Keras

Performance tuning in TensorFlow requires developers to manually fine-tune numerous hyperparameters (learning rate, batch size), optimize data pipelines, and balance model complexity against accuracy. This trial-and-error process is time-consuming and lacks systematic guidance.

Keras' abstraction layer obscures low-level backend details, making it harder to debug complex models. Developers are forced to rely on backend-specific tooling and error messages that surface through multiple abstraction layers, increasing diagnostic time.