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+# Institutional FAQ
+
+This page contains common questions from users of JupyterHub,
+broken down by their roles within organizations.
+
+# For all
+
+## Is it appropriate for adoption within a larger institutional context?
+
+Yes! JupyterHub has been used at-scale for large pools of users, as well
+as complex and high-performance computing. For example, UC Berkeley uses
+JupyterHub for its Data Science Education Program courses (serving over
+3,000 students). The Pangeo project uses JupyterHub to provide access
+to scalable cloud computing with Dask. JupyterHub is stable customizable
+to the use-cases of large organizations.
+
+## I keep hearing about Jupyter Notebook, JupyterLab, and now JupyterHub. What’s the difference?
+
+Here is a quick breakdown of these three tools:
+
+* **The Jupyter Notebook** is a document specification (the `.ipynb`) file that interweaves
+  narrative text with code cells and their outputs. It is also a graphical interface
+  that allows users to edit these documents. There are also several other graphical interfaces
+  that allow users to edit the `.ipynb` format (nteract, Jupyer Lab, Google Colab, Kaggle, etc).
+* **JupyterLab** is a flexible and extendible user interface for interactive computing. It
+  has several extensions that are tailored for using Jupyter Notebooks, as well as extensions
+  for other parts of the data science stack.
+* **JupyterHub** is an application that manages interactive computing sessions for **multiple users**.
+  It also connects them with infrastructure those users wish to access. It can provide
+  remote access to Jupyter Notebooks and Jupyter Lab for many people.
+
+# For management
+
+## Briefly, what problem does JupyterHub solve for us?
+
+JupyterHub provides a shared platform for data science and collaboration.
+It allows users to utilize familiar data science workflows (such as the scientific python stack,
+the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also allows administrators
+some control over access to resources, security, environments, and authentication.
+
+## Is JupyterHub mature? Why should we trust it?
+
+Yes - the core JupyterHub application recently
+reached 1.0 status, and is considered stable and performant for most institutions.
+JupyterHub has also been deployed (along with other tools) to work on
+scalable infrastructure, large datasets, and high-performance computing.
+
+## Who else uses JupyterHub?
+
+JupyterHub is used at a variety of institutions in academia,
+industry, and government research labs. It is most-commonly used by two kinds of groups:
+
+* Small teams (e.g., data science teams, research labs, or collaborative projects) to provide a
+  shared resource for interactive computing, collaboration, and analytics.
+* Large teams (e.g., a department, a large class, or a large group of remote users) to provide
+  access to organizational hardware, data, and analytics environments at scale.
+
+Here are a sample of organizations that use JupyterHub:
+
+* **Universities and colleges**: UC Berkeley, UC San Diego, Cal Poly SLO, Harvard University, University of Chicago,
+  University of Oslo, University of Sheffield, Université Paris Sud, University of Versailles
+* **Research laboratories**: NASA, NCAR, NOAA, the Large Synoptic Survey Telescope, Brookhaven National Lab,
+  Minnesota Supercomputing Institute, ALCF, CERN, Lawrence Livermore National Laboratory
+* **Online communities**: Pangeo, Quantopian, mybinder.org, MathHub, Open Humans
+* **Computing infrastructure providers**: NERSC, San Diego Supercomputing Center, Compute Canada
+* **Companies**: Capital One, SANDVIK code, Globus
+
+See the [Gallery of JupyterHub deployments](../gallery-jhub-deployments.md) for
+a more complete list of JupyterHub deployments at institutions.
+
+## How does JupyterHub compare with hosted products, like Google Colaboratory, RStudio.cloud, or Anaconda Enterprise?
+
+JupyterHub puts you in control of your data, infrastructure, and coding environment.
+In addition, it is vendor neutral, which reduces lock-in to a particular vendor or service.
+JupyterHub provides access to interactive computing environments in the cloud (similar to each of these services).
+Compared with the tools above, it is more flexible, more customizable, free, and
+gives administrators more control over their setup and hardware.
+
+Because JupyterHub is an open-source, community-driven tool, it can be extended and
+modified to fit an institution's needs. It plays nicely with the open source data science
+stack, and can serve a variety of computing enviroments, user interfaces, and
+computational hardware. It can also be deployed anywhere - on enterprise cloud infrastructure, on
+High-Performance-Computing machines, on local hardware, or even on a single laptop, which
+is not possible with most other tools for shared interactive computing.
+
+# For IT
+
+## How would I set up JupyterHub on institutional hardware?
+
+That depends on what kind of hardware you've got. JupyterHub is flexible enough to be deployed
+on a variety of hardware, including in-room hardware, on-prem clusters, cloud infrastructure,
+etc.
