Load Testing Service

What Is Load Testing?

Load testing is a performance test carried out to measure how a web application, mobile application, API service, e-commerce platform, corporate system or digital service behaves under a defined level of user and transaction volume.

Load testing measures how a system behaves under normal and heavy usage:

how many concurrent users it can support, how transaction times change, at what point performance degrades, which components create bottlenecks, and whether the existing infrastructure can meet the expected workload

The load testing service delivered by SecureSys is not simply a matter of sending a large number of requests to a system. Test scenarios close to real user behaviour are built so that the application, API, database, network and infrastructure components can be assessed together under load.

Load testing is critical for business continuity and user experience, particularly in systems expecting high traffic.

Why Should Load Testing Be Carried Out?

An application running smoothly with a small number of users does not mean it will perform the same way under heavy traffic.

Systems generally begin to reveal their real problems as the number of users rises.

An application running smoothly with 100 users may, at 5,000 concurrent users, see response times lengthen, database connections exhausted, CPU usage rise sharply, memory run short, API services time out, or the application become entirely unreachable.

Problems of this kind can have serious consequences during campaigns, launches, ticket sales, payment processing, examination systems, public services and e-commerce peaks.

The core purpose of load testing is therefore to answer this question:

"Can this system handle the real user load at a safe and acceptable level of performance?"

SecureSys Load Testing Service

SecureSys provides a professional load and performance testing service for measuring the performance of organisations' web, mobile, API and corporate applications under realistic user loads.

Depending on the organisation's needs, the test can measure:

concurrent user numbers, transaction volume, API request rate, response time, throughput, error rate, system resource usage and bottleneck points

The aim is not simply to see whether the system "falls over".

The real aim is to establish at what load level the system begins to lose performance and which technical component that loss originates from.

Are Load Testing and Performance Testing the Same Thing?

Load testing is one of the subcategories of performance testing.

Performance testing generally assesses criteria such as a system's speed, stability, scalability and response time.

Load testing focuses specifically on measuring system behaviour under a defined user or transaction load.

For example:

• 1,000 concurrent users logging in to the system
• 20,000 API requests sent per minute
• 500 payment transactions carried out simultaneously
• Measuring the performance of the search function under heavy traffic

can all be load testing scenarios.

What Is a Concurrent User?

One of the concepts most frequently used in load testing is the concurrent user.

A concurrent user is a user actively using the application within the same time window.

That number is not sufficient on its own, however.

10,000 users being connected to the system is not the same as 10,000 users transacting at the same moment.

User behaviour therefore has to be modelled in realistic load tests.

A user may follow a scenario such as:

log in → search for a product → open the product detail → add to basket → go to the payment screen → complete the transaction

The test has to be designed to imitate that real usage behaviour.

Why Do Realistic User Scenarios Matter?

The success of a load test depends largely on how realistic the test scenarios are.

Sending continuous requests to the same URL technically creates load but may not represent real user behaviour.

Real users visit different pages, carry out different operations and pause between them.

Load testing scenarios therefore have to take real business processes into account:

login, search, listing, data querying, form submission, file upload, payment, report generation and API calls

In the SecureSys approach, scenarios can be built around the organisation's real business flows.

Web Application Load Testing

Web application load testing measures the performance of web applications running over the internet or an intranet under user load.

E-commerce sites, customer portals, public applications, banking systems, SaaS platforms and corporate web applications can all be assessed.

The test can measure page load times, backend response times, error rates, concurrent user capacity and system resource usage.

The performance of critical functions in particular has to be assessed individually.

A home page loading quickly does not mean the whole system performs well.

Different bottlenecks can exist in payment, query or reporting screens.

API Load Testing

A significant proportion of modern applications is built on API services.

API load testing is therefore one of the most critical areas of performance testing.

By sending requests to particular endpoints at different intensities, API load testing can measure:

response time, requests per second, throughput, error rate and timeout rates

If a payment API is expected to handle 500 transactions per second, for example, the test can be carried out below and above that level in order to measure the system's capacity.

Looking at average response time alone is not sufficient in API testing.

Examining percentile values such as P95 and P99 gives a more accurate picture of how many users experience delay under heavy load.

Mobile Application Load Testing

A mobile application's performance does not depend only on the application on the device.

Backend API services, authentication infrastructure, databases and third-party services all affect performance directly.

Mobile application load testing therefore generally tests the application's backend services and API endpoints.

