Kindlebit
Product Engineering and QAProposed offering

Find the breaking point before your traffic does, and stop paying for capacity you do not need.

We model real usage, run load, stress and endurance tests, find the bottleneck in the code, database or infrastructure, and recommend fixes and capacity that match your targets and budget.

Plan a load testSee the test approach

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

Performance loop

  1. Workload model
  2. Load test
  3. Monitor
  4. Find bottleneck
  5. Fix
  6. Retest

Reference design. Components are options, not a statement of what is deployed at any customer.

Problems this service is built to solve

These are situations we expect buyers to recognise. Each one states why it happens, what it costs and how we would approach it.

Traffic spike failures

A ticket sale opens at noon. The site handles normal traffic well, but at noon requests time out, the queue overflows and customers see errors.

Why it happens
The system was never tested at spike levels, and connection pools and autoscaling were left at defaults.
What it costs
Lost sales, public complaints and emergency fixes during the event.
How we approach it
We model the spike from past events and projections, test beyond it, identify the first thing that fails, and tune scaling, queues and limits.
What to measure
Maximum sustainable throughput, error rate at target load and time to scale.

Slow APIs

A dashboard calls an API that takes seven seconds under normal use. Users think the application is broken.

Why it happens
Inefficient queries, chatty calls and no caching, which unit tests do not reveal.
What it costs
Frustrated users and abandoned tasks.
How we approach it
We measure latency distributions per endpoint under load, profile the slowest, fix queries and serialisation, and set performance budgets in the pipeline.
What to measure
p50, p95 and p99 latency per endpoint and the share of requests within the budget.

Database bottlenecks

At moderate load, CPU on the database hits one hundred percent. A single report query locks tables used by the checkout.

Why it happens
Missing indexes, long transactions and mixed workloads on one instance.
What it costs
Outages that scaling the application tier cannot cure.
How we approach it
We capture query plans and wait statistics under load, tune indexes and queries, separate reporting load and size connection pools.
What to measure
Slow query count, lock wait time and database CPU at target load.

Inefficient infrastructure spending

The company runs far more servers than needed to be safe, because nobody knows the real capacity.

Why it happens
Capacity decisions are made without test data.
What it costs
Cloud costs that grow with fear, not demand.
How we approach it
We measure capacity per instance type, define a safe utilisation target and recommend right sizing and autoscaling policies, then verify with tests.
What to measure
Cost per thousand transactions, utilisation at target load and headroom.

Solutions we engineer

Concrete capabilities, each with the need it serves, how it integrates, what you receive and the value to expect.

Workload modelling

Analysis of production logs and analytics to build realistic mixes, think times and data.

Customer need
Test what real users do.
Integration
Logs and analytics tools.
Deliverable
Workload model.
Business value
Valid results.

Load and stress testing

Ramp, spike, soak and breakpoint tests with defined pass criteria.

Customer need
Know capacity and failure behaviour.
Integration
Test environment or controlled production.
Deliverable
Test scripts and results.
Business value
Evidence of capacity.

Endurance testing

Long running tests tracking memory, connections and queue growth.

Customer need
Find leaks and degradation.
Integration
Monitoring.
Deliverable
Endurance report.
Business value
Stability over time.

Bottleneck analysis

Tracing, profiling and database analysis during tests.

Customer need
Locate the true constraint.
Integration
APM and database tools.
Deliverable
Analysis and fix list.
Business value
Targeted improvement.

Capacity planning

Models linking traffic to resources and cost, with scenarios.

Customer need
Plan for growth and events.
Integration
Cloud metrics and billing.
Deliverable
Capacity plan.
Business value
Cost control and readiness.

Performance in CI

Smoke performance tests with budgets in the pipeline and trend dashboards.

Customer need
Prevent regressions.
Integration
CI and k6 or similar.
Deliverable
Pipeline checks.
Business value
Early detection.

Optimisation support

Recommendations and hands on fixes in code, queries and infrastructure.

Customer need
Close the gap.
Integration
Your engineers.
Deliverable
Implemented improvements and retest.
Business value
Verified gains.

How we solve it

A delivery sequence built around coverage, execution, defect analysis and release criteria.

