Many engineers who are looking for an ML provider for their mobile project consider Clarifai, which is a model building and deployment platform designed for ML teams working through the full ML project lifecycle. With in-app tools for data labeling, modeling, and ground-truth data collection, Clarifai’s Portal includes the functionality to build and deploy custom models. Clarifai also includes a range of pre-built models for a variety of business and industry use cases. While Clarifai includes a mobile SDK to incorporate models in iOS and Android apps, Clarifai is primarily designed for cloud-based deployment.
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 | CLARIFAI |
Data Generation | ✅ Labeled synthetic data generation | ❌ Synthetic data generation not supported |
Data Upload | ✅ COCO dataset bulk upload; zip file upload; individual image upload | ✅ Manual data upload |
Data Labeling | ✅ In-app labeler:
| ✅ In-app labeler:
|
Data Export | ✅ Export labeled dataset snapshots | ❌ Not supported |
MODEL TRAINING | ||
Model Training Interface | ✅ No-code | ✅ No-code for pre-built (for custom models, coding is involved) |
Advanced Configurations | ✅ Fine-tune model inputs and outputs, configure performance requirements, and more | ✅ Add or remove concepts/tags to models as you build |
Mobile-Specific Optimizations | ✅ Use-case-based model variants; mobile-optimized model architectures | ✅ Use-case-based model variants |
MODEL MANAGEMENT | ||
Model Types Supported | ✅ Core ML (iOS); TensorFlow Lite (Cross-platform); SnapML (Snapchat) | ✅ Core ML (iOS); TensorFlow Lite (Cross-platform) |
Model Versioning | ✅ Keras/Model checkpoints; | ✅ Model version IDs |
Model Protection/Encryption | ✅ Model encryption available | ❌ Model encryption unavailable |
MODEL DEPLOYMENT | ||
Model Pre- and Post-Processing | ✅ Built-in processing with Fritz SDK | ✅ Built-in processing with Clarifai Mobile SDK |
Platforms Supported | ✅ iOS, Android, Snapchat | ✅ Android & IOS |
MODEL RETRAINING & ITERATION | ||
Ground-truth Data Collection | ✅ Automatically collect and label real-world data, and (re)train models on it | ✅ Automatically collect and label production model data, and (re)train models on it |
Model Iteration | ✅ Model checkpoints | ✅ Model versioning |
Model Updates | ✅ Over-the-air model downloads | ✅ Over-the-air model downloads |
PRICING & PLANS | ||
Free Tier | ✅ Permanent with monthly global usage limits; 14-day free trials for all paid subscription plans | ✅ 5,000 free operations per month |
Pricing Structure | ✅ Subscription-based pricing | ✅ Pay-as-you-go model licensing |
SUPPORT | ||
Available Support | ✅ Plan-based support includes: Community forum, Slack, email, phone, and dedicated success manager | ✅ Chat & online forum, Slack 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.