These tests are important particularly in systems where large numbers of mobile users log in or transact at the same time.

Database Performance and Load Testing

In many applications, the root cause of performance problems is the database layer.

As the number of users rises, query times can lengthen, connection pool limits can be reached, or locking problems can emerge.

During the load test, the following indicators can be examined on the database side:

query duration, connection count, CPU usage, disk I/O, lock and wait states

Database performance plays a critical role particularly in high-volume reporting or transaction systems.

What Is Response Time?

Response time is the time the system takes to answer a request made by a user.

If it takes 800 milliseconds for search results to appear when a user searches for a product, the approximate response time for that operation is 800 ms.

Looking only at the average response time can be misleading, however.

Even where the average response time is 1 second, a small proportion of users may experience 10-second delays.

Using different percentile values in performance analysis is therefore important.

What Are P95 and P99?

P95 means 95 per cent of transactions completed below the stated time.

If P95 response time is 2 seconds, 95 per cent of user transactions completed within 2 seconds.

P99 shows the same assessment at the 99 per cent level.

These values provide more realistic performance visibility, particularly in high-traffic systems.

What Is Throughput?

Throughput is the volume of transactions or requests a system can process within a given period.

An API successfully processing 2,000 requests per second, for example, is one indicator of its throughput capacity.

Throughput is an important metric for system scalability.

As load increases, throughput rises up to a certain point.

As system capacity is approached, however, the increase in throughput can stop and response time can begin to rise sharply.

That point can indicate that the system is approaching a bottleneck.

Why Does the Error Rate Matter?

Even if the system responds quickly during a performance test, the test should not be treated as successful if it is producing a large number of errors.

Error rate is therefore one of the core measurements in load testing.

HTTP 500, 502, 503, timeouts and application-specific error messages all have to be analysed.

If a system carries out 5,000 transactions per second but 10 per cent of them fail, the nominal capacity may not in fact be acceptable.

What Is Bottleneck Analysis?

Bottleneck analysis aims to identify the component limiting the application's performance.

A bottleneck can originate with the CPU, RAM, disk, database, network, application server, thread pool, connection pool or a third-party service.

If the application server's CPU usage sits at 30 per cent while database CPU usage reaches 100 per cent, for example, that indicates the problem lies on the database side rather than the application layer.

The real value of load testing is not simply detecting the slowdown but establishing why it slowed down.

What Is Capacity Testing?

Capacity testing focuses on determining the maximum load a system can support while maintaining an acceptable level of performance.

An organisation may want the answer to a question such as:

"How many concurrent users can our existing infrastructure serve?"

By increasing the load in a controlled way during the test, the system's performance limits can be established.

This information matters in particular for infrastructure investment and capacity planning.

Load Testing and Capacity Planning

One of the problems organisations frequently encounter in infrastructure investment is planning too much or too little capacity.

Too little capacity causes service disruption.

Too much capacity leads to unnecessary hardware and cloud cost.

Load tests support capacity planning by providing real performance data.

It can be established, for example, that the existing infrastructure supports 5,000 users without difficulty but loses performance beyond 7,000.

The organisation can then plan the necessary infrastructure investment more accurately against its expected growth.

Load Testing in Cloud Environments

One of the important advantages of cloud infrastructure is that resources can scale with demand.

Whether the automatic scaling configuration genuinely works has to be verified, however.

During the load test, the following can be assessed:

auto scaling, load balancers, container infrastructure, Kubernetes pod counts and cloud database performance

Whether the system automatically creates new application instances when user load doubles, for example, can be observed.

These tests are important particularly for high-traffic cloud applications.

Load Testing in Microservice Architectures

In microservice architectures, a single user transaction can depend on several services in the background.

A payment transaction, for example, may pass through the identity service, the customer service, the payment service, the stock service and the notification service.

Just one of those services slowing down can affect the performance of the whole transaction chain.

In microservice-based systems, therefore, the dependencies between services have to be examined as well as frontend performance.

How Does the Load Testing Process Work?

1. Defining the business objectives

The purpose of the test is established first.

How many users are expected?

Which transactions are critical?

What is the acceptable response time?

The answers to these questions directly affect the test design.

2. Building the scenarios

Transaction scenarios representing real user behaviour are prepared.

3. Preparing the test data

User accounts, product data and any other data needed during the test are created.

4. Baseline test

The system is run at a low load level so that its starting performance can be measured.

