Best ML Frameworks for Mobile (2026)
On-device AI for iOS and Android — the complete guide to mobile ML frameworks
Quick Recommendation
TFLite
Best Cross-PlatformChoose if you need:
- ✓You need cross-platform support for iOS and Android
- ✓Your models are trained in TensorFlow or Keras
- ✓You are building with React Native and need one integration path
Core ML
Best for iOSChoose if you need:
- ✓You are building an iOS-only app
- ✓Maximum performance on Apple Neural Engine is critical
- ✓You want seamless SwiftUI and Apple framework integration
ONNX
Most VersatileChoose if you need:
- ✓Your models come from multiple frameworks (PyTorch, TF, scikit-learn)
- ✓You need a universal model format across platforms
- ✓Performance parity with TFLite on Android matters
ML Kit
Easiest to UseChoose if you need:
- ✓You need pre-built ML features (face detection, text recognition, barcode)
- ✓You want a no-ML-expertise-required integration
- ✓Firebase integration is already in your stack
Side-by-Side Comparison
| Feature | TFLite | Core ML | ONNX | ML Kit |
|---|---|---|---|---|
| Platform Support | iOS, Android, Linux | Apple only | iOS, Android, Web | iOS, Android |
| Custom Models | Yes (TF/Keras conversion) | Yes (coremltools) | Yes (universal format) | TFLite custom models |
| Pre-built Models | Task Library (limited) | CreateML templates | ONNX Model Zoo | Extensive (vision, NLP, etc.) |
| React Native Support | react-native-tflite | Native bridge required | onnxruntime-react-native | react-native-mlkit |
| Hardware Acceleration | GPU, NNAPI, Hexagon | ANE, GPU, CPU | NNAPI, CoreML, XNNPACK | Delegates to TFLite |
| Model Optimization | Quantization, pruning | Quantization, palettization | ONNX optimizer, quantization | Automatic (Google-hosted) |
| Avg. Inference (MobileNet) | 8ms (Pixel 8) | 3ms (iPhone 15 Pro) | 9ms (Pixel 8) | 10ms (Pixel 8) |
| Learning Curve | Moderate | Low (Apple ecosystem) | Moderate | Very Low |
Our Verdict
For React Native apps that need custom on-device ML, TensorFlow Lite offers the best combination of cross-platform support and community libraries. Core ML is unbeatable for iOS-only performance. ML Kit is the right choice when you need standard ML features like face detection or OCR without training custom models. ONNX Runtime Mobile is the dark horse -- it accepts models from any framework and is increasingly competitive on both platforms.
Frequently Asked Questions
Need help choosing between TFLite 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.