PERFORMANCE & LOAD VALIDATION

Performance Testing.

Stress test throughput, latency and resilience before traffic surges occur.

Performance failures rarely happen during gentle development testing—they happen during marketing launches, flash sales, and high-concurrency usage spikes. AKREVON evaluates system limits, isolates query and memory bottlenecks, and establishes concrete performance budgets.

k6 & LocustLatency Percentiles (p95/p99)Stress & Spike TestsDatabase BottlenecksCore Web Vitals
Performance & Load Telemetry
Target SLO Passed
Throughput8,200 req/s2,500 VU active
p95 Latency124msSLO < 200ms
p99 Latency198msError rate: 0.00%
Latency Distribution Curvep50: 58ms // Zero Crash Rate
20ms100ms (p95)200ms (p99)500ms+
Load Engine: k6 Distributed ClusterCore Web Vitals: Green (LCP 1.2s, INP 42ms)

QUALITY CAPABILITIES

Measure latency, throughput, and system limits under load.

We simulate real-world concurrency, identify database lockups and API latency spikes, and optimize response times across your stack.

Load, Stress & Spike Testing
Load Testing

Load, Stress & Spike Testing

Simulate sustained traffic, sudden multi-thousand concurrent user spikes, and prolonged soak loads using k6 and distributed load engines.

k6 DistributedConcurreny SimulationSoak & Stress Runs
API Latency & Throughput Profiling
API Profiling

API Latency & Throughput Profiling

Measure p50, p95, and p99 response times across high-traffic endpoints, isolating slow database queries and microservice hops.

p95 & p99 MetricsQuery BottlenecksConnection Pools
Frontend Performance & Core Web Vitals
Web Vitals

Frontend Performance & Core Web Vitals

Audit Largest Contentful Paint (LCP), Interaction to Next Paint (INP), and Cumulative Layout Shift (CLS) on real devices and networks.

LCP & INP BudgetsBundle Size AuditsAsset Optimization
Database Bottleneck & Lock Analysis
Database

Database Bottleneck & Lock Analysis

Analyze query execution plans, table lock contention, indexing gaps, and connection pool saturation under heavy write operations.

EXPLAIN ANALYZELock ContentionIndex Optimisation
Resilience & Failover Validation
Resilience

Resilience & Failover Validation

Chaos-engineered tests evaluating how systems behave when third-party APIs fail, cache nodes restart, or networks partition.

Circuit BreakersGraceful DegradationFallback Caching
Continuous Performance CI Budgets
Governance

Continuous Performance CI Budgets

Enforce automated bundle size thresholds, latency regression gates, and Lighthouse scores directly within CI/CD pipelines.

Bundle BudgetsAutomated PR GatesTelemetry Dashboards

RELEASE GOVERNANCE

Before you test performance

Four critical capacity and architecture questions to establish meaningful benchmarks.

Base load tests on real user behaviour rather than arbitrary numbers.Active Focus

What is our realistic target concurrency and throughput model?

Testing 100,000 simple ping requests does not reflect real load. A meaningful model calculates actual active sessions, realistic think times (e.g. 5–15 seconds between clicks), read-to-write ratios, and critical checkout conversion paths.

Evaluation Criteria:
Peak vs. normal traffic multipliersRead vs. write transaction ratiosSession duration distributionsThird-party API throttling limits
Avoid running load tests against under-provisioned mock environments.Inspect

Is the test environment truly representative of production?

Testing against a small single-core test database gives completely misleading data. The performance environment must mirror production database sizing, network topologies, caching layers, and connection pooling configurations.

Evaluation Criteria:
Database dataset scale (realistic row counts)Cache instance sizingNetwork latency between tiersCDN presence and cache hit rates
Reject average response times as a measure of user health.Inspect

What are our acceptable latency percentiles (p95 and p99)?

A system with an average latency of 200ms can still have 5% of its users waiting 8 seconds. We define performance success by tail latency: p95 under 300ms, p99 under 800ms, and zero 5xx error responses under target load.

Evaluation Criteria:
Service Level Objectives (SLOs)Tail latency impact on conversionTimeout thresholds across tiersError rate ceilings (e.g. < 0.1%)
Identify the weakest architectural link before traffic strikes.Inspect

Where will the application fail first when traffic exceeds limits?

Every system has a bottleneck: database connection pools, memory leaks, Redis bandwidth, or third-party webhooks. We test until the system breaks to document the exact breaking point and implement graceful degradation.

Evaluation Criteria:
Database connection limitsWorker queue depth capacityMemory exhaustion pointsAuto-scaling reaction lag

DELIVERY LIFECYCLE

How We Validate Performance

A scientific, reproducible approach to stress testing and performance tuning.

Traffic Modeling & Baseline Audits

We analyze current analytics, define peak concurrency scenarios, and establish baseline performance benchmarks.

Deliverable:Workload Profile & Objectives

Load Script Authoring & Test Bed

We author distributed k6 test scripts with realistic authentication, data variations, and monitoring probes.

Deliverable:Automated k6 Test Suites

Execution, Profiling & Bottleneck Isolation

We execute load, stress, and soak tests while capturing real-time CPU, memory, database query, and network telemetry.

Deliverable:Full Telemetry & Bottleneck Report

Tuning, Retesting & CI Budgets

We collaborate with engineers to apply database indexes, caching, and pooling fixes, then re-test and lock in CI performance budgets.

Deliverable:Tuned Baseline & CI Performance Gates
COMMERCIAL VALUE

Why Performance Matters to Product Success

Every 100ms of latency reduction directly improves conversion, user retention, and search engine visibility.

Direct Conversion Rate Impact

Faster applications convert significantly more users. Eliminating checkout lag and sluggish form submissions preserves immediate revenue.

Revenue Growth

Infrastructure Cost Optimization

Optimized queries and efficient caching allow you to handle 5x the traffic on your existing cloud footprint without over-provisioning.

Cloud Efficiency

Guaranteed Launch Day Stability

Eliminate the fear of high-stakes marketing campaigns, product launches, or press coverage crashing your servers when it matters most.

Reputational Safety

ENGINEERING ADVANTAGE

Why AKREVON for Performance Testing

Full-stack performance engineers who understand database internals, network protocols, and frontend runtimes.

Scientific Workload Modeling

We reject blunt HTTP flood tests, authoring realistic user journeys that model actual database reads, writes, and session states.

Production Verified

Actionable Root-Cause Guidance

We do not just hand over a graph showing that an endpoint is slow; we inspect slow queries, explain plans, and recommend exact fixes.

Production Verified

Continuous Performance Culture

We help teams institute permanent automated performance budgets so speed remains a non-negotiable standard as the product evolves.

Production Verified
FREQUENTLY ASKED

Performance Testing answers

We primarily use k6 for its scriptability in TypeScript/JavaScript, distributed execution capabilities, and modern CI integration. We also leverage Locust, Lighthouse, Datadog, Grafana, and database-native query profilers.
QUALITY & DELIVERY

Ready to engineer rock-solid quality into your release?

Partner with AKREVON to build comprehensive automated test suites, stress-test performance boundaries, and deploy zero-defect release pipelines.