5. Increasing the load

User or transaction volume is increased in a controlled way.

6. Monitoring the performance metrics

Response time, throughput, error rate and resource usage are tracked.

7. Bottleneck analysis

The system components causing performance loss are identified.

8. Reporting

The results are reported with technical and management-level assessment.

9. Improvement and retest

The test is repeated after optimisation so that the performance gain can be verified.

What Is a Baseline Test?

Before starting a load test, the system's normal performance under low traffic has to be known.

This starting measurement is called the baseline.

If the application's response time is 300 ms under normal conditions but rises to 2 seconds under heavy load, for example, the effect of the load on performance is clearly visible.

Baseline values allow comparison with later tests.

What Is Ramp-Up?

Sending all users into the system at once is not always realistic in load testing.

Ramp-up is the controlled increase of test load over a defined period.

The number of users can be raised, for example, as:

100 → 500 → 1,000 → 2,500 → 5,000

This approach shows more clearly at what point the system begins to lose performance.

What Is a Soak Test?

A soak test, or endurance test, runs the system under a defined load for an extended period.

Some performance problems do not appear in tests lasting a few minutes.

Problems such as memory leaks, connection leaks or resource exhaustion can emerge when the system has been running for hours.

Long-duration testing is therefore important for critical, continuously running systems.

What Is a Spike Test?

Spike testing measures how the system behaves when user or transaction volume rises sharply within a short period.

Thousands of users logging in within seconds when a campaign starts, for example, creates a spike scenario.

The system has to be designed to handle that sudden load.

The Difference Between Load Testing and Stress Testing

These two concepts are close but not the same.

Load testing measures the system's performance under expected or planned traffic.

Stress testing deliberately pushes the system beyond its capacity in order to establish its breaking point.

If the system's normal target is 5,000 concurrent users, for example, the load test can be carried out at around 5,000.

In a stress test, the user count is raised to 10,000, 15,000 or higher in order to examine at what point the system fails.

Load testing and stress testing are therefore complementary performance tests.

Are Load Testing and DDoS Testing the Same Thing?

No.

Load testing measures application performance by simulating legitimate user behaviour.

DDoS testing assesses security and resilience against high-traffic or resource-exhaustion scenarios of an attack character.

The objective of load testing is performance.

The objective of DDoS testing is service continuity under attack and the effectiveness of the security mechanisms.

The two exercises therefore require different scope and security controls.

Which Systems Can Be Load Tested?

Load testing can be applied across many different digital platforms.

Systems suitable for load testing include:

web applications, e-commerce systems, mobile application backends, REST APIs, SOAP services, microservices, payment systems, customer portals, ERP integrations, public sector applications and SaaS platforms

Load Testing in E-Commerce Systems

E-commerce systems can face very high user traffic within a short period during campaigns.

Black Friday, special discount days or product launches can create several times the normal load on a system.

Critical operations such as product listing, search, adding to basket, applying campaigns, payment and order creation therefore have to be tested individually.

Performance problems at the payment stage in particular lead directly to lost revenue.

Load Testing in Public Sector and Critical Systems

Public services, examination systems, application platforms and citizen service portals can face very heavy user traffic at particular times.

Access problems in these systems affect large numbers of users.

Carrying out capacity and load testing before dates on which high traffic is expected is therefore important.

What Does a Load Testing Report Contain?

A professional load testing report should show more than the number of successful and failed transactions.

The report can include:

the test scenarios, user loads, response time values, P95/P99 values, throughput, error rates, system resource usage, the bottlenecks observed and a capacity assessment

Technical improvement recommendations can also be presented.

Software, systems and database teams can then see more easily at which layers optimisation is needed.

Optimisation After Load Testing

Identifying a performance problem does not mean the project has failed.

On the contrary, the core purpose of load testing is to identify those problems before real users experience them.

Depending on the bottlenecks identified, actions such as the following can be applied:

optimising application code, improving SQL queries, introducing caching, changing load balancing configuration, increasing server resources or adjusting cloud auto scaling policies

The load test is then repeated so that the effect of the changes can be measured.

When Should Load Testing Be Carried Out?

Load tests should not be carried out only immediately before an application goes live.

Testing is valuable in particular:

before a new application is deployed, before major campaigns, at significant version changes, after infrastructure changes, during cloud migrations, and when a significant increase in user numbers is expected

Repeating the tests periodically is also advisable for performance-critical systems.

