5 common assumptions in load testing—and why you should rethink them

Over the years, I’ve had countless conversations with performance engineers, DevOps teams, and CTOs, and I keep hearing the same assumptions about load testing. Some of them sound logical on the surface, but in reality, they often lead teams down the wrong path. Here are five of the biggest misconceptions I’ve come across—and what you should consider instead.

1⃣ “We should be testing on production”

A few weeks ago, I had a call with one of the biggest banks in the world. They were eager to run load tests directly on their production environment, using real-time data. Their reasoning? It would give them the most accurate picture of how their systems perform under real conditions.

I get it—testing in production sounds like the ultimate way to ensure reliability. But when I dug deeper, I asked them: “What happens if today’s test results look great, but tomorrow a sudden traffic spike causes a crash?” Who takes responsibility if a poorly configured test impacts real customers? Are you prepared for the operational risks, compliance concerns, and potential downtime?

Yes, production testing has its place, but it’s not a magic bullet. It’s complex, and without the right safeguards, it can do more harm than good. A smarter approach is to create a staging environment that mirrors production as closely as possible, ensuring meaningful insights without unnecessary risk.

2⃣ “Load testing is all about the tool—more features mean better results.”

This is one of the biggest misconceptions I hear. Teams assume that if they pick the most feature-packed tool, they’ll automatically find every performance issue. But load testing isn’t just about the tool—it’s about understanding how your users behave and designing tests that reflect real-world scenarios.

I’ve seen companies invest in powerful load testing tools but fail to integrate them properly into their CI/CD pipeline. Others focus on running massive test loads without first identifying their application’s weak spots. Here’s what matters more than just features:

• Do you understand your users’ behavior patterns?
• Have you identified performance gaps before running the test?
• Are you making load testing a continuous part of your development process?

The most successful teams don’t just run tests; they build performance testing into their workflows and use insights to optimize their applications. Having the right tool is important, but how you design your tests and interpret results matters even more.

3⃣ “Time-to-market isn’t that important—testing takes time, so what?”

This is one that often gets overlooked—until it’s too late. Some teams treat load testing as a final checkbox before release, assuming that if it takes longer, it’s no big deal. But here’s the reality:

• Every extra day spent on load testing delays product launches, giving competitors an edge.
• Development teams get stuck waiting for results instead of shipping new features.
• Customers expect fast, seamless experiences, and slow performance fixes can hurt satisfaction.

I’ve seen companies take weeks to run full-scale load tests, only to realize that they’re missing critical deadlines. In today’s market, speed matters.

The solution isn’t skipping load testing—it’s making it efficient. Instead of treating it as a bottleneck, integrate performance tests into your pipeline. Use automated performance testing in CI/CD, run incremental load tests instead of one massive test, and prioritize testing early in development.

Load testing shouldn’t slow you down—it should help you move faster with confidence.

4⃣ “More users? Just make the machine bigger.”

A lot of companies try to fix performance issues by upgrading their infrastructure—more CPU, more memory, bigger machines. But here’s the problem: scaling up doesn’t fix inefficient code.

I had a discussion with a tech lead recently who was struggling with performance issues. His first instinct? “Let’s increase the server capacity.” But when we dug into the data, we found that:

• A single database query was responsible for 80% of the slowdown.
• Users weren’t just “hitting the system” — they were interacting in unpredictable ways.
• The app was running inefficient loops that caused unnecessary processing.

Throwing hardware at the problem would have masked the issue temporarily, but it wouldn’t have solved it. Instead of focusing on infrastructure upgrades, ask yourself: Where are the real bottlenecks? Is it slow database queries, unoptimized APIs, or poor caching strategies? Is horizontal scaling a better option? Distributing the load across multiple instances is often more effective than just adding bigger machines.

How are users actually interacting with the system? Unexpected behaviors can cause slowdowns that won’t be solved by adding more resources.

Scaling up buys you time, but it won’t fix inefficiencies in your codebase.

5⃣ “Open source vs. commercial tools—free is better, right?”

This is a debate I hear all the time. Many teams, especially in startups, want to stick with open-source tools. They say, “We’d rather invest in DevOps and use free testing tools instead of paying for a commercial solution.” And I totally get that—open source is great for learning and experimentation.

But I’ve also seen companies hit a wall when they try to scale. They start with an open-source solution, and everything works fine—until they need to:

• Run complex test scenarios that require correlation and parameterization.
• Manage large-scale distributed tests across cloud environments.
• Get dedicated support when they run into critical issues.

That doesn’t mean open-source tools aren’t valuable—they absolutely are. They work well for teams with strong in-house expertise and for projects where flexibility is key. However, teams that need to move fast, handle enterprise-scale testing, or reduce maintenance overhead might benefit from evaluating different types of solutions that fit their needs.

Ultimately, it’s not about free vs. paid—it’s about choosing the right tool for your testing strategy.

Final Thoughts

Load testing is full of myths, and it’s easy to fall into these common traps. But if there’s one takeaway, it’s this:

Don’t test just for the sake of testing—test with purpose.

Understand your users before you run the test.

Make load testing part of your process, not a roadblock.

Have you encountered an assumption in load testing that turned out to be completely wrong? Let’s discuss!

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