We all love to build things that “just work.”

But sometimes, the very things that seem perfect on the surface can silently rack up technical debt, or worse, financial cost.

Let’s talk about caching.
Not in theory, but from a real-world, high-stakes experience.

The Problem That Wasn’t a Problem (Until It Was)

Sometime last year, I implemented a payment integration flow:

• Generate a token
• Authorize the request
• Create a payment link

It was seamless. This setup successfully handled hundreds of thousands of deposits. No errors. No downtime. Everything looked rock solid.

Until about 6 months later.

Our payment gateway provider reached out:

"We’re seeing a spike in token generation. It’s burning through our resources."

My first reaction: Wait, what?

The problem?

We followed the docs and generated a new token before every payment. Nothing seemed wrong. But here’s what we missed: each token had a 20-minute lifespan. Yet, we hit the token endpoint on every transaction, ignoring reuse.

At scale, that “works fine” code turned into resource-heavy behavior, burning unnecessary compute on the provider’s side.

The Fix: Caching to the Rescue

We introduced a simple but powerful fix: caching:

• Store the token once it’s generated
• For subsequent requests, we check if it’s still valid
• If valid, we reuse it; if not, we generate a fresh one.

Now here’s where it gets interesting.

Even though the token is valid for 20 minutes, we decided to cache and reuse it for only 15 minutes.

Why leave 5 minutes on the table?

Because using a token right up to its expiration is risky.
Factors like network latency, server clock differences, and the provider's internal buffer could cause the token to expire mid-request.

By shaving off 5 minutes, we:

• Avoid last-second expiry errors
• Ensure predictable behavior
• Maintain a healthy buffer for time-sensitive requests

It’s a minor trade-off that massively improved reliability.

The Result:

• Over 90% reduction in token requests
• Better performance across board
• Happier provider
• Zero disruption to our users

Just a simple cache, and we went from unintentionally wasteful to clean and efficient.

The Bigger Lesson

Caching isn’t just about speed.
It’s about cost, efficiency, resilience, and being a responsible API consumer.

Whether you're dealing with:

• Authentication tokens (Like in my case)
• Frequently accessed data
• Static assets
• Third-party API responses

Caching can be the difference between a system that just works... and one that scales gracefully.

What I Learned

• “It works” is not the finish line
• Success at scale can hide deep inefficiencies
• Always consider the cost behind your architecture decisions

What Would You Do?

Have you ever shipped something that worked great, only to discover it wasn’t as efficient or considerate as it looked?

If you were in this situation, how would you approach the fix?
Would you use a cache? Adjust the token strategy? Something else entirely?