👋 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:
You are designing how fast or slow, the system is allowed to move.
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:
A user uploads a file. You accept the request instantly, but the virus scan takes 10 seconds and happens in the background.
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:
"We don't need this to finish now."
"We'll deal with it in a minute... or ten."
That’s okay, but only if the user experience, monitoring, and retry logic are aligned with that delay.
3. Cross-Team Latency
Human latency matters too.
If a critical service change requires manual approval from 3 teams, your architecture is slow, even if your code is fast.
The same goes for release coordination across teams, data pipeline dependencies, or tribal knowledge barriers. These are architectural drag points, and good system design tries to surface and reduce them, not just document them after the fact.
4. Timeouts, Retries, and Circuit Breakers
Every time you add a retry loop, you’re introducing a delay under pressure.
Every time you set a timeout value, you’re expressing a latency tolerance, whether you realize it or not.
Smart architects don’t guess those values. They think about:
What failure looks like at the edge of that delay
What user impact occurs if it’s exceeded
What monitoring is in place to catch it
🛠 Tool of the Week: Latency Budget Worksheet
Here’s a lightweight framework I use to make latency visible and intentional. You can apply it to any user-facing flow or critical backend chain.
🧾 Latency Budget Template
Pick a single request flow or business-critical operation and ask:
🎯 Goal:
What is this request trying to achieve?
🔗 Chain of Steps:
List each service/component/data call involved
⏱️ Acceptable Delay per Step:
What is the max latency tolerated at each step?
📉 Worst-Case Total Time:
What’s the slowest it can get before users notice?
🔁 Retry / Timeout Logic:
Where are delays hidden in retries or fallback logic?
📊 Observability:
Can we see where the delay happens?
📣 Impact if Exceeded:
What actually breaks when we go over budget?
This isn’t a performance tuning tool.
It’s a visibility tool.
You’re not trying to optimize to zero, you’re trying to understand and control where time is spent.
✅ Mini Challenge
This week, take one flow in your system, ideally something that:
touches a user
spans more than one service
includes a background job, webhook, or retry pattern
Run it through the Latency Budget Worksheet.
You’ll probably find:
One place with no timeout defined
One retry loop that silently delays recovery
One dashboard that tracks nothing useful
Then ask:
Is this delay intentional?
Is this failure mode visible?
Can we give the user better feedback, or shorten the chain?
You don’t need to fix all of it.
Just learn to see time as part of your system’s behavior.
🎯 Want to learn how to design systems that make sense, not just work?
If you liked this issue, you’ll get even more out of the free 5-day crash course:
From Developer to Architect
It walks you through the exact mindset shifts that help devs grow into architectural roles, including how to:
Define clear system boundaries
Communicate design intent
Make confident tradeoffs
Avoid overengineering
Think in systems, not just services
Each lesson is short, practical, and designed to shift how you think, not just what you code.
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Delivered straight to your inbox. No fluff, just clear thinking.
👋 Wrapping Up
Speed isn’t always the answer.
But knowing where time lives in your system, and what it costs, is what separates good code from good design.
Architects don’t just build flows.
They shape experience through time.
Thanks for reading.
See you next week,
Bogdan Colța
Tech Architect Insights