Systems | Development | Analytics | API | Testing

Today, we’re announcing that Kong has acquired OpenMeter, the open source and SaaS leader for real-time usage metering and billing. OpenMeter’s capabilities will be integrated into Kong Konnect, enabling usage-based pricing, entitlements, and invoicing for APIs, events, and AI workloads. This is a huge milestone for Kong, and we’re excited about what this means for our customers and the future of how you build and scale revenue-generating digital products for the agentic AI era.

Most of the times teams spend their time testing that features work correctly under normal conditions. But do you know what happens when the database goes down,the network goes slow or a third party service stops responding? In this case, resilience testing solves this problem by intentionally breaking parts of your system to see how it responds to it. Instead of only testing perfect scenarios, you find real-world failures like server crashes, network issues and service timeouts.

The healthcare industry is buzzing about FHIR (Fast Healthcare Interoperability Resources). Pronounced “fire,” this widely adopted data standard has been revolutionizing how healthcare information is exchanged. But building a truly modern, secure, and scalable digital health platform takes more than just a data standard. Here comes SMART on FHIR, a framework that builds on FHIR to create a universal, secure, and user-friendly way for applications to access electronic health records(EHRs).

As QA teams scale, getting the right data at the right time becomes critical. Yet too often, it’s buried in folders, slowed by clunky filters, or locked behind SQL queries causing wasted time, delayed cycles, and inconsistent reporting. But moving as fast as we are today, you can’t wait on reports or guess where a test case lives. You need answers now. With QMetry’s Smart AI Search and AI-powered SQL generation, QA teams get instant access to data in natural language. No hunting.

Integrating AI into enterprise systems is a high-wire act: you must deliver value quickly—without breaking security, compliance, or scalability. This guide distills security-first patterns CISOs can operationalize immediately: zero-trust for every AI interaction, least-privilege RBAC, end-to-end encryption and secret management, auditable-by-default pipelines, and a platform approach that minimizes custom code and speeds delivery. Bottom line: Treat AI like any external, untrusted client.

APIs have become the backbone of modern software architecture—but building and securing them is often harder than it should be. Developers today are expected to expose dozens of data sources as clean, secure, and compliant APIs—fast. From managing authentication to documenting endpoints and enforcing governance, the traditional approach to API integration comes with friction, delay, and technical debt.

So we have all been hearing a lot about AI lately. Everyone is talking about ChatGPT, OpenAI, Claude, image generation, and now there is a new trend about “Agentic AI.“ I know it is getting confusing with all these fancy terms flying around us. Let me break it down for you in simple terms. You can think of this as your friendly guide to understanding the two biggest players in the AI game right now!

Continuous integration and continuous delivery/deployment (CI/CD) in API management refers to the automation of building, testing, publishing, and promoting APIs across environments (development, staging, and production) using CI/CD practices. It brings DevOps principles to the API lifecycle, enabling faster and more reliable API releases.

Behind every smooth user experience is a maze of APIs quietly handling requests, responses, and data flows. This makes APIs critical connectors that enable applications to communicate and share data seamlessly. When these vital conduits fail, the consequences can be severe—system outages, data breaches, and frustrated users. API testing is the unsung hero ensuring your digital world runs smoothly and securely.

Artificial intelligence (AI) is widely used today, from voice assistants to Netflix recommendations, but AI models do not always behave as intended. Testing an app before it is released is standard practice, and similarly, AI models should be thoroughly tested. Testing an AI model can verify that the model’s decisions are accurate, fair, and safe.