Apple CreateML Alternatives | Fritz AI

Create ML vs. Fritz AI Studio

Mobile Machine Learning Technology Comparison

Many engineers who are looking for an ML provider for their mobile project consider Create ML. This is Apple’s official model training platform, purpose-built for on-device iOS models. As such, it is closely connected to other Apple tools and systems. As such, it is a useful option for building initial ML models for iOS deployment, and includes a wide range of model types to start building with, in computer vision, natural language processing, and more. Create ML is also readily-accessible from Xcode, Apple’s IDE for building iOS apps. However, models built with Create ML can’t be deployed to Android, and developers must come to Create ML with fully-constructed and labeled datasets.

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.

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Create ML vs. Fritz AI Product Specs




Data Generation

✅ Labeled synthetic data generation

❌ Data generation not supported

Data Upload

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

✅ Drag-and-drop data upload

Data Labeling

✅ In-app labeler:

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

❌ No in-app labeler

Data Export

✅ Export labeled dataset snapshots

❌ Dataset export not supported


Model Training Interface

✅ No-code

✅ No-code

Advanced Configurations

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

✅ Data augmentation included

Mobile-Specific Optimizations

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

✅ Use-case-based model variants


Model Types Supported

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

✅ Core ML (iOS)

Model Versioning

✅ Keras/Model checkpoints;

✅ Created from training session checkpoint

Model Protection/Encryption

✅ Model encryption available

✅ Model encryption available via Core ML compiler


Model Pre- and Post-Processing

✅ Built-in processing with Fritz SDK

❌ No built-in model pre- and post-processing

Platforms Supported

✅ iOS, Android, Snapchat

✅ iOS


Ground-truth Data Collection

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

✅ Manually collect and label real-world data, and train models on it

Model Iteration

✅ Model checkpoints

✅ Training session checkpoints

Model Updates

✅ Over-the-air model downloads

✅ Export model to CoreML

✅ Automatic model updates


Free Tier

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

✅ Free

Pricing Structure

✅  Subscription-based pricing

✅ Free


Available Support

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

✅ Online & Developer Forums

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.