Appendix: Projecting deployment costs#
Clarification on cost projections
As a non-profit research project, Project Jupyter does not offer, recommend, or sell cloud deployment services for JupyterHub.
The information in this section is offered as guidance as requested by our users. We caution that costs can vary widely based on providers selected and your use cases.
Cost calculators for cloud providers#
Below are several links to cost estimators for cloud providers:
Factors influencing costs#
Cost estimates depend highly on your deployment setup. Several factors that significantly influence cost estimates, include:
Computational resources provided to users
Number of users
Usage patterns of users
Memory (RAM) makes up the largest part of a cost estimate. More RAM means that your users will be able to work with larger datasets with more flexibility, but it can also be expensive.
Persistent storage for users, if needed, is another element that will impact the cost estimate. If users don’t have persistent storage, then disks will be wiped after users finish their sessions. None of their changes will be saved. This requires significantly fewer storage resources, and also results in faster load times.
For an indicator of how costs scale with computational resources, see the Google Cloud pricing page.
The number of users has a direct relationship to cost estimates. Since a deployment may support different types of users (i.e. researchers, students, instructors) with varying hardware and storage needs, take into account both the type of users and the number per type.
User usage patterns#
Another important factor is what usage pattern your users will have. Will they all use the JupyterHub at once, such as during a large class workshop? will users use JupyterHub at different times of day?
The usage patterns and peak load on the system have important implications for the resources you need to provide. In the future JupyterHub will have auto-scaling functionality, but currently it does not. This means that you need to provision resources for the maximum expected number of users at one time.
Here are a few examples that describe different use cases and the amount of resources used by a particular JupyterHub implementation. There are many factors that go into these estimates, and you should expect that your actual costs may vary significantly under other conditions.
The Data 8 course at UC Berkeley used a JupyterHub to coordinate all course material and to provide a platform where students would run their code. This consisted of many hundreds of students, who had minimal requirements in terms of CPU and memory usage. Ryan Lovett put together a short Jupyter notebook estimating the cost for computational resources depending on the student needs.