Latency is a feature, but most teams treat it like a bug

👋 Hey there,

If you’ve ever sat in a system design meeting and someone says “it’s just a little latency”, this issue is for you.

Latency isn’t just a metric.
It’s not just something to monitor or complain about in logs.
It’s a design constraint, and sometimes, even a feature.

Good system architecture isn’t just about how data flows or where services live. It’s about how time moves through your system.
And more importantly: where time hurts.

This issue is all about understanding latency as a first-class part of architecture, not an afterthought.

💡Latency Is a Feature, Use It Intentionally

We tend to treat latency as a bug.
Something we need to minimize, eliminate, or apologize for.

But the truth is: latency is baked into every system we build, whether we like it or not. The difference between a senior developer and a system architect is whether that latency is incidental or intentional.

Here’s what architects understand that many teams miss:

That’s not just about API response times.
It’s about communication speed between services.
Data propagation delays.
Retry loops.
Human approval steps.
Eventually consistent writes.
Batch jobs that run once a day.

These aren’t accidents. They’re architecture decisions.

And if they’re not made intentionally, they still exist. They surprise you later.

🔍 Real Examples of Latency-as-Design

Let’s make this concrete. Here are some common latency patterns you’ve probably seen, even if no one called them “design decisions” at the time:

1. Eventual Consistency

When you choose eventual consistency (e.g. in a read replica or downstream data sync), you’re trading accuracy for performance.
That’s not just a tradeoff, it’s a latency budget decision.

Example:

That’s not failure. That’s designed to delay, and it needs to be visible, traceable, and safe.

2. Async Workflows (Queues, Webhooks, CRON Jobs)

Putting a task on a queue is a latency choice.
You're explicitly saying: