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TensorFlow Lite vs Core ML
On-device ML for mobile apps — cross-platform vs Apple-native
14 min read•Tools: TensorFlow Lite, Core ML•Updated Feb 2026
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TensorFlow LiteC
Core MLQuick Recommendation
TensorFlow Lite
Best for Cross-PlatformChoose if you need:
- ✓You need the same model on both iOS and Android
- ✓You are building a React Native app with cross-platform ML
- ✓Your models are trained in TensorFlow or Keras
- ✓You need GPU delegate and NNAPI support on Android
Core ML
Best for Apple DevicesChoose if you need:
- ✓You are building an iOS-only or Apple ecosystem app
- ✓You want maximum performance on Apple Neural Engine
- ✓You need tight SwiftUI integration with zero overhead
- ✓You want to use Apple's built-in Vision and NLP frameworks
Side-by-Side Comparison
| Feature | TensorFlow Lite | Core ML |
|---|---|---|
| Platform Support | iOS, Android, Linux, microcontrollers | iOS, macOS, watchOS, tvOS, visionOS |
| Hardware Acceleration | GPU delegate, NNAPI, Hexagon DSP | Apple Neural Engine, GPU, CPU |
| Model Format | .tflite (FlatBuffers) | .mlmodel / .mlpackage |
| Model Conversion | TF Lite Converter from TF/Keras | coremltools from PyTorch/TF/ONNX |
| Model Size Optimization | Quantization, pruning, clustering | Quantization, palettization, pruning |
| React Native Support | react-native-tflite (community) | Requires native bridge module |
| On-Device Training | Limited (transfer learning) | Supported (personalization) |
| Typical Latency | 5-15ms (MobileNet, Pixel 8) | 2-8ms (MobileNet, iPhone 15) |
Our Verdict
For React Native teams building cross-platform apps, TensorFlow Lite is the clear winner since it runs the same model on both iOS and Android with a single integration path. Core ML delivers superior performance on Apple hardware and is the right choice for iOS-only apps where every millisecond of inference latency counts. At Mendios, we typically use TF Lite for cross-platform React Native apps and Core ML only when building native iOS experiences.
Frequently Asked Questions
Need help choosing between TensorFlow Lite and Core ML?
Our engineers have production experience with both tools. We can help you make the right choice based on your specific requirements, timeline, and budget.