Systems | Development | Analytics | API | Testing

The Model Context Protocol (MCP) has pioneered a new interface layer between AI agents and tools. It has become easier to enable seamless access to external services, APIs, workflows, and data with natural language. MCP servers are now powering the decentralization of AI intelligence and orchestrating the interplay among modern AI systems. In doing so, they also introduce a more open, fluid, and automation-driven attack surface. However, traditional API security models weren’t built for this.

WSO2 Kubernetes Gateway provides a robust, Kubernetes-native platform for managing APIs. It’s purpose-built for cloud-native teams requiring fine-grained control over APIs in modern, distributed environments. With support for microservices architecture, secure ingress, and service discovery, Kubernetes Gateway solves the infrastructure side of the API equation.

In this episode, Emmanuel Paraskakis, CEO Level 250 breaks down the core responsibilities of an API product manager. Speaking from his experience in product management for over fifteen years, Emmanuel distinguishes an API product manager’s focus from conventional product roles, underscoring their critical importance in building scalable digital platforms.

NGINX One provides a modern solution for enterprises to manage infrastructure at scale across globally distributed systems. The platform has built-in tools for essential performance and uptime metrics, giving DevOps teams visibility into the health of their NGINX instances. But for effective API observability and analytics, you have to go beyond infrastructure metrics.

Most engineering teams have infrastructure monitoring nailed down—they are tracking uptime, latency, and error rates, and have set up alerting in places. But API issues don’t always start there. Infrastructure metrics don’t tell you how your API users experience your API. A critical integration may have been repeatedly facing failures due to invalid authentication tokens. A new version you have deployed might have introduced a subtle schema change that breaks older clients.

Usage-based pricing has long been the default for APIs—straightforward to implement and easy for customers to understand. You charge based on consumption: API calls, compute time, or data volume. It is predictable, measurable, and scales well with usage. But as APIs become more intelligent—especially in AI-driven platforms—raw consumption no longer remains a reliable proxy for customer value. A user can rack up thousands of API calls and still achieve nothing meaningful.

NGINX with OpenResty offers unmatched performance for serving APIs (application programming interfaces) at scale, with the added benefits of the open-source ecosystem. It’s fast, flexible, and production-proven—an ideal choice for scalable web platforms and high-throughput APIs. But even the most reliable platform can leave teams blind to what matters: real-time API usage, user behavior, and production errors.

Enterprise organizations rarely operate with a single API gateway. As business units adopt technologies independently, it is common to find, for example, Kong in one domain, AWS API Gateway in another, and additional platforms elsewhere. This flexibility drives velocity—but it also fragments visibility. Without a unified view of API activity, enterprise teams face inconsistencies in reporting, gaps in customer insights, and difficulty enforcing governance policies.

The Model Context Protocol (MCP) has taken the internet by storm by rapidly becoming the standard for Large Language Models (LLMs) to communicate with external data sources or tools. MCP provides a structured way to fetch data and trigger workflows through APIs and functions. However, with great power comes great responsibility.