Best IT Security Software for F5 NGINX Ingress Controller

Find and compare the best IT Security software for F5 NGINX Ingress Controller in 2026

Use the comparison tool below to compare the top IT Security software for F5 NGINX Ingress Controller on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    NGINX Reviews
    NGINX Open Source is the web server that supports over 400 million websites globally. Built upon this foundation, NGINX Plus serves as a comprehensive software load balancer, web server, and content caching solution. By opting for NGINX Plus instead of traditional hardware load balancers, organizations can unlock innovative possibilities without being limited by their infrastructure, achieving cost savings of over 80% while maintaining high performance and functionality. It can be deployed in a variety of environments, including public and private clouds, bare metal, virtual machines, and container setups. Additionally, the integrated NGINX Plus API simplifies the execution of routine tasks, enhancing operational efficiency. For today's NetOps and DevOps teams, there is a pressing need for a self-service, API-driven platform that seamlessly integrates with CI/CD workflows, facilitating faster app deployments regardless of whether the application utilizes a hybrid or microservices architecture, which ultimately streamlines the management of the application lifecycle. In a rapidly evolving technological landscape, NGINX Plus stands out as a vital tool for maximizing agility and optimizing resource utilization.
  • 2
    open-appsec Reviews
    open-appsec is an open-source initiative that builds on machine learning to provide pre-emptive web app & API threat protection against OWASP-Top-10 and zero-day attacks. It can be deployed as add-on to Kubernetes Ingress, NGINX, Envoy and API Gateways. The open-appsec engine learns how users normally interact with your web application. It then uses this information to automatically detect requests that fall outside of normal operations, and sends those requests for further analysis to decide whether the request is malicious or not. open-appsec uses two machine learning models: 1. A supervised model that was trained offline based on millions of requests, both malicious and benign. 2. An unsupervised model that is being built in real time in the protected environment. This model uses traffic patterns specific to the environment. open-oppsec simplifies maintenance as there is no threat signature upkeep and exception handling, like common in many WAF solutions.
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