The top 5 launches of 2021 (so far)The top 5 launches of 2021 (so far)Developer Advocate

Some key features:

  • GKE Autopilot manages node and node pools.
  • Autopilot clusters are preconfigured with an optimized cluster configuration.
  • Autopilot comes with an SLA that covers both the control plane and your pods. 

Where does it fit into our product strategy?

A question I frequently hear is, “Is this a serverless version of GKE?” A better way to think of Autopilot is that it’s “nodeless”. GKE is the industry’s first fully managed Kubernetes service that implements the full Kubernetes API. GKE Autopilot clusters offer the benefits of a serverless offering with full access to the Kubernetes API. Those who use Kubernetes are using it as a layer on top of IaaS to build their own application platforms. That doesn’t mean, however, it’s not meant for developers. On the contrary, it’s meant for developers who want an easier onboarding to Kubernetes. 

Where does it fit into the broader industry?

Another question I often get is, “How does this differ from AWS Fargate?” GKE Autopilot is similar to Fargate for EKS with one major difference: Autopilot supports almost the entire Kubernetes API, including DaemonSets, jobs, CRDs, and admission controllers. One of the standout differences is that you can attach block storage to Autopilot (i.e. HDDs). Autopilot is still Kubernetes and designed that way from the ground up. Our goal from the outset was that Autopilot is GKE, and not a forked or separate product. This means that many of the improvements we make to autoscaling in GKE Autopilot will be shared back to GKE Standard and vice versa. In Autopilot, we’ve combined GKE automation and scaling and lots of great community enhancements.

For developers running on GKE, nothing really changes. For developers interested in starting on Kubernetes, I have yet to see an offering like GKE Autopilot. With Autopilot, you still get the benefits of Kubernetes, but without all of the routine management and maintenance. That’s a trend I’ve been seeing as the Kubernetes ecosystem has evolved. Few companies, after all, see the ability to effectively manage Kubernetes as their real competitive differentiator.

Where can you get started?

Start here:

Get hands-on:

3. Tau VMs

Tau VMs were announced just this June. Tau VMs are a new Compute Engine family, optimized for cost-effective performance of scale-out workloads. 

T2D, the first instance type in the Tau VM family, is based on 3rd Gen AMD EPYCTM processors and leapfrogs the available VMs for scale-out workloads from any leading public cloud provider , both in terms of performance and workload total cost of ownership. 

Our Compute Engine offerings (like our general purpose, compute optimized, memory optimized, and accelerator optimized VMs) already cover a broad range of workload requirements from dev/test to enterprise apps, HPC, and large in-memory databases. There is, however, still a need for compute that supports scale-out enterprise workloads, including media transcoding, Java-based applications, containerized workloads, and web servers. Developers want focused VM features without breaking the bank or sacrificing their productivity. The purpose of our Tau VM family is to provide an intermediate path to the cloud that gives you those features.

Some key features:

  •  T2D VMs will come in predefined VM shapes, with up to 60 vCPUs per VM, and 4 GB of memory per vCPU, and offer up to 32 Gbps networking.
  • The AMD EPYC processor-based VMs also preserve x86 compatibility.
  • AMD EPYC processors are built using the Zen 3 architecture, which reduces communication penalties during scale out.
  • You can add T2D nodes to your GKE clusters.

Where does it fit into our product strategy?

We launched Tau VMs to complement our general purpose VMs and provide what enterprise data centers have always aimed for: the best performance for enterprise workloads at the best price. With T2D, we saw 56% better raw performance over key competitors.