You can integrate TensorFlow Lite by adding the dependency to your build.gradle file, importing your trained model (.tflite file), and using the TensorFlow Lite Interpreter API to run inference on-device. Android Studio provides model binding features that generate type-safe interfaces for your models automatically.

Yes, ML Kit offers both free on-device APIs and cloud-based APIs. On-device APIs are completely free with no usage limits, while cloud-based APIs have generous free tier limits. This makes it cost-effective for most applications, especially those processing data on-device.

On-device AI models are optimized for mobile hardware and typically use minimal resources. TensorFlow Lite models are quantized and compressed to reduce size and improve inference speed. Performance depends on model complexity, but most common use cases (image classification, object detection) run smoothly on modern devices.

Both options are available! ML Kit provides ready-to-use pre-trained models for common tasks like text recognition, face detection, and barcode scanning. TensorFlow Lite also offers pre-trained models you can use directly. For custom use cases, you can train your own models using TensorFlow or PyTorch and convert them to TensorFlow Lite format.