Systems | Development | Analytics | API | Testing

Kong Mesh 2.12: SPIFFE/SPIRE Support and Consistent XDS Resource Names

We're very excited to announce Kong Mesh 2.12 to the world! Kong Mesh 2.12 delivers two very important features: SPIFFE / SPIRE support, which provides enterprise-class workload identity and trust models for your mesh, as well as a consistent Kuma Resource Identifier (KRI) naming convention for resources in the Mesh. Read on to learn more!

Building a First-Class Kubernetes Experience in Kong Konnect

This is the second post in a series about reasons to attend API Summit 2025. Check out the previous post here. To unlock Kubernetes’ full potential, many enterprises are relying on three key building blocks available in Kong Konnect today: Together, these components extend Kubernetes from being just a container orchestration platform. They lay the foundation for Kubernetes to support the exposure, governance, and operation of APIs — and the AI workflows that increasingly rely on those APIs.

Data Streaming: The Key to Tackling Data Challenges for AI Success

As artificial intelligence (AI) matures from experimentation into production use cases, the symbiotic relationship between data and AI becomes increasingly clear. To deliver real business impact—smarter automation, better customer experiences, and massive cost takeout—AI use cases are only as powerful as the data they’re running on.

Powering Event-Driven, Multi-Agent AI: Confluent Named MongoDB Global Tech Partner of the Year

We’re proud to announce that Confluent has been named MongoDB’s 2025 Global Tech Partner of the Year. This award highlights the strength of our partnership and joint go-to-market execution, helping enterprises build the next generation of intelligent, event-driven artificial intelligence (AI) applications.

Rapid Data Creation using AI | Komal Chowdhary | Virtual Meetup

Managing Test Data is one of the most challenging aspects of testing and software development, especially as systems grow in complexity n require intricate, context specific data.....The need to generate, anonymize, and transform data at scale often consumes significant testing time and resources, which in turn impacts efficiency. However, without relevant test data, we cannot effectively trigger actions or observe system behaviors. LLM offer a powerful solution to this challenge by generating diverse and complex data structures on demand.

How Ephemeral Test Environments Solve DevOps' Biggest Challenge

Ephemeral test environments have surfaced as a solution to DevOps teams’ growing challenges. Dealing with spiraling cloud costs and infrastructure maintenance is only getting more complex. Development teams find themselves competing for limited or stale environments while datasets grow larger. As a result, development velocity suffers. Application teams need realistic data for effective testing.