Cloud-Costs | 2i2c

Adding User Group Insights to Cloud Cost Dashboards with Grafana

Mon, 24 Nov 2025 00:00:00 +0000

We are excited to announce that we have extended our cloud cost dashboards to support display costs filtered by user groups using Grafana! This new feature allows administrators to monitor and manage cloud expenses based on user group memberships in JupyterHub.

Available for dedicated AWS clusters only (and excluding CloudBank managed accounts). Other deployments on GCP will be supported in the future.

Learn more #

Take a look at the Community Hub Guide to see what’s new
Check out the documentation of the 2i2c-org/jupyterhub-cost-monitoring project to see how it all works
Jenny recently presented her work on the cost monitoring system at JupyterCon 2025 earlier this month. Watch a video or look at the slides.

Give us feedback! Click here to provide feedback that will help us make this more impactful.

Acknowledgements #

Tarashish @ Development Seed for collaborating on this project with us.
NASA VEDA and the DSE Team at NASA MSFC ODSI for funding much of this work.
Kyle Lesinger from the NASA MSFC Office of Data Science and Informatics for providing valuable feedback and bug reports during development.

Enabling transparent cloud cost monitoring with user-level dashboards

Tue, 30 Sep 2025 00:00:00 +0000

We are excited to announce that dashboards to monitor cloud usage and costs at a per-user level are now available! See the cost monitoring documentation for more information.

A key goal of 2i2c is to make the cloud safe for science. By providing transparent cost monitoring, we give communities the confidence that they won’t face unexpected bills and can better understand how their usage patterns translate to cloud costs. This visibility is especially valuable in our shared platform model, where each community gets their own independent hub while benefiting from shared infrastructure expertise.

The user-level cost breakdown allows communities to identify individual usage trends and manage their resources more effectively. Communities can now see exactly how their computational work translates to cloud spending, enabling better resource planning and budget management.

Give us feedback! Click here to provide feedback that will help us make this more impactful.

Learn more #

Cost monitoring documentation

Acknowledgements #

Tarashish @ Development Seed for working on this with us.
NASA VEDA for funding much of this work.
Andy @ Openscapes, Alex @ Development Seed and Sarah @ Earthscope for giving us close feedback.

Reducing base infrastructure costs on AWS with smarter instance types

Wed, 17 Sep 2025 00:00:00 +0000

We’ve been working to reduce the base costs of running our cloud infrastructure on AWS by switching to more efficient instance types for our core nodes. This is the core infrastructure we use to ensure hubs are “always available” for users, even when no one is actively using a hub. By moving from older r5.xlarge instances to newer, more efficient r8i-flex.large instances, we’ve significantly reduced these baseline costs while maintaining the same level of service. Here’s a plot of daily savings for the GeoJupyter community.

The graph above shows the impact on EC2 node costs specifically (this doesn’t include the entire cost of always-on infrastructure, but represents a significant portion). We are rolling out this change to all new clusters, and starting to work through our pre-existing AWS clusters.