LoadBooster vs Gatling.io

Struggling to choose between LoadBooster and Gatling.io? Both products offer unique advantages, making it a tough decision.

LoadBooster is a Development solution with tags like load-testing, performance-testing, web-application-testing.

It boasts features such as Simulate large numbers of concurrent users, Measure application performance under load, Identify bottlenecks and performance issues, Conduct capacity planning and benchmarking, Supports various protocols and technologies, Detailed reporting and analytics, Scriptable and customizable tests and pros including Comprehensive load testing capabilities, Easy to use and set up, Detailed performance metrics and insights, Supports a wide range of web technologies, Scalable to handle large user loads.

On the other hand, Gatling.io is a Development product tagged with load-testing, performance-testing, scalability-testing.

Its standout features include Record and playback - Record user actions and replay them to simulate load, Advanced simulation engine - Flexible scenario definition using Scala based DSL, Multiple protocols - Supports HTTP, WebSocket, JMS and more, Assertions and validations - Validate response content, status codes, timings etc, Interactive HTML reports - Detailed metrics on response time, throughput, failures etc, CLI and Maven plugin - Can integrate with CI/CD pipelines, Cloud scale testing - Integrates with Kubernetes for large scale load tests, and it shines with pros like Open source and free to use, Powerful Scala based DSL for flexible test scenarios, Good documentation and active community support, Integrates well with CI/CD pipelines, Detailed HTML reports for analysis.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

LoadBooster

LoadBooster

LoadBooster is a load and performance testing tool for web applications. It allows users to simulate large numbers of concurrent users and see how their application performs under load. Useful for capacity planning, benchmarking, and finding bottlenecks.

Categories:
load-testing performance-testing web-application-testing

LoadBooster Features

  1. Simulate large numbers of concurrent users
  2. Measure application performance under load
  3. Identify bottlenecks and performance issues
  4. Conduct capacity planning and benchmarking
  5. Supports various protocols and technologies
  6. Detailed reporting and analytics
  7. Scriptable and customizable tests

Pricing

  • Freemium
  • Subscription-Based

Pros

Comprehensive load testing capabilities

Easy to use and set up

Detailed performance metrics and insights

Supports a wide range of web technologies

Scalable to handle large user loads

Cons

Limited free version with restricted features

Steep learning curve for advanced use cases

Can be resource-intensive for large-scale testing


Gatling.io

Gatling.io

Gatling.io is an open-source load and performance testing framework based on Scala, Akka and Netty. It allows users to simulate load on a system and analyze overall performance under various user loads. Gatling is used for testing APIs, microservices and web applications.

Categories:
load-testing performance-testing scalability-testing

Gatling.io Features

  1. Record and playback - Record user actions and replay them to simulate load
  2. Advanced simulation engine - Flexible scenario definition using Scala based DSL
  3. Multiple protocols - Supports HTTP, WebSocket, JMS and more
  4. Assertions and validations - Validate response content, status codes, timings etc
  5. Interactive HTML reports - Detailed metrics on response time, throughput, failures etc
  6. CLI and Maven plugin - Can integrate with CI/CD pipelines
  7. Cloud scale testing - Integrates with Kubernetes for large scale load tests

Pricing

  • Open Source

Pros

Open source and free to use

Powerful Scala based DSL for flexible test scenarios

Good documentation and active community support

Integrates well with CI/CD pipelines

Detailed HTML reports for analysis

Cons

Steep learning curve for Scala DSL

Limited debugging capabilities compared to commercial tools

Lacks some enterprise features like SLA reporting

Not ideal for non-technical users