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★ ★ ★ ★ ★ 4.9 Client Rated
Load testing answers the question your engineering and business teams need answered before every major release: will the system hold up when real users actually use it? We design and execute load tests that simulate your expected peak concurrent user volumes — from hundreds to hundreds of thousands of simultaneous sessions — against your web applications, APIs, and backend services, measuring response times, throughput, error rates, and resource utilization as load increases. Our load testing engagements are built around realistic user behavior models drawn from your actual traffic analytics, not synthetic uniform request patterns that produce results that look good in reports but fail to replicate real-world conditions. We use k6, Apache JMeter, and Gatling depending on your technology stack, scripting requirements, and CI/CD integration needs — and every test delivers a findings report that maps observed degradation to the specific architectural or code-level bottleneck responsible.
Every system has a breaking point — and the operational question is whether you discover it during a planned stress test or during a production incident at the worst possible moment. Our stress testing service pushes your system beyond its expected operating capacity to identify where failure occurs, how it manifests (graceful degradation vs. catastrophic failure), and how the system recovers when load returns to normal levels. We apply progressively increasing load well beyond your peak traffic projections, identify the components — database connection pools, thread queues, memory allocation, external service dependencies — that become the failure boundary, and provide the engineering diagnosis and remediation recommendations that allow your team to address those limits before they're encountered in production. Stress testing is particularly critical before major commercial events — product launches, marketing campaigns, seasonal peaks — where traffic spikes are predictable but their magnitude is uncertain.
Scalability testing evaluates your system's ability to maintain acceptable performance levels as both load and infrastructure scale — answering whether your architecture can grow with your business, and at what cost. We design scalability test suites that measure performance across a defined range of load levels and infrastructure configurations, quantifying the relationship between user volume, infrastructure investment, and response time degradation. This work is particularly valuable for organizations evaluating cloud auto-scaling configurations, planning infrastructure sizing for anticipated growth, validating Kubernetes horizontal pod autoscaling behavior, and making the build-vs-buy infrastructure decisions that require a clear picture of performance-per-dollar at scale. Scalability test results feed directly into capacity planning models that give your engineering and finance teams a shared, data-grounded basis for infrastructure investment decisions.
Production traffic rarely arrives uniformly — it comes in bursts, spikes, and waves that systems engineered only for average load handle poorly. Our spike testing service simulates sudden, sharp increases in load — the flash sale that sends e-commerce traffic from baseline to 10x in under a minute, the viral social media moment that floods a content platform, the coordinated login event at the start of a live event — to evaluate how your system responds to abrupt load transitions rather than gradual ramp-ups. We specifically test the behavior of auto-scaling infrastructure under spike conditions (where scaling latency can allow response times to degrade significantly before new capacity comes online), caching layer behavior during cache cold-start periods, and queue saturation dynamics in event-driven architectures where sudden message volume surges can create cascading backpressure.
A system that performs correctly under load for thirty minutes may fail under the same load over eight hours — and the failures that emerge over time are often the most expensive and the hardest to diagnose in production. Our endurance testing service runs your system under sustained production-representative load for extended periods — typically 4–24 hours depending on your operational requirements — specifically targeting the time-dependent failure modes that short-duration tests miss: memory leaks that grow gradually until they trigger out-of-memory failures, database connection pool exhaustion that accumulates under sustained load, thread leaks that degrade throughput over time, file descriptor exhaustion in high-throughput I/O operations, and cache hit rate degradation as data access patterns shift over a longer operational window. Endurance test findings directly predict which production incidents are most likely to occur during your next peak traffic period.
APIs are the performance-critical layer in most modern application architectures — the interface through which every client, integration, and microservice interaction flows — and API performance problems propagate upstream into every user experience that depends on them. Our API performance testing service validates the throughput, latency, and reliability of your REST and GraphQL APIs under realistic call volumes, testing individual endpoint performance as well as the realistic mixed-traffic patterns of concurrent API consumers with varied request distributions. We test both authenticated and unauthenticated endpoints, evaluate rate limiting and throttling configuration under load, test the behavior of API gateway layers and downstream service dependencies under concurrent request pressure, and identify the response time outliers (P95, P99 latency) that average response time metrics systematically conceal but that users and dependent systems actually experience.
