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Kong Gateway 2.0 RC Released at Kong Summit 2019

Kong Gateway 2.0 takes all the work we put into creating Kong and builds on it to create a truly stable, battle-tested API gateway that is stable under extreme performance conditions and a diverse array of architectures and implementations. With this release, we are especially excited to address some of the most common requests from our community, as well as lay the foundation for continued growth and innovation within our open source platform. Read on below to check out the new capabilities.

Kong Acquires Insomnia - Expanding Our Offering With Advanced API Testing

Today we are excited to welcome Insomnia to the team at Kong! Insomnia has been on our radar at Kong for quite some time. I was first introduced to Insomnia and Greg in the early days of the project, and I am proud to have been one of the earliest supporters. From the first, I knew that Insomnia was solving a real problem for developers, and it has been amazing to see the community of contributors grow as more and more developers adopt Insomnia to make testing and debugging APIs easier.

Announcing Kong Studio: Design and Testing Built For Microservices

Today we’re thrilled to announce a brand new product area for Kong – Kong Studio, an integrated design and test environment for Kong Enterprise customers. We are excited to make the leap in extending our service control platform to include pre-production use cases focused on improving the way that customers build and test their microservices and APIs.

Kong Ingress Controller 0.6 Released with Support for Admission Controller, Istio, and Kuma

We are thrilled to announce the release of Kong Ingress Controller 0.6! This release builds on the previous releases and unlocks integrations and features, including the Admission Controller, integration with Kuma and Istio, and support for Kustomize native configuration management.

5 Best Practices for Securing Microservices at Scale

As outlined in a previous article on security challenges for microservices, DevOps are getting more widely distributed, spread thin, and forced to plan for higher levels of interactivity as well as evolving national security “backdoor” measures. Microservices, born from a still-emerging DevOps laboratory environment, can be deployed anywhere: on-prem, in the public cloud, or a hybrid implementation.

Introducing Kuma: The Universal Service Mesh

We are excited to announce the release of a new open source project, Kuma – a modern, universal control plane for service mesh! Kuma is based on Envoy, a powerful proxy designed for cloud native applications. Envoy has become the de-facto industry sidecar proxy, with service mesh becoming an important implementation in the cloud native ecosystem as monitoring, security and reliability become increasingly important for microservice applications at scale.

Kong and Istio: Setting up Service Mesh on Kubernetes with Kiali for Observability

Service mesh is redefining the way we think about security, reliability, and observability when it comes to service-to-service communication. In a previous blog post about service mesh, we took a deep dive into our definition of this new pattern for inter-service communication. Today, we’re going to take you through how to use Istio, an open source cloud native service mesh for connecting and securing east-west traffic.

Kong 1.3 Released! Native gRPC Proxying, Upstream Mutual TLS Authentication, and Much More

Today, we are excited to announce the release of Kong 1.3! Our engineering team and awesome community has contributed numerous features and improvements to this release. Based on the success of the 1.2 release, Kong 1.3 is the first version of Kong that natively supports gRPC proxying, upstream mutual TLS authentication, along with a bunch of new features and performance improvements.

Building Metrics Pipeline for High-Performance Data Collection

This is part three in a series discussing the metrics pipeline powering Kong Cloud. In previous posts in this series, we’ve discussed how Kong Cloud collects, ships, and stores high volumes of metrics and time-series data. We’ve described the difference between push and pull models of collecting metrics data, and looked at the benefits and drawbacks of each from a manageability and performance perspective.