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Load Testing Blog

Welcome to OctoPerf's blog, where we embark on a journey through the dynamic landscape of load testing, a crucial aspect of ensuring your applications thrive in the face of user demands.

Whether you're a seasoned performance engineer or just diving into the world of load testing, our blog is your go-to resource for insights, best practices, and cutting-edge tools.

Categories Highlights

  • JMeter Load-testing

    Load-testing using the n°1 open-source solution: Apache JMeter™.

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  • Real-Browser Load-testing

    Load-testing using real-browser solutions like Playwright and Selenimum Web-Driver.

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  • Load-testing Methodology

    Load-testing methodology and tools comparison.

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  • OctoPerf's releases

    Stay tuned on the JMeter™ Performance Center latest releases: OctoPerf!

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Start Your Load Testing Journey Today

Whether you're a developer aiming to optimize your code, a QA engineer ensuring application resilience, or a business owner safeguarding user satisfaction, load testing is your ally. Join us on this expedition into the heart of performance excellence.

Are you ready to transform your understanding of load testing? Let the journey begin!

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Latest Blog Posts

Playwright Virtual Users: Load Testing What Real Browsers Actually See

This is the third post in our "Features Sitting Idle" series, where we shine a light on OctoPerf features that are already in your account but rarely used to their full potential.

Features Sitting Idle - Playwright VUs

"Your Application Loads Content Through JavaScript. Your Load Test Doesn't See It."

This is a blind spot many teams discover too late. Tests pass, metrics look fine, yet real users report slowness or errors after a release.

The root cause is almost always the same: the load test was built against the HTTP protocol layer, but the user pain happens in the browser, above that layer.

Designing a Token-Efficient MCP Server: the OctoPerf Approach

In the first two articles of this series we showed what the OctoPerf MCP Server does. This one is for the builders: how we designed it, and specifically how we kept its token cost under control.

Because here is the thing nobody tells you when you start writing a Model Context Protocol server: the hard part is not exposing your API to an LLM. The hard part is not exposing too much of it. Every byte a tool returns lands in the model's context window, where it costs money, adds latency, and dilutes the model's attention. A server that naively mirrors a REST API produces an agent that is expensive, slow, and confused.

This article walks through the five patterns we applied to avoid that fate. None of them is specific to load testing: if you are building an MCP server for your own product, they should transfer directly.

OctoPerf vs BlazeMeter: Which JMeter-based Performance Testing Platform Should You Choose in 2026?

When evaluating Apache JMeter based enterprise-grade performance testing platforms, two names frequently appear on the shortlist: OctoPerf and BlazeMeter.

Both platforms help engineering teams execute large-scale load tests, validate application scalability and integrate performance testing into modern CI/CD pipelines. Both support Apache JMeter and both provide cloud-based infrastructure capable of generating traffic from multiple geographic regions.

At first glance, they seem very similar. In reality, they take very different approaches to performance engineering.

BlazeMeter has evolved into a broader continuous testing platform covering multiple testing disciplines. OctoPerf has remained focused on one mission: making performance testing accessible, scalable and efficient for organizations of all sizes.

As a result, the most significant differences between the two platforms are not necessarily found in their ability to execute load tests, but rather in areas such as licensing flexibility, analytics depth, deployment options and the growing role of AI in performance engineering workflows.

Table of Contents

OctoPerf MCP Server in Action: Browser Probes, Scheduled Runs and Smarter Reports

In our previous article we introduced the OctoPerf MCP Server and followed three skills (validation triage, auto-correlation, scenario diagnosis) through a complete workflow: from a raw HAR recording to a diagnosed 500-user load test.

This second part covers the remaining skills, and they take the story further: measuring what real users perceive during the load with a Playwright browser probe, turning the test into a recurring schedule, and letting the agent read the resulting reports widget by widget, trends included. Same format as before: the actual conversation between a user and the LLM, then the matching result in OctoPerf.

Introducing the OctoPerf MCP Server: Load Testing from Your AI Assistant

We are excited to announce the official release of the OctoPerf MCP Server. Built on the open Model Context Protocol, it lets any AI agent (Claude.ai, Claude Code, Codex, Gemini CLI, GitHub Copilot and more) drive your OctoPerf account directly: import Virtual Users, fix replay errors, run scenarios and read back metrics, all in plain language, without leaving the chat.

In this article we first present the server globally: what it exposes, how authentication works, and how to connect your favorite client in minutes. We then follow a complete, realistic workflow through three of the bundled skills: validation triage, auto-correlation and scenario diagnosis, showing the actual conversation between a user and the LLM, and the matching result in OctoPerf.