+
+The most common way to set up a JupyterHub is to use a JupyterHub distribution, these are pre-configured
+and opinionated ways to set up a JupyterHub on particular kinds of infrastructure. The two distributions
+that we currently suggest are:
+
+* [Zero to JupyterHub for Kubernetes](https://z2jh.jupyter.org) is a scalable JupyterHub deployment and
+  guide that runs on Kubernetes. Better for larger or dynamic user groups (50-10,000) or more complex
+  compute/data needs.
+* [The Littlest JupyterHub](https://tljh.jupyter.org) is a lightweight JupyterHub that runs on a single
+  single machine (in the cloud or under your desk). Better for smaller usergroups (4-80) or more
+  lightweight computational resources.
+
+
+## Does JupyterHub run well in the cloud?
+
+Yes - most deployments of JupyterHub are run via cloud infrastructure and on a variety of cloud providers.
+Depending on the distribution of JupyterHub that you'd like to use, you can also connect your JupyterHub
+deployment with a number of other cloud-native services so that users have access to other resources from
+their interactive computing sessions.
+
+For example, if you use the [Zero to JupyterHub for Kubernetes](https://z2jh.jupyter.org) distribution,
+you'll be able to utilize container-based workflows of other technologies such as the [dask-kubernetes](https://kubernetes.dask.org/en/latest/)
+project for distributed computing.
+
+The Z2JH Helm Chart also has some functionality built in for auto-scaling your cluster up and down
+as more resources are needed - allowing you to utilize the benefits of a flexible cloud-based deployment.
+
+## Is JupyterHub secure?
+
+The short answer: yes. JupyterHub as a standalone application has been battle-tested at an institutional
+level for several years, and makes a number of "default" security decisions that are reasonable for most
+users.
+
+* For security considerations in the base JupyterHub application,
+  [see the JupyterHub security page](https://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html)
+* For security considerations when deploying JupyterHub on Kubernetes, see the
+  [JupyterHub on Kubernetes security page](https://zero-to-jupyterhub.readthedocs.io/en/latest/security.html).
+
+The longer answer: it depends on your deployment. Because JupyterHub is very flexible, it can be used
+in a variety of deployment setups. This often entails connecting your JupyterHub to **other** infrastructure
+(such as a [Dask Gateway service](https://gateway.dask.org/)). There are many security decisions to be made
+in these cases, and the security of your JupyterHub deployment will often depend on these decisions.
+
+If you are worried about security, don't hesitate to reach out to the JupyterHub community in the
+[Jupyter Community Forum](https://discourse.jupyter.org/c/jupyterhub). This community of practice has many
+individuals with experience running secure JupyterHub deployments.
+
+
+## Does JupyterHub provide computing or data infrastructure?
+
+No - JupyterHub manages user sessions and can *control* computing infrastructure, but it does not provide these
+things itself. You are expected to run JupyterHub on your own infrastructure (local or in the cloud). Moreover,
+JupyterHub has no internal concept of "data", but is designed to be able to communicate with data repositories
+(again, either locally or remotely) for use within interactive computing sessions.
+
+
+## How do I manage users?
+
+JupyterHub offers a few options for managing your users. Upon setting up a JupyterHub, you can choose what
+kind of **authentication** you'd like to use. For example, you can have users sign up with an institutional
+email address, or choose a username / password when they first log-in, or offload authentication onto
+another service such as an organization's OAuth.
+
+The users of a JupyterHub are stored locally, and can be modified manually by an administrator of the JupyterHub.
+Moreover, the *active* users on a JupyterHub can be found on the administrator's page. This page
+gives you the abiltiy to stop or restart kernels, inspect user filesystems, and even take over user
+sessions to assist them with debugging.
+
+## How do I manage software environments?
+
+A key benefit of JupyterHub is the ability for an administrator to define the environment(s) that users
+have access to. There are many ways to do this, depending on what kind of infrastructure you're using for
+your JupyterHub.
+
+For example, **The Littlest JupyterHub** runs on a single VM. In this case, the administrator defines
+an environment by installing packages to a shared folder that exists on the path of all users. The
+**JupyterHub for Kubernetes** deployment uses Docker images to define environments. You can create your
+own list of Docker images that users can select from, and can also control things like the amount of
+RAM available to users, or the types of machines that their sessions will use in the cloud.
+
+## How does JupyterHub manage computational resources?
+
+For interactive computing sessions, JupyterHub controls computational resources via a **spawner**.
+Spawners define how a new user session is created, and are customized for particular kinds of
+infrastructure. For example, the KubeSpawner knows how to control a Kubernetes deployment
+to create new pods when users log in.
