Sometimes yes. Sometimes absolutely not. ‘Performance testing is best practice’ is not a reason to spend money.
If twelve people will use an internal tool and the biggest performance risk is somebody making tea while a report loads, you probably do not need a specialist performance programme.
If thousands of customers can arrive at once, response time affects conversion, background jobs compete for resources, or failure under load can corrupt transactions, you probably do.
If you are unsure whether performance testing is warranted, that is usually answerable from the usage pattern, architecture and consequences of failure before anybody spins up an expensive load test.
Start with a credible workload
Before buying tools or a consultancy, work out what you are actually trying to prove. Expected concurrent users. Peak transactions. Critical response times. Batch loads. Growth. External dependencies. Failure behaviour. Those findings should feed a credible release-readiness decision, rather than become a separate ceremonial workstream.
Do not invent a giant round number because it sounds impressive in a meeting. ‘Test one million users’ is not a performance requirement if nobody can explain where that million came from or what those users are doing.
Performance is not just speed
You may need to know whether the service remains stable over time, what happens when a dependency slows down, whether queues recover, whether scaling works and whether the system fails cleanly rather than slowly setting fire to itself.
And if you do run a test, make sure the environment and data are credible enough for the result to mean something. A beautiful graph from an environment with half the production architecture removed is still a beautiful graph from the wrong system.
If you are not sure whether performance testing is worth doing, talk to us before commissioning it. We can review the expected usage and architecture, tell you what risks justify testing and help define something more useful than ‘a lot of users’.
Decide what result would change your decision
Before testing, agree what failure actually means. Is the service unacceptable above a response time? Must it sustain a transaction rate? Must it recover after a spike? Are there hard capacity limits? Without decision criteria you can spend days generating graphs and then have a meeting about whether the graphs feel concerning.
Performance work also needs technical observation. CPU, memory, database behaviour, queues, external calls and errors often explain more than a single average response-time line. The point is to understand where the system bends and breaks, not to decorate a report with a dramatic ramp-up chart.
If the risk is modest, you may only need a focused check. If the launch depends on high volume or strict response times, treat performance as engineering work, not a ceremonial test at the end.