Release notes

Have you ever experienced Spot instance drought, when instances you need are temporarily not available and so your workloads become unschedulable? The Spot fallback feature guarantees capacity by temporarily moving impacted workloads onto on-demand nodes. After a period of time, CAST AI will check for Spot availability and move the workloads back to spot instances. This feature is available on the Policies page under the Spot instance section and supports EKS, Kops, and GKE clusters.

Added support for private kOps clusters that do not have K8s API IP exposed to the internet. CAST AI agent now supports “call-home mechanism” for private IP K8s clusters.

Node list went through a major upgrade and now contains much more detailed information about individual nodes in the cluster.

Autoscaler can now be instructed to scale the cluster with instances that have locally attached SSD disks, when the storage-optimized label is used in a workload spec. For details, please refer to the documentation.

Minor improvements to UI and bug fixes.

We have launched a new feature that we call Rebalancer. It allows users to automatically migrate clusters from the current state to the most optimal configuration. The migration is performed via three distinct phases: 1) during the preparation, the user can inspect all impacted workloads; 2) later the user gets a migration plan so they understand what nodes will be added & removed and what cost impact can be expected; 3) lastly - the migration plan is executed by adding new nodes, migrating workloads and deleting obsolete nodes.

The Available savings report is now enhanced with a graph that displays point in time actual and optimal cluster costs as well as other dimensions (i.e. CPU, Memory, node count).

Added support for kOps version 1.20.

For AWS/kOps clusters we previously deployed a Lambda function per cluster, its no longer the case. From now on a single Lambda function is deployed per account.

Implemented the handling of cases when customer has removed some permissions (or the cluster itself) in their cloud provider account. In such a scenario, the cluster would be displayed with status “Failed” in our console and user would have two options: remove the cluster from the console or fix the error in their cloud provider’s account.

Fixed various reported bugs and implemented other UI improvements.

Organizational roles have been released. Every organization now has Owner, Member, or Viewer (read-only) roles that can be managed in our console.

Cluster headroom and Node constraints policies are now independent and can be set separately.

Improved cluster onboarding flow. Customers are no longer required to enter the access key and secret details, the onboarding script takes care of these details now.

Customers can now set annotation on the pod level that would prevent Evictor from removing the node that hosts the pod. More details about annotations and labels used by Evictor can be found in our documentation.

The node deletion policy now removes nodes that are marked by Evictor immediately, ignoring the time delay set for empty nodes in the “Node deletion” policy. That way, customers can avoid paying for nodes that were marked as unschedulable.

Customers using AWS GovCloud regions (AWS GovCloud (US-East) and AWS GovCloud (US-West)) are now able to connect their clusters and check possible savings.

CPU hrs report is now available in the console. The report presents the total amount of CPU hours accumulated across all of the nodes in the organization.

GKE clusters running shielded nodes are now also fully supported in our platform.

Improved our inventory to support a wider range of instance types.

Delivered multiple Autoscaler improvements.

Minor UI improvements and bug fixes.

GKE cluster optimization. Customers running unshielded GKE clusters can now onboard them into CAST AI and benefit from all cost optimization policies.

Cluster metrics endpoint - we have released the first version of the metrics endpoint that provides visibility into the CAST AI-captured metrics. The initial description of metrics and setup guide can be found in Github. We will continue expanding the list of exposed metrics, so stay tuned.

Implemented Node Root Volume Policy policy that allows the configuration of root volume size based on the CPU count. This way nodes with a high CPU count can have a larger root disk allocated upon node creation.

We have enhanced the Spot policy for EKS and kOps, so customers can instruct CAST AI to provision the least interrupted spot instances, most cost-effective ones, or simply leave the default - balanced approach. We also support an ability to override this cluster-wide policy on the deployment level.

CAST AI agent v.0.20.0 was released - the agent now supports auto-discovery of GKE clusters, users are no longer required to enter any cluster details manually.

Cluster headroom and Node constraints policies are now separated and can be used simultaneously.

We made it easier for users to set correct node CPU and Memory constraints that adhere to supported ratios.

Bug fixes and small interface improvements.

Implemented a new feature that allows users to set the time for how long an empty node should be kept alive before deletion. This “empty node time-to-live” setting makes node deletion policy less aggressive in case users do not want to delete empty nodes right away. Read more about this feature in our docs.

CAST AI agent v0.19.2 was released - we removed managed fields and sensitive environment variables from objects as well as introduced compression of delta content sent by the agent. Ensure that you always update to the latest version of our agent. Check github for more details.

Quality of life improvements:

• GKE connect cluster improved UX

• Savings estimator now displays totals of nodes in current and optimized configurations

• Savings estimator now displays the status of all Cost optimization policies

• Spot instance recommendations for workloads from now on can be exported to .csv

• Users can now investigate the content of yaml file in connect your cluster screen, before deploying it to the cluster

• Improved UX for scenarios when Add-ons are not installed or can’t be found

Enhancement of our Audit log has continued, making it more detailed and useful.