+
+For more sophisticated computational resources (like distributed computing), JupyterHub can
+connect with other infrastructure tools (like Dask or Spark). This allows users to control
+scalable or high-performance resources from within their JupyterHub sessions. The logic of
+how those resources are controlled is taken care of by the non-JupyterHub application.
+
+
+## Can JupyterHub be used with my high-performance computing resources?
+
+Yes - JupyterHub can provide access to many kinds of computing infrastructure.
+Especially when combined with other open-source schedulers such as Dask, you can manage fairly
+complex computing infrastructure from the interactive sessions of a JupyterHub. For example
+[see the Dask HPC page](https://docs.dask.org/en/latest/setup/hpc.html).
+
+## How much resources do user sessions take?
+
+This is highly configurable by the administrator. If you wish for your users to have simple
+data analytics environments for prototyping and light data exploring, you can restrict their
+memory and CPU based on the resources that you have available. If you'd like your JupyterHub
+to serve as a gateway to high-performance compute or data resources, you may increase the
+resources available on user machines, or connect them with computing infrastructure elsewhere.
+
+## Can I customize the look and feel of a JupyterHub?
+
+JupyterHub provides some customization of the graphics displayed to users. The most common
+modification is to add custom branding to the JupyterHub login page, loading pages, and
+various elements that persist across all pages (such as headers).
+
+# For Technical Leads
+
+## Will JupyterHub “just work” with our team's interactive computing setup?
+
+Depending on the complexity of your setup, you'll have different experiences with "out of the box"
+distributions of JupyterHub. If all of the resources you need will fit on a single VM, then
+[The Littlest JupyterHub](https://tljh.jupyter.org) should get you up-and-running within
+a half day or so. For more complex setups, such as scalable Kubernetes clusters or access
+to high-performance computing and data, it will require more time and expertise with
+the technologies your JupyterHub will use (e.g., dev-ops knowledge with cloud computing).
+
+In general, the base JupyterHub deployment is not the bottleneck for setup, it is connecting
+your JupyterHub with the various services and tools that you wish to provide to your users.
+
+
+## How well does JupyterHub scale? What are JupyterHub's limitations?
+
+JupyterHub works well at both a small scale (e.g., a single VM or machine) as well as a
+high scale (e.g., a scalable Kubernetes cluster). It can be used for teams as small a 2, and
+for user bases as large as 10,000. The scalability of JupyterHub largely depends on the
+infrastructure on which it is deployed. JupyterHub has been designed to be lightweight and
+flexible, so you can tailor your JupyterHub deployment to your needs.
+
+
+## Is JupyterHub resilient? What happens when a machine goes down?
+
+For JupyterHubs that are deployed in a containerized environment (e.g., Kubernetes), it is
+possible to configure the JupyterHub to be fairly resistant to failures in the system.
+For example, if JupyterHub fails, then user sessions will not be affected (though new
+users will not be able to log in). When a JupyterHub process is restarted, it should
+seamlessly connect with the user database and the system will return to normal.
+Again, the details of your JupyterHub deployment (e.g., whether it's deployed on a scalable cluster)
+will affect the resiliency of the deployment.
+
+## What interfaces does JupyterHub support?
+
+Out of the box, JupyterHub supports a variety of popular data science interfaces for user sessions,
+such as JupyterLab, Jupyter Notebooks, and RStudio. Any interface that can be served
+via a web address can be served with a JupyterHub (with the right setup).
+
+## Does JupyterHub make it easier for our team to collaborate?
+
+JupyterHub provides a standardized environment and access to shared resources for your teams.
+This greatly reduces the cost associated with sharing analyses and content with other team
+members, and makes it easier to collaborate and build off of one another's ideas. Combined with
+access to high-performance computing and data, JupyterHub provides a common resource to
+amplify your team's ability to prototype their analyses, scale them to larger data, and then
+share their results with one another.
+
+JupyterHub also provides a computational framework to share computational narratives between
+different levels of an organization. For example, data scientists can share Jupyter Notebooks
+rendered as [voila dashboards](https://voila.readthedocs.io/en/stable/) with those who are not
+familiar with programming, or create publicly-available interactive analyses to allow others to
+interact with your work.
+
+## Can I use JupyterHub with R/RStudio or other languages and environments?
+
+Yes, Jupyter is a polyglot project, and there are over 40 community-provided kernels for a variety
+of languages (the most common being Python, Julia, and R). You can also use a JupyterHub to provide
+access to other interfaces, such as RStudio, that provide their own access to a language kernel.
diff --git a/docs/source/index.rst b/docs/source/index.rst
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@@ -86,6 +86,7 @@ Getting Started
    :maxdepth: 1
 
    getting-started/index
+   getting-started/institutional-faq
    getting-started/config-basics
    getting-started/networking-basics
    getting-started/security-basics