What Questions Does Load Testing Answer?

After a professional load test, the organisation should be able to answer the following more clearly:

How many users can our system handle?

How long do pages take to load under heavy traffic?

How many transactions per second can our APIs handle?

Beyond which user level does the error rate rise?

Is the bottleneck in the application, the database or the infrastructure?

Are the existing server resources sufficient?

Can the existing infrastructure handle the expected growth?

Is the auto scaling mechanism working correctly?

Answering these questions with measurable data offers a significant advantage in capacity planning and infrastructure investment.

How Are Success Criteria Defined in Load Testing?

To decide whether a load test has succeeded or failed, measurable acceptance criteria have to be defined before the test begins.

Statements such as "the system is fast" or "the application handles heavy user load" are not technically sufficient.

Measurable performance targets should be established instead.

An organisation can define criteria such as:

• P95 response time for critical transactions below 2 seconds,
• API services handling a defined number of requests per second,
• Error rate remaining below a defined level at 5,000 concurrent users,
• No resource exhaustion in the application services during the test,
• Critical transactions completing successfully under heavy traffic

These criteria have to be assessed together with the application's business requirements.

The performance expectations of an application in which users only view information will not be the same as those of a platform carrying out real-time financial transactions.

SecureSys load testing work aims to establish measurable performance targets before the test wherever possible.

The test then produces not only charts and technical values but also a statement of whether the system meets the defined business objectives.

Why Is Average Response Time Not Enough on Its Own?

One of the mistakes made frequently in performance testing is assessing only the average response time.

The average gives an idea of the system's general performance but may not reflect the experience of all users.

Most of 1,000 transactions may complete in 500 milliseconds while a particular subset takes 8–10 seconds.

The average may look acceptable while some users experience serious performance problems.

Professional load testing therefore assesses different performance metrics together:

average, median, P90, P95 and P99

P95 and P99 in particular matter for understanding the system's extreme performance behaviour under heavy load.

This shows how the application performs not only for the average user but for the great majority of users.

What Is Requests Per Second (RPS)?

Requests per second (RPS) is the number of requests a system receives or can process per second.

It is one of the important performance indicators, particularly for API services and high-traffic web platforms.

An API running without difficulty at 1,000 requests per second but starting to produce errors at 2,500 provides significant information about system capacity.

RPS should not be assessed on its own, however.

As load increases, how the following change together has to be examined:

RPS, response time, throughput and error rate

Ideally, the system should handle increasing load linearly or predictably up to a certain point.

As capacity is approached, response times can rise sharply or the error rate can increase.

Identifying that point is extremely valuable for capacity planning.

What Is Think Time and Why Does It Matter?

Real users do not carry out operations one after another within milliseconds.

After logging in, a user examines the screen, searches for a product, looks at a record or fills in a form.

There are pauses between these operations.

In performance testing, that pause is called think time.

A load test carried out without think time can turn into an automated traffic generation exercise rather than a representation of real user behaviour.

If a real user waits an average of 10 seconds between two operations, a test tool sending dozens of transactions per second does not represent the real traffic model.

SecureSys load testing scenarios therefore aim to model real user behaviour and transaction intervals wherever possible.

What Is a Transaction?

Assessing HTTP requests individually is not always sufficient in load testing.

A particular business process the user carries out may need to be measured as a whole.

These operations can be modelled as transactions.

The following, for example, can be defined as separate transactions:

user login, product search, add to basket, payment, order creation

The test then measures not only technical endpoint performance but the performance of the business processes that matter to the user.

Overall response time may be at an acceptable level while the "payment" transaction duration has risen significantly, for example.

This information helps technical teams reach the source of the problem faster.

Why Is Monitoring Critical During a Load Test?

Examining only the results from the test tool is often not sufficient during a load test.

The test tool shows us what the user experiences.

Understanding why the system slowed down requires the backend infrastructure to be monitored as well.

As far as possible, the following metrics should therefore be tracked during the test:

CPU usage, RAM consumption, disk I/O, network usage, application thread count, connection pool usage, database connection count, query durations and container resource consumption

Response time may be seen to rise suddenly when the user count reaches 3,000, for example.

If the database connection pool is seen to reach maximum capacity at the same moment, that is a strong indicator of the source of the problem.

