What metrics would you use to evaluate Kong's Ingress Controller?

Introduction

Evaluating Kong's Ingress Controller requires a comprehensive approach to product success metrics. To address this challenge effectively, I'll follow a structured framework that covers core metrics, supporting indicators, and risk factors while considering all key stakeholders. This approach will help us gain a holistic view of the Ingress Controller's performance and impact.

Step 1

Product Context

Kong's Ingress Controller is a critical component in Kubernetes environments, managing external access to services within a cluster. It acts as a traffic manager, routing incoming requests to the appropriate services based on defined rules.

Key stakeholders include:

1. DevOps teams: Seeking efficient traffic management and easy configuration
2. Application developers: Requiring seamless service exposure and routing
3. Security teams: Ensuring proper access control and traffic encryption
4. Business leaders: Expecting improved application performance and reduced operational costs

User flow:

1. DevOps engineers deploy the Ingress Controller in their Kubernetes cluster
2. They configure routing rules and policies using Kubernetes manifests
3. The Ingress Controller intercepts incoming traffic and routes it based on these rules
4. Application developers expose their services through the Ingress, without worrying about underlying network complexities

Kong's Ingress Controller fits into the company's broader API management strategy, extending Kong's capabilities into Kubernetes environments. Compared to competitors like NGINX Ingress Controller, Kong offers more advanced features like plugin support and native API gateway integration.

Product Lifecycle Stage: Growth - Kong's Ingress Controller is gaining traction in the market, with increasing adoption among Kubernetes users seeking advanced traffic management capabilities.

Software-specific context:

• Platform: Kubernetes
• Integration points: Kubernetes API server, Kong API Gateway
• Deployment model: Containerized, typically deployed as a Kubernetes deployment