QA & Release Assurance

Performance Testing

See how your application behaves under real traffic before a launch or campaign tests it for you. We run load, stress and soak tests, trace bottlenecks to their cause and help you fix them.

Discuss your projectPlan a load test

Who plans load tests with us

You don't know how much traffic your product can take before pages slow down or requests fail, or which part gives way first: the code, the database or a service you depend on.

  • Teams expecting a traffic peak from a launch, promotion or press coverage
  • Engineering leads after moving database, hosting or architecture
  • SaaS teams about to onboard a customer much larger than any current one

From baseline to findings

Typical test sequence for a web application; stop criteria are agreed with you before the first run.

  1. Baseline

    Record how critical flows behave at normal traffic, so every later run has a reference point.

    Checkpoint: Targets and stop criteria signed off

  2. Load to peak

    Ramp to your expected peak and hold it, watching queues, connection pools and third-party calls.

    Checkpoint: Stop if errors pass the agreed limit

  3. Stress past peak

    Raise load in steps beyond peak until something gives way, and note which component fails first.

    Checkpoint: Stop at the agreed load ceiling

  4. Sudden spike

    Send a sharp surge, then drop it, to see whether autoscaling and caches recover cleanly.

    Checkpoint: Stop if recovery stalls

  5. Long soak

    Hold steady load over a long window to expose slow leaks and gradual drift.

    Checkpoint: Stop if memory keeps climbing

  6. Findings and retest

    Rank bottlenecks by user impact, propose fixes, and rerun the affected scenarios to confirm them.

When something fails: If a stop criterion trips, we halt the run, keep the logs and metrics, and agree next steps with you before resuming.

Know your limits before traffic finds them

Slow pages and timeouts tend to appear when traffic peaks: a launch, a sale, a campaign or a month-end batch. Performance testing shows how your application, database and infrastructure behave under realistic load, where they degrade and why. We model traffic from your analytics and plans, test in an environment agreed with you, trace bottlenecks to their cause and retest after fixes. That includes features that call AI models, where latency, rate limits and cost grow with traffic.

AI-assisted analysis, engineer-led testing

How AI assists

  • Drafts load scripts and traffic models from your API specs, analytics and access logs, for engineers to review.
  • Correlates response times with traces, database queries and resource metrics to point to likely bottlenecks.
  • Summarizes long test runs and compares them with earlier baselines to flag performance regressions.

What our experts own

  • Engineers decide what realistic load means for your business and which thresholds count as a failure.
  • Test windows, environments and load limits are agreed with you before any test runs.
  • Each bottleneck is confirmed with profiling before a fix is recommended.
  • Fixes are prioritized by impact and effort, then retested against the same baseline.

What you receive

Load tests, diagnosis and a capacity plan

  • Load and stress testing

    Expected and peak traffic simulated with tools such as k6, JMeter or Gatling, to find where response times and error rates start to climb.

  • Spike and soak testing

    Sudden surges and long endurance runs that expose scaling gaps, memory leaks and connection-pool exhaustion.

  • Bottleneck analysis

    Slow queries, missing indexes, repeated database calls, blocking code and saturated services, traced with profiling and observability data.

  • Frontend performance review

    Core Web Vitals, bundle size, rendering and caching checked on the pages that matter most, with specific fixes.

  • Third-party and AI model limits

    How payment gateways, model APIs and other services behave under load: rate limits, timeouts, retries, fallbacks and usage costs.

  • Capacity report and baselines

    Where your system degrades first and what to fix, plus repeatable scripts and baselines in your repository for future releases.

How a performance test runs

  1. 01

    Baseline and goals

    Measure current behavior, agree target response times and error rates for critical flows, and confirm the test environment.

  2. 02

    Model realistic traffic

    Build scenarios from analytics, logs and business plans: user mix, ramp-up, peak and sustained load, including third-party calls.

  3. 03

    Run and diagnose

    Run tests while watching application, database and infrastructure metrics, and trace each bottleneck to its cause.

  4. 04

    Fix, retest, report

    Recommend or implement fixes, rerun the same scenarios to confirm the gain, and deliver the capacity report.

Two ways to work with AI tools

AI helps draft tests and investigate defects. Choose where it may process your code and test data.

Not sure? We'll recommend one during scoping. Compare AI delivery options

Typical load-testing requests

Typical scenarios we scope, not client case studies.

  • A limited release with a sharp surge

    A store plans a limited product drop where most visitors arrive at once. We model the surge from past traffic and expected sign-ups, run spike tests in staging and show which component saturates first.

  • Slower pages after a database move

    A team moved to a managed database and busy hours now feel slow. We rerun the same scenarios against earlier measurements, profile the slowest queries and connection settings, and confirm each fix with a retest.

  • An AI summary on a busy page

    A product adds an AI-generated summary to a page most visitors open. We test how the model provider's rate limits, timeouts and retries behave at peak, check the fallback users see, and estimate how usage costs grow with traffic.

What load testing leaves out

  • Deliberate denial-of-service attacks are out of scope; we generate realistic traffic, not attack traffic.
  • Third-party APIs are loaded only as far as their terms allow; beyond that we stub them and test how you handle their limits.
  • Functional correctness is Software QA & Testing; this service measures speed, errors and capacity under load.
  • Fixes beyond queries, caching and code paths, such as re-architecture or new hosting, are scoped separately under Cloud Infrastructure or Application Modernization & Stabilization.

How performance work connects across the team

  • Design: speed users can feel

    Designers review loading states, progressive rendering and feedback for slow actions, so the product feels responsive even when work takes time.

  • Engineering: fixes at the cause

    Engineers fix the queries, caching and code paths found in testing, and the same scenario is rerun to confirm the improvement.

  • Operations: capacity and alerts

    Findings feed server sizing or autoscaling, capacity and cost plans, and alert thresholds, agreed with whoever runs your infrastructure.

  • Ongoing: catch slowdowns early

    Key scenarios rerun before major releases and after infrastructure changes, so performance regressions surface before users notice them.

FAQ

Frequently asked questions

Will performance testing affect our live users?

Normally we test in a staging environment sized like production. If a production test is needed, we agree the window, load limits and stop conditions with you first, and notify third-party providers where their terms require it.

How much load can you simulate?

Enough to reach your realistic peak and go past it. We use distributed load generators in the cloud when one machine isn't enough. The goal is to find where your system degrades and why, not to produce an impressive number.

How often should we run performance tests?

Before major launches and campaigns, after infrastructure or architecture changes, and when you switch the model or provider behind an AI feature. Key scenarios can also run on a schedule or in your pipeline to catch gradual slowdowns.

Do you need production data, and do AI tools see it?

Most tests need no production data: we model traffic from analytics and anonymized logs and generate synthetic test data. AI-assisted analysis runs within the boundary you choose: Private / Local AI Engineering on infrastructure you control, or Claude Code / OpenAI Codex Engineering with commercial providers under agreed data-handling terms.

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Plan for your next traffic peak

Tell us about your next launch, campaign or traffic concern. We'll suggest the scenarios worth testing first.

Discuss your projectPlan a performance test