ Model Checkpoints

✅ Debug & profile training runs

✅ Automatic iteration management

Model Updates

✅ Over-the-air model downloads

❌ Manual model updates required


Free Tier

✅  Permanent with monthly global usage limits; 14-day free trials for all paid subscription plans

✅ Free for two months with usage limits

Pricing Structure

✅  Subscription-based pricing

✅ Usage-based, per training hour


Available Support

✅  Plan-based support includes: Community forum, Slack, email, phone, and dedicated success manager

✅ Ticket-based support

Download the Mobile Machine Learning Technology Comparison Guide

This free guide breaks down the top mobile ML technologies and reviews them based on key components:

  • Data capabilities
  • Model training, management, and deployment
  • Model retraining and iteration
  • Pricing and support options

This guide offers product specs for Amazon SageMaker, Firebase ML, TensorFlow Lite, Create ML, Clarafai, and Fritz AI to help you decide which platform is best for your project.