Seqera Platform Feature Requests

Problem: Platform drives GKE clusters through the control plane's IP endpoint. Security-conscious customers increasingly run private-endpoint-only clusters ( enablePrivateEndpoint=true , often with privateEndpointEnforcementEnabled=true ), where the control plane has no public IP and no authorized-networks entry can admit Seqera Cloud. For these customers, GKE compute environment creation fails at the reachability probe, and today their only supported choices give something up: Google Batch (no Kubernetes), a re-enabled restricted public endpoint (posture change), the generic Kubernetes CE over the DNS endpoint (no Fusion, no Data Explorer on work dirs), or Enterprise (deployment change). Proposal: Every GKE cluster has a second control-plane endpoint: a DNS name ( controlPlaneEndpointsConfig.dnsEndpointConfig.endpoint , GA 2024-11-11, always present) served through Google Front End and authenticated by IAM ( container.clusters.connect ) instead of source IP. GKE compute environments should prefer it when enabled ( allowExternalTraffic=true ), falling back to the IP endpoint otherwise. That makes private-endpoint-only clusters work from Seqera Cloud with no change to the cluster's network posture and the full GKE feature set intact - Fusion, Wave, Data Explorer, log retrieval.

Allow the Seqera Platform's built-in implementation of "spot" instances to automatically failover to "on-demand" instances. This can be achieved with a Nextflow config but having a built-in implementation could help, especially when frequently creating or destroying compute environments.

The search/filter field only matches literal text. Ideally, I would like to be able to filter some runs by using a prefix that multiple labels may share. E.g. if I link some runs to an external integration that always adds a label : dg_run_[<RUN_ID>] I would like to be able to get all of these runs by searching for dg_run_ . I currently solve this by adding an extra label to each of these runs (shared across all of them) and I can filter without the wildcard search, but it would be cleaner to be able to do a wildcard search directly without the need of adding an extra label.

Currently Data Explorer preview supports common formats. For the image analysis community, a big win would be to be able to visualize large, multiplexed images directly on s3 storage with something like: https://github.com/hms-dbmi/viv https://avivator.gehlenborglab.org/ The value of this will become more assessable as we onboard more clients doing image analysis.

It would be great to have the ability to create folders directly in data explorer. Right now, the workaround is to create the folder locally and then upload that folder.

As a user, I want to be able to provide multiple subnet IDs when configuring an AWS Cloud compute environment, so that I can spread workloads across multiple Availability Zones for resilience and spot capacity availability. The AWS Cloud CE currently accepts only a single subnet ID ( subnetId ) at the API level, which flows through to both the UI (single string field) and the Terraform provider ( subnet_id string attribute). This means users cannot configure multi-AZ deployments from the UI, API, or Terraform today. By contrast, AWS Batch CEs (via forge config) already support subnets: []string in the API and a list attribute in Terraform. AWS Cloud CE should be brought to parity.

Once a Studio session is running, it incurs costs. Estimating the hourly cost of the session and clearly presenting that in the user interface will ensure that users are aware of the potential cost impacts. The estimated hourly cost could initially be estimated on the “Add Studio” page (when the CE is selected) and be displayed on the specific Studio session detail page once the session is in a running state. When the session is in a stopped state, the cost estimator is still visible, but considered inactive.

If I go to the Outputs tab, it is not obvious what I need to do to enable it. If I don't have lineage setup correctly, I only have a greyed out button that links me to "Data Explorer". As a user I'd like guidance on what i need to do to make this tab useful.

Currently there is a single workspace default compute environment shared by both pipeline runs and Studios. Choosing a Studios-friendly default (e.g. AWS Cloud/EC2) changes the pipeline-run default (e.g. AWS Batch) too. Please support independently selecting/defaulting the compute environment for pipeline runs and Studios.

I want Benchling data available in Data Explorer, specifically the hierarchy of experiments and associated metadata to streamline the workflow inputs.