The Challenges of Scaling - Why “More” Can Sometimes Be Less

When a system starts to grow - more users, more data, more requests - we need it to handle the load. This is where the concept of Scaling comes in: how to make the system handle more work without breaking.

But here’s the thing - adding “more power” doesn’t always improve performance.

Two Types of Scaling

Vertical Scaling

Strengthening the machine itself - adding more memory, more cores, a stronger processor. It’s like upgrading your computer - it can handle more tasks, but there’s a physical limit to how much it can grow.

Horizontal Scaling

Adding more machines - each handling part of the load. It’s like opening more checkout lines in a supermarket: each one shortens the queue a bit.

When Does It Work Well?

• When requests are independent of each other - for example, processing different images or separate inference requests.
• When the system knows how to distribute the work intelligently, without one server waiting for another.
• When there isn’t much “communication” between machines - meaning each can simply do its own thing.

In such cases, as more instances are added, the overall throughput truly increases.

And When Does It Hurt Performance?

• When there are dependencies between processes - if each machine needs to wait for another to finish.
• When new bottlenecks are created - for example, everyone accessing the same database or network.
• When communication between machines becomes expensive - sometimes coordinating them takes more time than the actual processing.

In other words: sometimes adding more machines is like adding more people to a team, but without planning how they work together - they just get in each other’s way.

How Do You Know If Scaling Is Really Helping?

Two important metrics:

• Throughput - How much work the system completes in a given time.
• Latency - How long it takes to process a single item.

If Throughput increases without Latency growing - scaling is working well. If Throughput stalls or Latency spikes - it’s a sign to stop and check the architecture.

Conclusion

Scaling isn’t magic. It’s a great tool when you understand where the bottleneck is and how to split the work correctly. Because in the end, you don’t always need “more power” - sometimes you just need smarter distribution.