Best Load Balancing Software for GitLab

Find and compare the best Load Balancing software for GitLab in 2025

Use the comparison tool below to compare the top Load Balancing software for GitLab on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Google Cloud Platform Reviews
    Top Pick

    Google Cloud Platform

    Google

    Free ($300 in free credits)
    56,321 Ratings
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    Google Cloud Platform offers advanced load balancing solutions that efficiently allocate traffic across various resources, ensuring optimal performance and high availability. With features like Google Cloud Load Balancing, applications can automatically scale to accommodate significant traffic demands. GCP's global load balancing capabilities allow organizations to provide seamless and rapid user experiences, even during periods of high demand. New users can take advantage of $300 in free credits, enabling them to test, run, and deploy workloads while assessing the platform's load balancing functionalities in a budget-friendly way. Additionally, GCP's intelligent routing capabilities prioritize traffic to the nearest available resources, minimizing latency and enhancing overall performance. These solutions empower businesses to effectively manage sudden traffic increases without sacrificing the quality of user interactions.
  • 2
    NeoLoad Reviews
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    Software for continuous performance testing to automate API load and application testing. For complex applications, you can design code-free performance tests. Script performance tests in automated pipelines for API test. You can design, maintain, and run performance tests in code. Then analyze the results within continuous integration pipelines with pre-packaged plugins for CI/CD tools or the NeoLoad API. You can quickly create test scripts for large, complex applications with a graphical user interface. This allows you to skip the tedious task of manually coding new or updated tests. SLAs can be defined based on the built-in monitoring metrics. To determine the app's performance, put pressure on it and compare SLAs with server-level statistics. Automate pass/fail triggers using SLAs. Contributes to root cause analysis. Automatic test script updates make it easier to update test scripts. For easy maintenance, update only the affected part of the test and re-use any remaining.
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