Define targets

Agree users, throughput, response time targets, availability and the events that matter.

Model the workload

Analyse logs to build user journeys, data volumes and arrival patterns.

Prepare environment and tools

Set up a production like environment, test data and monitoring, and write scripts.

Run baseline tests

Measure behaviour at normal load and record metrics for comparison.

Run load, stress and soak tests

Increase load, find limits and watch for degradation over time.

Analyse bottlenecks

Correlate results with traces, resource use and queries, and rank causes.

Fix and retest

Apply changes, rerun tests and confirm improvements and no regressions.

Report and monitor

Deliver capacity findings, set performance budgets and monitoring for production.

Solution in action: Preparing a ticketing site for a high demand sale

Reference Architecture An illustrative scenario. It describes how we would structure the work, not a delivered customer project.

Starting problem

A sale is opening in three weeks and last year's launch failed.

Existing workflow

Tests are limited to a few users, and scaling relies on default settings.

Improved workflow

The workload is modelled from last year's logs. A spike test finds that connection pool exhaustion occurs at forty percent of target load, caused by a slow seat lock query. After fixes and a queue for the booking step, a retest meets the target with headroom, and autoscaling thresholds are adjusted.

A high traffic event is scheduled.

Systems involved

Web tier, API, database, queue, load generators, monitoring.

Data movement

Synthetic accounts and seats, anonymised traffic profile.

Human decisions

Engineers review findings. Product owner approves the waiting room design.

Automation opportunities

Test execution, metric capture and report generation.

Exception handling

Tests stop automatically if error rates exceed safety limits.

Resulting user experience

The sale opens and customers experience a queue, not errors.

KPIs to evaluate

  • Throughput at target latency
  • Error rate at peak
  • Time to scale
  • Cost per thousand orders

What you receive

Concrete deliverables for this service, written so you can check them against the contract.

  • Performance targets and workload model
  • Load, stress and soak test scripts
  • Test results with graphs and raw data
  • Bottleneck analysis and fix recommendations
  • Capacity and cost plan
  • CI performance checks and budgets
  • Monitoring dashboards for production
  • Retest report after fixes

Technology and engineering

Options we would evaluate for this service. Unless a group is marked as publicly listed on kindlebit.com, treat each tool as a proposed implementation option. Naming a tool does not imply a vendor partnership.

Load tools (proposed implementation options)

  • k6
  • Apache JMeter
  • Gatling
  • Locust

Observability

  • Grafana
  • Prometheus
  • Datadog
  • New Relic
  • OpenTelemetry

Database analysis

  • pg_stat_statements
  • SQL Server Query Store
  • MySQL slow query log

Cloud

  • AWS
  • Azure
  • Google Cloud

Relevant Kindlebit work and evidence

We use the strongest evidence available and say which kind it is.

Proposed offering

Evidence status for this service

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

See case study status
Reference ArchitectureInteractive demo with simulated data

Performance panel within the quality platform demonstration

The demonstration includes a simulated performance run with latency percentiles. Results are synthetic.

Open the demonstration

Business outcomes and success criteria

These are the measures we would agree before work starts. They are criteria for success, not results from past engagements.

Latency

p95 and p99 response times at target load.

Capacity

Maximum throughput within error and latency limits.

Reliability

Error rate during spikes and soak tests.

Cost efficiency

Infrastructure cost per thousand transactions.

Questions buyers ask

Can you test in production?

Sometimes, with safeguards. We usually prefer a production like environment, and use controlled production tests for specific cases.

How realistic is a test environment?

As realistic as data volume, configuration and dependencies allow. We document differences and scale results with care.

How long does a performance test engagement take?

A focused engagement takes two to six weeks, depending on journeys, environment readiness and required fixes.

Which tool do you use?

We commonly use k6, JMeter or Gatling, chosen by your protocols, team skills and CI needs.

What if we have no monitoring?

We set up the minimum monitoring needed to interpret tests, and recommend production monitoring.

Do you only test, or also fix?

Both are available. Many clients use our analysis and fix themselves, others ask us to implement and retest.

Related services

Industries where this applies

Plan a load test before your next peak.

Tell us about the event, the system and the target. We will outline the test and what it would take to pass.

Plan your load test Explore a demonstration