Find your application's breaking point before your users do. Expert load, stress, spike, and soak testing using JMeter, Gatling, and k6 — with detailed bottleneck analysis and infrastructure recommendations.
Every application works fine with 10 users. Performance testing tells you what happens with 10,000 — before you find out the hard way in production during your busiest period.
The cost of skipping performance testing: A slow application costs real money. Research consistently shows that even a 1-second increase in page load time reduces conversions significantly for e-commerce applications. For SaaS products, slow response times directly impact user retention and churn. For mobile apps, poor performance leads to negative app store reviews that are almost impossible to recover from. Performance issues that reach production affect your entire user base simultaneously — unlike functional bugs that affect individual users one at a time.
At 360 Fahrenheit, we design realistic load test scenarios based on your actual traffic patterns and business peaks — not arbitrary numbers. A flash sale event, a product launch, a marketing campaign send — we model the exact conditions your infrastructure needs to handle and test against them before they happen, not after.
We have delivered performance testing engagements for e-commerce platforms, SaaS applications, fintech services, and enterprise systems across Pakistan, Saudi Arabia, UAE, and the UK — helping teams avoid production incidents that would have caused significant revenue loss and reputational damage.
Different performance risks require different testing approaches. We design the right mix for your application and business context.
Simulate expected peak traffic to validate your system handles real-world demand within acceptable response time thresholds.
Push beyond normal limits to find breaking points, failure modes, and how gracefully your system degrades under extreme load.
Sudden massive traffic increases to test auto-scaling, queue handling, and recovery behaviour after an unexpected traffic surge.
Extended duration tests running for hours or days to detect memory leaks, connection pool exhaustion, and gradual performance degradation.
Measure how your system's performance scales as load increases — identifying the point of diminishing returns and optimal infrastructure sizing.
Dedicated load testing of individual API endpoints — measuring throughput, response time percentiles, and concurrency limits per endpoint.
A complete performance testing engagement — from realistic load model design to detailed bottleneck analysis and specific, actionable infrastructure recommendations.
We analyse your application's actual usage patterns — page views by hour, peak concurrent users, critical transaction flows, and seasonal traffic patterns — to build a load model that accurately simulates real user behaviour. Generic load tests that ignore real usage patterns miss the bottlenecks that matter in production.
When performance degrades under load, we pinpoint exactly where — slow database queries, unoptimised connection pools, inadequate caching, memory-intensive operations, or infrastructure limits. We don't just tell you "the system slows down at 500 users" — we tell you specifically which component is the constraint and what to do about it.
We generate test load from cloud infrastructure — AWS, Azure, or GCP — to simulate realistic distributed traffic from multiple geographic locations. Local load generation tools give you misleading results because real users come from different locations, not a single machine on your office network.
We integrate your performance tests with monitoring tools — Grafana, InfluxDB, New Relic, Datadog, or AWS CloudWatch — providing real-time visibility into server CPU, memory, database connections, and response times during test execution. This makes bottleneck identification significantly faster and more accurate.
We configure automated performance tests that run in your CI/CD pipeline on every release — with defined pass/fail thresholds for response time and error rate. If a code change causes a significant performance regression, the build fails automatically before the change reaches production.
A comprehensive report covering test scenarios, load profiles executed, response time percentiles (p50, p90, p95, p99), throughput measurements, error rates, and specific bottleneck findings — with prioritised, actionable recommendations for your development and infrastructure teams.
We use the right tool for each project — not a one-size-fits-all approach to performance testing.
JMeter is our primary tool for most performance testing engagements — mature, feature-rich, and capable of simulating thousands of concurrent users with realistic HTTP, database, and message queue load patterns. Gatling is our choice for teams that want code-based test scripts in Scala and high-performance load generation. k6 is ideal for teams already working in JavaScript and for API-focused performance testing with a modern, developer-friendly scripting experience. We recommend the right tool based on your tech stack and team.
A structured performance testing engagement from baseline measurement to production-ready recommendations.
We establish your current system performance baseline under normal load, review your traffic analytics and business peak periods, and define specific performance requirements — target response times, acceptable error rates, required concurrent user capacity. These requirements become the pass/fail criteria for all subsequent testing.
We design realistic load scenarios modelling your actual user behaviour — which pages users visit, in what order, with what think times, and in what proportions. A checkout flow represents maybe 5% of your traffic but 100% of your revenue, so it gets disproportionate attention. We design the load model to reflect these real-world priorities.
We write performance test scripts with proper parameterisation — realistic user data, dynamic correlation of session tokens and CSRF values, and accurate simulation of browser-level HTTP behaviour. Scripts are validated against your test environment to confirm they accurately simulate real user journeys before high-load testing begins.
We execute the full test programme — baseline, load, stress, and spike tests — from cloud infrastructure, with real-time monitoring dashboards active throughout. We watch for performance anomalies as they occur and can pause tests to protect your environment if unexpected issues arise during execution.
We analyse all test results, correlate performance metrics with monitoring data to identify root-cause bottlenecks, and produce a detailed written report with findings and specific recommendations. We present findings to your engineering and infrastructure teams in a working session, answering technical questions and prioritising the remediation backlog together.
Performance testing is most valuable before major releases, before launching marketing campaigns that will drive significant traffic, before scaling to new markets, and whenever major architectural changes are made. Ideally, lightweight performance tests run in your CI/CD pipeline on every release, with comprehensive performance test campaigns run quarterly or before significant traffic events. Starting performance testing early — not just before launch — means issues are caught when they are cheap to fix.
The number depends on your actual traffic — we base our load targets on your current peak concurrent users plus a 3–5x growth buffer, not arbitrary round numbers. We analyse your web analytics, application logs, or CDN data to determine realistic targets. Testing with 10,000 virtual users when your peak is 500 concurrent users wastes budget and generates misleading results.
Yes. We handle authenticated load test scenarios using parameterised credentials, session management, OAuth token refresh, and CSRF token correlation. We work with your development team to set up appropriate test accounts or authentication bypass mechanisms that allow load testing without affecting production user data.
We strongly recommend running performance tests against a dedicated test or staging environment that mirrors production — not against production itself. If production testing is unavoidable (for some cloud-native architectures this is the only realistic option), we schedule testing during off-peak hours and implement careful load ramp-up procedures to avoid customer impact.
Yes. 360 Fahrenheit is based in Lahore, Pakistan and serves clients across Pakistan, Saudi Arabia, UAE, UK, and the United States. Performance testing is fully remote — our load generation infrastructure runs in the cloud, and all communication and reporting is delivered digitally. Time zone differences are not a barrier to effective performance testing engagements.