The real value of performance testing therefore emerges through:

load generation + application measurement + infrastructure monitoring + correlation

Application Performance Monitoring (APM) and Load Testing

Where an organisation has APM in place, assessing the load test results together with that data offers a significant advantage.

APM systems can show which functions within the application consume time during a transaction.

Of an API request taking 4 seconds in total, for example, 200 milliseconds may be spent in the application layer, 3.5 seconds in a database query and the remainder in an external service.

This makes it easier to reach the direct source of the performance problem.

Correlating load test and APM data is particularly valuable in complex microservice architectures.

The Effect of Caching on Performance

Modern applications use various caching mechanisms in order to improve performance.

Redis, Memcached, CDNs and application-level caching can significantly reduce the load on databases and backend services.

Misconfigured caching can nonetheless cause performance problems or unexpected results.

Cache hit/miss rates and the effect of caching on system performance can be assessed during a load test.

The first test may find that the system sends thousands of identical queries per second to the database, for example.

After an appropriate caching mechanism is applied, the same test can be repeated so that the performance improvement is measured.

This makes it measurable whether the optimisations carried out genuinely worked.

Load Balancer Performance Testing

In high-traffic applications, load balancers are used to distribute user traffic across several servers.

Incorrect load balancing configuration can nonetheless leave some servers overloaded.

Three of four application servers running at 30 per cent CPU while one runs at 95 per cent, for example, indicates a problem in traffic distribution.

Whether traffic distribution is balanced, and how the load balancer behaves, can be assessed during a load test.

Whether traffic is successfully transferred to the other servers when one application server goes offline can also be assessed through controlled test scenarios.

Auto Scaling Testing

In cloud and container-based infrastructure, system capacity can be increased automatically with traffic volume.

This mechanism is called auto scaling.

An auto scaling policy having been defined does not mean it works correctly, however.

By increasing user traffic in a controlled way during the load test, the system's scaling behaviour can be observed.

Whether an application expected to scale as:

2 instances → 4 instances → 8 instances

genuinely creates new resources at the right moment can be assessed.

Whether unnecessary resources are shut down when traffic falls can also be examined.

This matters in particular for cloud cost optimisation.

Load Testing in Kubernetes Environments

Container and Kubernetes-based architectures are increasingly common in modern applications.

In these environments, performance does not depend on application code alone.

Pod resource limits, Horizontal Pod Autoscaler configuration, the ingress controller, the service mesh, node capacity and container resource limits can all affect performance.

During the load test, the following can be monitored:

pod CPU/RAM usage, replica counts, scaling behaviour, pod restarts and node resource consumption

Throttling caused by application pods reaching their CPU limit, for example, can lead to a significant rise in response time on the user side.

For organisations using Kubernetes, it is therefore important that performance testing covers the container infrastructure as well.

How Do Third-Party Services Affect Performance?

Modern applications rarely run entirely within their own infrastructure.

Payment services, SMS providers, email services, authentication platforms and external APIs can all be part of the application's business processes.

One of those services slowing down can affect the performance of the entire application.

A payment provider's response time rising from 500 milliseconds to 5 seconds, for example, leaves users waiting a long time at the payment screen.

Knowing the third-party dependencies during a load test, and interpreting the performance results with those dependencies in mind, is therefore important.

SLA and SLO Checks in Performance Testing

Organisations can have defined service level targets for their critical applications.

SLA – service level agreement and SLO – service level objective values can be used to define the performance and availability targets the system has to reach.

Targets such as the following can be set for a service:

P95 < 2 seconds, successful transaction rate > 99.5%, critical API response time < 1 second

The load test results can be compared against these targets in order to measure whether the system meets its performance requirements.

This matters in particular in technical acceptance processes between service providers and customer organisations.

Load Testing and Business Continuity

Performance is not solely a user experience matter.

In critical systems, a performance problem can turn directly into a business continuity risk.

An e-commerce system being unavailable during a campaign leads to lost sales; a payment system slowing down leads to lost transactions; a public service platform being unreachable disrupts citizen services; and corporate applications losing performance reduces employee productivity.

Load tests can therefore be treated as one of the technical components of an organisation's business continuity and operational resilience work.

Performance Problems Can Become Lost Revenue

For businesses generating revenue through digital channels in particular, performance directly affects commercial results.

A user waiting a long time at the payment or order stage may abandon the transaction.

Performance testing should therefore not be seen as technical work for IT teams alone.

