Can you test against production?

We prefer a production-like staging environment with synthetic data. Where only production will do, we run carefully rate-controlled tests in agreed low-traffic windows with an instant kill switch.

Which tools do you use?

k6, JMeter, and Gatling for load generation; APM tooling (Datadog, New Relic, Grafana) for diagnosis. Scripts and dashboards are handed off so you can re-run every scenario yourself.

How do you model realistic traffic patterns?

We analyze your historical logs to extract real user journeys, then replay them at scale while gradually ramping load. This beats synthetic patterns because it mirrors the actual contention your system will see.

What is a soak test and why do it?

Soak tests run moderate load for hours or days to catch memory leaks and connection exhaustion that only appear after sustained use. They reveal problems that spike tests miss.

How much does infrastructure need to cost to pass load tests?

That depends on your traffic forecast and availability target. We quantify the trade-off: exact costs for different scenarios so you can optimize ROI, whether that means scaling horizontally, tuning code, or both.

Quality Engineering & Assurance

Performance, Scalability & Load Testing Built for Peak Performance

Testing for the breaking point so launch day, sale day, and viral day all feel like any other day. We simulate extreme concurrent user traffic to identify database deadlocks, API latency, and scaling limits before they impact customers.

Talk to an Expert

Start your project with us

Resilient by Design

Every software architecture has a breaking point under high concurrent load, and finding it early prevents costly public failures. We model realistic user journeys and simulate peak traffic spikes, stress levels, and long-running endurance runs to expose hidden infrastructure limitations. Our performance engineers trace execution paths from edge CDNs down to database locks and memory usage. We work closely with your development teams to prioritize bottlenecks, tune query structures, and validate auto-scaling rules so your application remains highly responsive and stable during major traffic events.

The Old Way

Fragile Apps

The Intelegencia Way

Hardened Apps

Crashing under Traffic

Elastic Load Handling

High API Latency

Sub-200ms Latency

Database Bottlenecks

Tuned Query Paths

Memory Leaks at Scale

Stable Long-Run Memory

Unproven Auto-Scaling

Validated Scale-Out Logic

High-Scale Load Testing

We simulate massive concurrent user spikes to stress-test your platform's absolute capacity limits. By analyzing system behavior under load, we identify resource leaks, database connection limits, and API degradation points before your product launch, giving your team the data needed to scale confidently.

Stress & Spike Testing

Endurance (Soak) Testing

API Performance Audit

DB Query Optimization

Front-End Speed Analysis

Bottleneck Diagnosis & Tuning

Locating structural bottlenecks is key to optimizing system performance. We trace every millisecond of request latency to identify slow database queries, cold caches, or unoptimized microservice communications. We deliver a prioritized remediation plan and run verification tests to prove latency reductions.

Query & Index Tuning Plans

Cache & CDN Strategy

Auto-Scaling Validation

Connection Pool Sizing

Before/After Benchmark Reports

Capacity Planning & Scalability Validation

System growth requires proactive resource planning rather than reactive fire-fighting. We model your expected traffic growth milestones and validate that your auto-scaling policies deploy resources correctly. You receive clear benchmarks indicating when to scale database replicas or upgrade compute instances.

Traffic Growth Forecasting

Multi-Stage Capacity Tests

Infrastructure Cost Modeling

Auto-Scaling Tuning

Threshold Alerting Setup

Capacity Planning & Scalability Validation

The Resilience Stack

Performance Units

Four pillars that stress-test your infrastructure: simulating extreme concurrent load, measuring response times at scale, forecasting capacity needs as you grow, and injecting failures to prove system resilience survives outages.

Scale Simulator

Testing auto-scaling logic under extreme load.

Latency Profiler

Tracing every millisecond from edge to database.

Capacity Planner

Modeling the infrastructure your growth will need.

Chaos Engine

Injecting failures to test system resilience.

Production-Like Test Environments

Testing in staging environments that diverge from production often conceals concurrency issues. We construct realistic test environments that mirror your production configuration, seeding them with production-parity data volumes. This high-fidelity simulation guarantees that test performance metrics accurately reflect real-world user experiences.

Production Configuration Mirroring

Data Volume Parity

Network Condition Injection

Third-Party Service Simulation

Chaos Test Scenarios

The Test

Our Performance Flow

Five steps that simulate peak traffic and prove resilience: mapping real workload patterns, measuring current baseline capacity, pushing the app until it breaks, remediating bottlenecks in code and infrastructure, and validating that fixes hold under sustained pressure.

Workload Model

Mapping real traffic patterns and peak scenarios.

Baseline Test

Measuring current speed and capacity limits.

Stress Run

Pushing the app until it breaks to find the limit.

Remediation

Tuning code, queries, and infrastructure.

Final Verify

Validating that fixes hold up under pressure.

Measured Performance. Proven Growth.

Max Capacity

P95 Latency

Launch Incidents

Test Scenarios

Frequently Asked Questions
About Performance, Scalability & Load Testing

Here you will find answers to questions we get asked the most about our offerings.

Before anything that changes your traffic profile: launches, marketing pushes, seasonal peaks, big architectural changes. As a rule, if a date matters to the business, test capacity at least four weeks ahead so there is time to fix what we find.