Identifying that a performance problem exists is the starting point, not the endpoint. Our bottleneck analysis service goes beyond test execution to provide the engineering diagnosis that tells your development team exactly what is causing observed degradation and what to do about it. We correlate performance test observations with application profiling data (CPU flame graphs, heap dumps, garbage collection logs), database query execution plans and index utilization analysis, network latency and connection overhead measurement, and infrastructure resource utilization telemetry — triangulating from symptom to root cause with the engineering depth required to produce actionable remediation recommendations rather than generic "optimize your database queries" advice. For complex multi-service architectures, our distributed tracing analysis identifies the specific service, query, or external dependency in the call chain that is responsible for tail latency and throughput degradation.
Performance regressions introduced early in the development cycle are exponentially cheaper to fix than those discovered in pre-release load testing or, worse, in production. Our CI/CD performance testing service integrates automated performance baselines directly into your delivery pipeline — using k6, Gatling, or Locust to run lightweight performance smoke tests on every build, with configurable thresholds that fail the pipeline when response time or error rate regressions exceed defined limits. We design the performance testing strategy across the full delivery pipeline: unit-level performance benchmarks in the build stage, API-level performance smoke tests in the integration stage, and full load test execution against staging environments triggered by merge-to-main events or release candidate promotion. This shift-left approach gives your engineering teams the feedback they need to catch and fix performance regressions at the point where they're cheapest to resolve — in the development cycle, not the release cycle.
Discover the maximum capabilities of your system through capacity testing. Using tools like Apache JMeter and LoadRunner, we identify the limits of user load and resource usage, enabling you to optimize performance for peak demand without compromising reliability.
Ensure your application performs seamlessly across different environments and settings. Through configuration testing, we assess how varied system setups impact performance using tools like Apache JMeter and LoadRunner, ensuring a consistent experience across all configurations.
Resolve bottlenecks efficiently with isolation testing. We evaluate individual components or modules using tools like Gatling and Apache JMeter, identifying and addressing specific performance challenges without disrupting the overall system.
Openpay needed a substantial upgrade to its payment processing capabilities, particularly focusing on mobile applications. The aim was to integrate advanced technologies for secure credit card transactions and to enhance core business functionalities. The project demanded extensive technical expertise to support mobile payment initiatives and refine essential system processes.
Coca-Cola sought an intelligent customer segmentation system that could identify and analyze behavioral patterns across different market segments. The solution had to automatically adapt to new data, allowing for optimized marketing strategies and improved return on investment.
Aston Martin sought to streamline and enhance the purchasing process, focusing on improving user experience across all touchpoints for an online real estate marketplace. This involved simplifying navigation, increasing transparency, and implementing personalized engagement strategies to cater to individual preferences and streamline the path to purchase.
The most operationally painful performance incidents — the e-commerce site that goes down during a flash sale, the SaaS platform that degrades on Monday morning when all users log in simultaneously, the payment gateway that times out during end-of-quarter processing peaks — are almost never surprising to the engineering teams who understand the system's architecture. The bottlenecks that cause production failures under load are typically present in the system long before they're triggered, and they're detectable through rigorous performance testing conducted under realistic load conditions well before the traffic event that surfaces them. The performance incidents that feel sudden and unexpected are almost always the result of insufficient performance testing coverage, not genuinely unpredictable system behavior. Organizations that invest in systematic performance testing before peak traffic events consistently avoid the incident response cost, reputational damage, and emergency remediation expense that organizations without that investment pay when production systems fail under load.
The most commonly reported performance metric — average response time — is also one of the least informative for making engineering decisions about production performance. Average response time hides the distribution of performance across all requests: a system with an average response time of 200ms that delivers 95% of responses in under 100ms but 5% in over 2,000ms provides a dramatically worse user experience than the average suggests, and it's the 5% of slow responses that generate user complaints, trigger dependent service timeouts, and drive churn in user experience research. The metrics that actually predict user experience quality and system reliability are percentile latencies — P95 (the response time experienced by the 95th percentile of requests), P99, and P99.9 — alongside error rate and throughput. Engineering teams that make performance decisions based on averages rather than percentile distributions consistently underestimate the performance problems their users are experiencing. Regression Testing: Conducted after code updates, ensuring that new changes do not negatively impact existing functionalities, keeping the software stable as it evolves. Sanity Testing: After any changes or fixes, sanity testing ensures specific functionalities or areas of the application still operate correctly, saving time by narrowing the testing focus. Smoke Testing: Provides a quick, high-level assessment of the software’s main features to determine if the build is stable enough for further detailed testing.
