Many engineers who are looking for an ML provider for their mobile project consider TensorFlow Lite. It’s is a lightweight ML model framework based in TensorFlow, Google’s popular open-source deep learning library. TensorFlow Lite models are built with mobile and edge deployment front and center, and supportive libraries help ML engineers optimize models in terms of size, speed, and prediction accuracy. While an essential part of the mobile ML ecosystem, TensorFlow Lite in and of itself is not a tool to build custom models. As such, developers must manage TensorFlow Lite models on their own, unless they’re managing those models with another external service, or in-house model management systems.
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.
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 NowDATA | FRITZ AI | TENSORFLOW LITE |
Data Generation | ✅ Labeled synthetic data generation | ❌ N/A |
Data Upload | ✅ COCO dataset bulk upload; zip file upload; individual image upload | ❌ N/A |
Data Labeling | ✅ In-app labeler:
| ❌ N/A |
Data Export | ✅ Export labeled dataset snapshots | ❌ N/A |
MODEL TRAINING | ||
Model Training Interface | ✅ No-code | ✅ Code-based conversion of TensorFlow models to TFlite versions |
Advanced Configurations | ✅ Fine-tune model inputs and outputs, configure performance requirements, and more | ✅ Quantize by converting 32-bit floats to more efficient 8-bit integers or run on GPU |
Mobile-Specific Optimizations | ✅ Use-case-based model variants; mobile-optimized model architectures | ✅ Use-case-based model variants ✅ Toolkit for model optimization |
MODEL MANAGEMENT | ||
Model Types Supported | ✅ Core ML (iOS); TensorFlow Lite (Cross-platform): SnapML (Snapchat) | ✅ TensorFlow Lite (Cross-platform) |
Model Versioning | ✅ Keras/Model checkpoints; | ✅ Keras/Model checkpoints; |
Model Protection/Encryption | ✅ Model encryption available | ❌ Model encryption unavailable |
MODEL DEPLOYMENT | ||
Model Pre- and Post-Processing | ✅ Built-in processing with Fritz SDK | ❌ Managed by the developer via TensorFlow |
Platforms Supported | ✅ iOS, Android, Snapchat | ✅ Android, iOS |
MODEL RETRAINING & ITERATION | ||
Ground-truth Data Collection | ✅ Automatically collect and label real-world data, and retrain models on it | ✅ Manually implement, pre-trained models, re-train pre-trained models, or build custom models |
Model Iteration | ✅ Model Checkpoints | ✅ Model Checkpoints |
Model Updates | ✅ Over-the-air model downloads | ❌ Managed by developer ✅ Hosted models are downloaded and extracted automatically |
PRICING & PLANS | ||
Free Tier | ✅ Permanent with monthly global usage limits; 14-day free trials for all paid subscription plans | ✅ Open-Source |
Pricing Structure | ✅ Subscription-based pricing | ✅ Open-Source |
SUPPORT | ||
Available Support | ✅ Plan-based support includes: Community forum, Slack, email, phone, and dedicated success manager | ✅ GitHub, TFLite community |
This free guide breaks down the top mobile ML technologies and reviews them based on key components:
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.