Systems | Development | Analytics | API | Testing

The Apache Kafka community is proud to announce the release of Apache Kafka 4.1.0. This blog post highlights the many new features and improvements included in this release. For a full list of changes, be sure to check the release notes. Queues for Kafka (KIP-932) is now in preview. It's still not ready for production, but you can start evaluating and testing it. See the preview release notes for more details. This release also introduces a new Streams Rebalance Protocol (KIP-1071) in early access.

In today’s cost-conscious climate, every line item on your cloud bill is under the microscope. That makes now the ideal time to rethink your data streaming strategy. For many teams, using a hyperscaler-hosted Apache Kafka service feels like the easy choice—one vendor, one bill, no additional contracts, and minimal setup. It makes sense early on, especially for small-scale projects or basic use cases. But, as your Kafka usage grows and becomes mission-critical, that simplicity comes at a cost.

APIs are the foundation of modern software ecosystems—connecting applications, services, and databases so information can flow securely and efficiently. But as systems become more complex and businesses demand faster innovation, traditional, manual approaches to API management no longer scale. That’s where AI-powered API gateways come in.

Be “Day 0 Ready” with our Pixel 10 Series Testing — eliminate coverage gaps and stay ahead of your users.

In the software development lifecycle (SDLC), deployment is the final step, the one where your app is delivered to users. Traditionally, this has meant installing software on the customer’s premises or hosting it in-house. But this approach comes with significant overhead: With the rise of cloud computing, Software as a Service(SaaS) and Platform as a Service(PaaS), these manual steps are quickly being replaced.

The rise of Model Context Protocol (MCP) has given AI agents and large language models (LLMs) a standardized way to talk to external tools, APIs, and data sources. In theory, it solves the messy integrations and custom connectors that have slowed down real-world agent adoption. A clean protocol should mean smooth interoperability. However, we’re observing certain patterns of fragmentation. Each MCP server runs in isolation. Agents have to handle multiple connections.

Agentic AI is transforming how businesses operate by enabling systems to handle complex tasks autonomously. Instead of relying on constant human input, these AI systems break down high-level goals into smaller tasks, make decisions independently, and improve continuously through feedback. Here's what you need to know: Key Features: Autonomously manage workflows and processes. Handle multi-step decision-making and problem-solving. Learn and adapt based on performance data.

You didn’t miss a requirement. You just built a strategy for the wrong kind of system.

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.