Rolled out various bug fixes and small improvements.

Our Savings Estimator as well Autoscaler are now able to target higher variety of instance types when recommending SPOT instances. This improvement allows customers to unlock more savings from the use of instance families that previously would not be considered.

From now on users can rename the organization after the initial creation.

Audit log is now much more detailed and available for EKS and kOps clusters (previously this feature was available only on CAST AI created clusters).

We introduced annotation and label that protects a node from being considered for eviction and deletion, you can read more about it in our documentation.

During the migration in to CAST AI selected nodes, customers might want to specify minimum and maximum values of CPU and RAM for nodes to be added to a cluster. Now users can easily set these parameters in our Unscheduled pods policy and limit the possible pool of nodes that CAST AI considers. As before, other option is to use Cluster headroom settings.

We have added the support of kOps 1.11, 1.15 and 1.17.

Removed IAM permission to create new roles from our credentials script.

Implemented another quality of life improvement - clusters can now be sorted based on the name, region or status.

Fixed bugs and made minor improvements to UI.

CAST AI now supports Organizations! Multiple team members from a company can now join CAST AI, create organization inside our console and collaboratively manage K8s clusters.

GCP customers can connect GKE clusters to CAST AI and see how much they could save by using CAST AI optimization engine. As always this is completely free and safe as our agent operates in read only mode. Try it out now. Functionality to optimize GKE cluster using CAST AI is currently in development.

Users running kOps clusters on AWS can now fully benefit from CAST AI cost analysis and optimization functionality. Connect your kOps cluster now, to see how much you can save and realize those savings by turning on AI driven optimization policies.

Connected AWS (EKS and kOps) clusters can now be paused and resumed as easily as CAST AI created clusters. Functionality to pause and resume on pre-set schedule is coming soon as well.

Node list is now accessible as soon as cluster is connected, customers no longer need to onboard cluster to access this functionality.

Additional Control plane nodes can now be added to CAST AI created clusters.

Clusters that were onboarded to CAST AI can now be disconnected via UI, customers have an option to delete or leave CAST AI created nodes.

We have reacted to user feedback and made minor adjustments in UI as well as fixed bugs.

We have released the Add-ons management functionality for CAST AI clusters. Now CAST AI clusters will be created faster without any add-ons pre-installed. Afterward, users will be able to choose the add-ons they wish to use. The Add-ons feature is available in the cluster dashboard, try it out! 

We increased the frequency of communication between the agent deployed on the client’s cluster and CAST AI and reduced the amount of data the agent sends via the network. Now CAST AI can react in as little as 15 seconds and scale the cluster as required.

We have applied minor improvements and fixes to increase the accuracy of our Available savings report.

Improved experience for selecting and managing your subscription.

Created a guide on how to disconnect your EKS cluster from CAST AI.

Last but not least, we fixed some bugs and made small improvements to the UI.

We have made cost optimization policies available to EKS clusters created outside of CAST AI! On top of the previously released “Connect your cluster” functionality, users can now onboard EKS credentials and start using CAST AI policies to reduce their cloud bills.

Created a guide on how to migrate out of EKS nodes into CAST AI selected nodes in AWS.

We have also uplifted the design of the Available Savings report, so users can better understand CAST AI recommendations and the next steps required to achieve an optimal cluster configuration.

Streamlined the workflow by reducing the number of actions required to onboard an EKS cluster and access the Available Savings report

Created a guide for the installation and configuration of VPA for pod rightsizing.

As always, we hunted down and fixed various bugs.

Save costs by stopping your clusters when they're idle! We have launched a “Cluster schedule” functionality to pause and resume clusters based on the user-defined schedule. Find this feature in your cluster dashboard or check the documentation.

The node autoscaler policy now supports GCP Preemptive Instances.

We introduced additional validations in GCP credentials onboarding.

As always our team took care of bug fixes, performance optimizations, and small UI improvements.

Launched an agent to connect the EKS cluster (that was not created by CAST AI) to our console. Users can now connect clusters in read-only mode and use the “Savings” feature to analyze proposed optimizations and their impact on the cloud bill.

Revamped dashboard UI.

Node interruptions made visible in the logs data via Audit log UI.

Canada East (Montréal) is now a supported region in our cluster creation flow.

Fixed minor bugs.

We have simplified the user credentials creation process for GCP.

You can now control your clusters using our own Command Line Interface (CLI). Read more about the beta version and install it!

Improved handling of Kubernetes nodes and load balancers, so the status of the nodes is tracked, and load balancers are removed when appropriate.

Improved Unschedulable Pods policy to peak in to the future and consider nodes that are being created.

Improved structure of our documentation; check it at docs.cast.ai.

Updated UI elements in our console and, as always, our team shipped some bug-fixes.

Launched the status page so our customers can check the health of our platform.