
Logiciel helps engineering teams test how applications, APIs, and infrastructure behave under real-world demand. Find bottlenecks early, validate scalability, and release with greater confidence before traffic exposes the problems.
We combine workload design, performance engineering, system diagnostics, and test automation so teams can understand not only whether performance degrades, but why.
aligned with architecture, production usage patterns, release risk, and expected growth
built around concurrency, transaction volume, request patterns, user journeys, and peak demand
for response time, throughput, error rates, resource utilization, and system behavior
across APIs, application services, databases, infrastructure, and external dependencies
that can be rerun across releases, environments, and scaling changes
that connect performance symptoms to likely technical causes and remediation priorities
your engineering team can continue using as traffic, architecture, and product complexity grow
Validate response times, concurrency, throughput, page-level transactions, backend behavior, and high-value web journeys under realistic demand.
Measure latency, throughput, failure behavior, rate limits, dependencies, and scalability across individual APIs and service chains.
Test backend responsiveness, network-dependent behavior, critical mobile workflows, and service performance across realistic usage conditions.
Simulate expected and peak user demand to understand whether systems maintain acceptable performance under anticipated traffic.
Push systems beyond normal capacity to identify saturation points, failure behavior, recovery characteristics, and architectural limits.
Run sustained workloads to uncover memory leaks, connection issues, resource exhaustion, degradation, and instability that appear over time.
Evaluate how application and infrastructure performance changes as users, transactions, services, and computing resources increase.
We review your architecture, current testing, production patterns, critical journeys, traffic expectations, performance risks, environments, and existing tooling.
We simulate normal, peak, and beyond-capacity workloads to measure system behavior, identify thresholds, and understand failure conditions.
We evaluate API latency, throughput, concurrency, dependency behavior, error rates, and service-level performance under controlled workloads.
We test critical web and mobile journeys against realistic traffic patterns to identify backend and user-experience performance risks.
We build reusable test suites, workload scripts, environment configurations, reporting, and execution patterns for repeatable performance validation.
We correlate test results with application, database, infrastructure, and service behavior to narrow down likely causes of degradation.
We integrate appropriate performance checks into delivery workflows so regressions can be detected earlier without turning every build into a full-scale load test.
A structured approach to translating production behavior into test scenarios using concurrency, transaction mix, request frequency, traffic growth, and peak demand.
A method for connecting latency, throughput, errors, and resource pressure to application services, databases, infrastructure, and external dependencies.
A practical framework for defining baselines, thresholds, test cadence, ownership, reporting, and release criteria as systems scale.
We review system architecture, traffic patterns, critical transactions, existing telemetry, environments, known constraints, and current performance expectations.
We model normal usage, peak traffic, concurrency, transaction mix, sustained demand, and failure conditions around realistic system behavior.
We configure performance testing software, test scripts, data, monitoring, execution infrastructure, and repeatable workload profiles.
We run load, stress, endurance, or scalability tests and correlate results with application, database, infrastructure, and dependency behavior.
We prioritize findings, rerun tests after changes, establish performance baselines, and help integrate repeatable checks into the engineering lifecycle.



Teams that needed to ship fast, and did. Here's what partnering with Logiciel felt like from the inside.
Performance testing services evaluate how software behaves under different levels and types of demand. They help teams measure response time, throughput, stability, resource usage, scalability, and failure behavior before performance issues affect production users.
Performance testing is the broader discipline of evaluating speed, stability, scalability, and system behavior. Load testing is one type of performance testing focused on how a system behaves under expected or peak levels of demand.
API performance testing can measure response time, throughput, concurrency, error rates, service dependencies, rate-limit behavior, and how APIs respond as request volume increases.
Yes. Web performance testing and mobile app performance testing can evaluate critical user journeys, backend services, APIs, network-dependent behavior, and system response under realistic usage conditions.
The right performance testing software depends on your architecture, protocols, workload requirements, infrastructure, existing engineering stack, and reporting needs. We can work with suitable existing tools or recommend a fit based on the testing objective.
Performance testing is most useful before major releases, infrastructure changes, cloud migrations, high-traffic events, architectural changes, and expected growth. Mature teams also use repeatable performance checks throughout the development lifecycle.
Performance tests reveal where and when degradation occurs. Combined with application, database, infrastructure, and observability data, they can help narrow the problem to specific services, queries, resource constraints, dependencies, or architectural bottlenecks.
Validate capacity, uncover bottlenecks, and understand how your system behaves before the next release, traffic spike, or scaling event puts it under pressure.