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# Overview
TensorFlow was designed to be a good deep learning solution for mobile
platforms. Currently we have two solutions for deploying machine learning
applications on mobile and embedded devices:
@{$mobile/mobile_intro$TensorFlow for Mobile} and @{$mobile/tflite$TensorFlow Lite}.
## TensorFlow Lite versus TensorFlow Mobile
Here are a few of the differences between the two:
- TensorFlow Lite is an evolution of TensorFlow Mobile. In most cases, apps
developed with TensorFlow Lite will have a smaller binary size, fewer
dependencies, and better performance.
- TensorFlow Lite is in developer preview, so not all use cases are covered yet.
We expect you to use TensorFlow Mobile to cover production cases.
- TensorFlow Lite supports only a limited set of operators, so not all models
will work on it by default. TensorFlow for Mobile has a fuller set of
supported functionality.
TensorFlow Lite provides better performance and a small binary size on mobile
platforms as well as the ability to leverage hardware acceleration if available
on their platforms. In addition, it has many fewer dependencies so it can be
built and hosted on simpler, more constrained device scenarios. TensorFlow Lite
also allows targeting accelerators through the [Neural Networks
API](https://developer.android.com/ndk/guides/neuralnetworks/index.html).
TensorFlow Lite currently has coverage for a limited set of operators. While
TensorFlow for Mobile supports only a constrained set of ops by default, in
principle if you use an arbitrary operator in TensorFlow, it can be customized
to build that kernel. Thus use cases which are not currently supported by
TensorFlow Lite should continue to use TensorFlow for Mobile. As TensorFlow Lite
evolves, it will gain additional operators, and the decision will be easier to
make.
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