The signal that you've outgrown the list
Sequential is the right tool right up until your architecture stops being a straight line — and the failure is honest: it's not that Sequential gets hard, it's that it becomes impossible. A list literally cannot express a fork. So the question isn't "is Sequential good enough?" but "does my data flow branch?" Any one of these is the signal to move on:
- Multiple inputs — text + image, metadata + time series fused in one model.
- Multiple outputs — e.g. a classification head and a regression head sharing a backbone.
- Skip connections — ResNet-style residual blocks where a layer's input is added back to its output.
- Shared layers — the same weights applied to two inputs (Siamese networks, twin encoders).
- Any non-linear topology — anything that isn't a single unbroken chain.
The complexity ladder, not a hierarchy
Keras gives you three model-building APIs, and they form a ladder: Sequential → Functional → Subclassing. The Functional API (Track 4) covers everything above — it represents a model as a graph of layers, so forks, merges, and shared weights all become natural. When even a static graph isn't enough — you need if branches in the forward pass, dynamic shapes, or a fully custom training step — you drop to Model subclassing and write the call() method yourself.
The key mindset: each rung up buys expressiveness at the cost of boilerplate. Climbing too high wastes effort on flexibility you don't use; staying too low constrains the model you can build. Pick the lowest rung that fits your data flow — and note that "Sequential" being the bottom rung is a compliment, not a demotion. For a straight pipeline it remains the best choice in the whole library.