The single most common reason performance test results fail to predict production behavior is that the test environment where the testing is conducted differs materially from the production environment in ways that affect performance characteristics. An application tested against a single-instance database on the same host as the application server will behave completely differently under load than it will in production against a multi-tenant cloud database with network latency, connection pooling constraints, and competing workloads. Testing against a seeded dataset of ten thousand records tells you nothing about query performance at ten million records. Load generating against an application on the same network produces response time measurements that exclude the network latency that real users experience. Investing in production-representative test environments — with realistic data volumes, realistic infrastructure topology, and realistic external dependency behavior — is the prerequisite for performance test results that are actually predictive of production outcomes.
The cost of remediating a performance problem is determined almost entirely by when in the development and delivery cycle it is discovered. A performance regression caught by an automated threshold check in the CI/CD pipeline — before the code that introduced it has been merged — takes one developer an hour to fix. The same regression discovered in pre-release load testing requires test environment setup, test execution, profiling, code fix, retest, and regression validation — measured in days. The same regression discovered in production triggers an incident response process, emergency deployment, potential rollback, customer communication, and post-incident review — measured in engineer-weeks and reputational cost. Shift-left performance testing — embedding automated performance checks at every stage of the delivery pipeline — doesn't eliminate performance problems, but it consistently moves their discovery to the point where they're cheapest and fastest to resolve, compounding into significantly lower total engineering cost per performance issue over time.
Modern applications are deeply dependent on third-party services — payment gateways, identity providers, CDNs, analytics platforms, notification services, mapping APIs — and the performance of these dependencies under load is one of the most significant and least tested sources of production performance risk. Internal load testing that mocks or stubs third-party service responses produces performance results that measure the application's own performance but miss the reality that under production load, third-party services may respond slowly, rate-limit requests, or return errors at rates that create cascading degradation throughout the application. Performance testing that includes realistic third-party dependency behavior — using measured response time distributions from production monitoring, configured rate limiting at realistic thresholds, and simulated error injection for dependency failure scenarios — produces results that are substantially more predictive of actual production performance than testing that treats dependencies as infinitely fast and reliable.
The most mature engineering organizations treat performance as a continuous engineering discipline — measuring, monitoring, and improving system performance as an ongoing operational practice — rather than as a testing phase that happens before major releases. This means maintaining performance baselines that are updated with every release and trigger alerts when they regress, instrumenting production systems with the percentile latency and throughput monitoring that makes performance degradation visible before users notice it, conducting regular performance reviews that connect observed production metrics to development priorities, and treating performance regression as a bug with the same priority discipline applied to functional bugs. Organizations that practice performance engineering continuously consistently catch degradations earlier, fix them faster, and deliver more reliable performance improvements than those that treat performance testing as a periodic checkpoint disconnected from the ongoing development process.
We build high-performance software engineering teams better than everyone else.
Coderio specializes in Performance Testing, delivering scalable and secure solutions for businesses of all sizes. Our skilled developers have extensive experience building modern applications, integrating complex systems, and migrating legacy platforms. We stay up to date with the latest technology advancements to ensure your project's success.
We have a dedicated team of Performance Testing with deep expertise in creating custom, scalable applications across a range of industries. Our team is experienced in both backend and frontend development, enabling us to build solutions that are not only functional but also visually appealing and user-friendly.
No matter what you want to build, our tailored services provide the expertise to elevate your projects. We customize our approach to meet your needs, ensuring better collaboration and a higher-quality final product.
Our engineering practices were forged in the highest standards of our many Fortune 500 clients.
We can assemble your Performance Testing team within 7 days from the 10k pre-vetted engineers in our community. Our experienced, on-demand, ready talent will significantly accelerate your time to value.
We are big enough to solve your problems but small enough to really care for your success.
Our Guilds and Chapters ensure a shared knowledge base and systemic cross-pollination of ideas amongst all our engineers. Beyond their specific expertise, the knowledge and experience of the whole engineering team is always available to any individual developer.
We believe in transparency and close collaboration with our clients. From the initial planning stages through development and deployment, we keep you informed at every step. Your feedback is always welcome, and we ensure that the final product meets your specific business needs.
Beyond the specific software developers working on your project, our COO, CTO, Subject Matter Expert, and the Service Delivery Manager will also actively participate in adding expertise, oversight, ingenuity, and value.
Smooth. Swift. Simple.
We are eager to learn about your business objectives, understand your tech requirements, and specific Performance Testing needs.
We can assemble your team of experienced, timezone-aligned, expert Performance Testing developers within 7 days.
Our [tech] developers can quickly onboard, integrate with your team, and add value from the first moment.
Whether you’re looking to leverage the latest technologies, improve your infrastructure, or build high-performance applications, our team is here to guide you.
Accelerate your software development with our on-demand nearshore engineering teams.
