Newest first. 313 load testing, JMeter, test automation and monitoring guides, plus tool comparisons. Browse by topic from the blog home.
Compare JMeter, k6, Gatling, Locust, Artillery and LoadFocus for CI/CD: how each runs headless, fails a build on a threshold and scales beyond one runner.
Explore a detailed comparison of cloud testing vs local testing for mobile performance testing. Learn which approach fits your needs in 2026.
Optimize mobile API performance: shrink payloads, paginate, cache, compress, cut chatty calls, refresh tokens safely and test on slow networks.
Endurance testing and soak testing are the same practice: normal load held for hours. What it catches, how long to run it, and how to read the trend.
Spike testing applies traffic suddenly instead of ramping. Why autoscaling and cold caches fail in that window, and why recovery is half the test.
Volume testing increases the data a system holds rather than the user count. What it finds, how to run one, and how to read the curve it produces.
A practical comparison of 10 API monitoring tools, what each is genuinely good at, which have real free tiers, and the open source options worth running.
Microservices load testing explained: service-level versus end-to-end tests, dependencies and mocks, tracing, autoscaling and circuit breaker behaviour.
Cloud vs on-premise website monitoring: what each can see, where private networks and data rules matter, and when running both is the sensible answer.
Edge computing changes what a load test measures: mostly the CDN cache, not your origin. Edge function limits, how to test them, and the CDN load testing rules.
Continuous performance testing runs load tests on every change. What to run per pull request, nightly and before release, and how to fail a build on thresholds.
A practical plan for SaaS load testing: set targets from projected growth, model tenants, test regions, gate releases in CI and read the results.
OAuth2 makes load testing harder because tokens expire mid-test and refresh adds its own latency.
Open source vs commercial load testing tools compared on cost, scaling, CI/CD, support and team skills, with vendor prices read off their pages in October 2026.
Monitor an MCP server over Streamable HTTP: send a real JSON-RPC tools/list check, assert status, body and latency, and alert on auth and version errors.
Data-driven load testing gives each virtual user different data and paths. How to pick data, keep rows unique per user, model journeys and avoid mistakes.
What AI actually does in performance testing today: script drafting, result summaries and where it stops, with tools checked against their own docs.
Judge a platform on CI integration and result analysis, not on maximum virtual users.
A scheduled test only helps if it alerts, since nobody reads a passing report. Match frequency to deploy cadence rather than running nightly by default.
Mix journeys by real proportion rather than testing each endpoint evenly. Vary data per user, or caching makes the system look faster than it is.
Turn a cURL command, HAR file, Postman collection or OpenAPI spec into a runnable k6 script or JMeter test plan, free in your browser, then run it in the cloud.
The main cloud testing challenges: environments unlike production, risky test data, noisy shared hardware, third-party services and cost, with a fix for each.
Three ways to run k6 at scale: self-hosted k6 with the k6 Operator, Grafana Cloud k6, or a managed platform, with the cost and effort of each.
The six problems that most often undermine API performance tests (dependencies, test data, auth, load model, metrics, CI) and a concrete fix for each.
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