Python's __slots__ can reduce memory usage by up to 50% for large object classes

If you're instantiating millions of objects, defining __slots__ in your class
prevents Python from creating a per-instance __dict__ — saving significant memory.

Example

class Point:
    __slots__ = ('x', 'y')
    def __init__(self, x, y):
        self.x, self.y = x, y

Benchmarks show ~40–50% RAM reduction vs. a regular class when creating 1M+ instances.

Why It Works

• Normal classes store instance attributes in a __dict__, which has overhead
• __slots__ replaces that dict with fixed-size memory slots per attribute
• Less memory allocation = faster GC and better cache locality too

Tradeoffs

• ❌ You lose dynamic attribute assignment (obj.new_attr = val raises AttributeError)
• ❌ Pickling and multiple inheritance need extra care
• ❌ Not worth it for small scripts — only shines at volume

Best Use Cases

✅ High-throughput data pipelines

✅ Game entity systems

✅ ML feature objects

✅ Anything spawning massive numbers of instances

Tested on CPython 3.11/3.12. Results vary slightly by platform.