Load testing can be directly related to:

customer experience, service continuity, sales capacity and corporate reputation

Reporting the test results in a way management can understand is therefore important.

How Should Load Test Results Be Interpreted?

The thousands of measurements produced at the end of a performance test may mean little on their own.

The real value emerges when the data is interpreted together.

A test may produce the following picture, for example:

1,000 users: the system is stable. 2,500 users: performance is acceptable. 5,000 users: P95 response time begins to rise. 7,500 users: database CPU usage reaches a critical level. 10,000 users: the error rate rises markedly.

This result is extremely valuable for the organisation.

The answer to "how many users can the system handle?" now rests on measurable data rather than estimation.

And if the problem at 7,500 users has been established as originating with the database rather than the application server, the investment can also be directed to the right place.

This is precisely the core purpose of professional load testing: taking system performance out of the realm of estimation and making it measurable.

What Information Should Be Provided Before a Load Test?

To plan a sound load test, it helps to establish some information about the application's usage profile beforehand.

This can include current daily and peak user numbers, expected maximum traffic, critical user scenarios, the application architecture, the test environment, target response time values and any existing SLA/SLO requirements.

Where the organisation holds historical traffic data, the test scenarios can be built using it.

An e-commerce platform serving 2,000 concurrent users normally but expecting 10,000 during a campaign, for example, can have its test model built around that growth scenario.

The load test SecureSys carries out then becomes a capacity validation exercise closer to the organisation's real usage conditions, rather than a theoretical traffic generation exercise.

Why Does a Retest Matter After a Load Test?

Identifying the bottlenecks in a load test is only the first stage.

Once the application, database or infrastructure teams have carried out the necessary optimisation, the same scenarios have to be run again under conditions as similar as possible.

This can be treated as a performance retest.

The first test may have found a performance problem at 5,000 concurrent users, for example.

Database queries may then have been optimised, a caching mechanism introduced and the application servers reconfigured.

If the retest shows the system reaching acceptable performance at 8,000 users, the effect of the improvement has been verified in measurable terms.

The performance improvement process can therefore be run through the cycle:

Measure → Identify the Bottleneck → Optimise → Retest → Compare

This also allows management to see the effect of infrastructure and software investment on performance through concrete data.

The SecureSys load testing service is a professional performance testing service for measuring the performance, capacity and scalability of web applications, API services and critical digital platforms under high user and transaction volume.

Why the SecureSys Load Testing Service?

Load testing is not simply generating a high volume of requests.

Building the right scenarios, modelling real user behaviour, analysing the performance metrics correctly and assessing the application and infrastructure components together are all necessary.

The SecureSys load testing service measures the performance of web applications, API services and critical digital platforms under realistic user loads.

The tests assess not only the system's breaking point but also:

response times, transaction capacity, error rates, scalability and bottleneck points

The SecureSys approach aims to report the results in a way technical teams can use and management can understand.

SecureSys is a Türkiye-based technology and cyber security company providing load testing, performance testing, stress testing and capacity analysis services for web applications, API services, corporate systems and high-traffic digital platforms.

Frequently Asked Questions

What is load testing?

Load testing is a test carried out to measure the performance of applications or systems under a defined level of user and transaction volume.

How many users is a load test run with?

The user count is determined by the system's real traffic data and expected capacity targets. There is no fixed number.

Can a load test be run in production?

Technically it can, but the possible effects on production systems have to be assessed carefully. Many projects use a controlled test or performance environment instead.

Can API load testing be carried out?

Yes. The performance of REST, SOAP and similar API services can be measured at different transaction volumes.

How long does a load test take?

The duration varies with the number of scenarios, the scope of the system, the user targets and the number of test repetitions.

Are load testing and stress testing the same?

No. Load testing tests expected usage levels, whereas stress testing pushes the system beyond its capacity in order to investigate its breaking point.

Which values are measured in a load test?

Response time, P95, P99, throughput, requests per second, error rate and system resource usage are among the core metrics.

Is Your System Ready for Real User Load?

Performance problems are often invisible while the system is in normal use.

The real problem emerges as the number of users rises.

Rather than waiting for your application to slow down or become entirely unreachable during heavy traffic, you can measure its capacity in advance.

With the SecureSys load testing service you can analyse your applications' performance under realistic user loads, identify the bottlenecks and plan your future capacity needs with measurable data.

To define the load testing scope for your web application, API service or critical digital platform and request a proposal, get in touch with SecureSys.