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To self-host marimo for a team, deploy marimohub—the team platform for storing, managing, and running marimo notebooks—and choose its storage, kernel-compute, and identity backends. For a small team on one machine, the project documents a single-host outline, but explicitly labels it untested and limits it to one replica. Teams that need higher availability or horizontal scaling should consider the Helm deployment on Kubernetes, while planning separately for ingress, TLS, and kernel infrastructure.
This guide is about marimohub, not the separate marimo Kubernetes operator, which deploys individual notebook servers. A marimohub deployment brings together the web app, API, access control, version history, and kernel lifecycle; it is more than a notebook server behind a login page.
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The standard route is configuration-driven deployment with the prebuilt container, intended for Docker, Podman, and Kubernetes. SDK composition is for teams that need custom adapters, routes, or unusual runtimes. Choose based on how you will operate the Hub and where notebook kernels should run—not simply on how quickly the web app can be started.
| Decision | Single Linux host | Kubernetes with Helm |
|---|---|---|
| Operating footprint | One Linux machine, local filesystem storage, and Docker compute with a container per kernel, as described in marimo’s single-instance deployment outline. | The Helm chart deploys the Hub in Kubernetes; the Kubernetes compute backend can run kernels in native Pods. |
| Scale and availability | One replica, bounded by the host’s kernel capacity. The project directs teams needing high availability or horizontal scaling to Kubernetes and object storage. | Fits teams already operating Kubernetes that need independently managed Hub replicas and kernel Pods. Cluster-specific ingress and kernel setup remain operator responsibilities. |
| Documented setup confidence | The official page labels this an “Outline — not yet a tested recipe.” | The Helm guide covers installation, updates, rollback, and validation. Pin the chart version and keep one maintenance pod. |
| Kernel exposure | The outline uses proxy exposure: kernel ports stay off the network, but kernels share the app’s origin. The documentation says this mode is for trusted users. | Exposure and networking depend on the selected kernel backend and origin-isolation configuration. |
There is no universal CPU, memory, cost, or team-size threshold established by the deployment guidance. Benchmark representative notebooks at the concurrency you expect, using the compute backend you plan to run.
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Plan the three backend decisions first
Storage: choose durability and compatibility
marimohub uses storage for platform data and notebook-related content. The documented S3-compatible backend requires conditional-write support; treat that as a compatibility requirement, not an optional performance setting. The project lists AWS S3, Cloudflare R2, Tigris, CoreWeave CAIOS, and recent MinIO as supported examples. Older MinIO or Ceph builds may not qualify. The described MinIO and Ceph configurations require path-style addressing.
Confirm conditional writes and the addressing mode against the exact storage service and version you operate before relying on it. The overview also names Ceph among S3-compatible choices, but does not make every Ceph build interchangeable with every supported store.
Compute: decide where notebook code runs
Compute is separable from the Hub service. The documented choices include a Kubernetes backend, which creates a Pod and Service for each kernel session and can create an Ingress for subdomain exposure, and Modal as a serverless example. The marimo documentation describes Modal as requiring no infrastructure for the operator to provision or scale; it does not provide a pricing comparison. Choose according to your team’s infrastructure and operations model, and account for where kernels run, what secrets they can access, and how their files persist.
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Identity: use OIDC for production sign-in
The production identity backend described by the project is OIDC. Google, Okta, and Auth0 are named examples; Microsoft Entra ID is covered in the Azure deployment guide. These are provider examples, not a claim that credentials or provider-specific configuration are supplied automatically. The operator must configure the identity provider and register the callback.
Set up sign-in and session security
Configure the OIDC issuer, client ID, client secret, session secret, and required email-domain allowlist. Email verification is required by default. Register the callback with the identity provider exactly as the Hub will use it:
https://<your-host>/api/auth/callback
The callback must match the registered HTTPS URI exactly. The session secret is used to sign the session cookie; manage it as a secret rather than placing it in a notebook or other user-accessible project file.
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Choose kernel exposure with the trust boundary in mind
The marimo project’s Security model documentation states: “marimohub runs untrusted code (notebook kernels) on behalf of authenticated users.” Authentication therefore does not make notebook code safe to execute. Treat kernels as code-execution environments, and decide which users may create or edit notebooks, what credentials kernels receive, and how kernels are isolated from the Hub and other workloads.
The documented subdomain exposure mode separates the kernel domain from the app’s origin. By contrast, the single-host outline’s proxy option is same-origin and is described as appropriate for trusted users. Do not choose the simpler proxy path for a less-trusted audience just because kernel ports are not directly exposed to the network; origin separation is a distinct security consideration.
Be cautious with deployment-wide secrets. The configuration documentation warns that deployment-wide Modal secrets are injected into all editor, app, and job sandboxes. A notebook author able to run code in those environments may be able to read the secrets. For credentials that belong to one project, use project-specific integration secret references rather than granting every sandbox a deployment-wide credential.
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Configure editing and persistence for the way the team works
Shared or exclusive editing
In shared editor mode, multiple editors use one persistent sandbox for a notebook. In exclusive mode, one editor owns the notebook’s editing session; other editors can start temporary sandboxes or confirm a takeover. These editor-sharing settings do not apply to apps or viewer sessions.
Source-only or workspace persistence
Source persistence saves notebook source and pyproject.toml. Workspace persistence also captures runtime files and restores them in the next session. The documented captured files include hidden and project files such as .env, .gitignore, .git/, and __marimo__/; common regenerable caches are excluded.
Project members with read access can read captured files. Before enabling workspace persistence, inspect what notebooks may write into their workspace, especially credentials, tokens, and other sensitive runtime artifacts. A hidden file is not protected from project members merely because its name begins with a dot.
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Deploy and validate in a controlled sequence
- Select the deployment route. Use the configuration-driven prebuilt-container route for standard Docker, Podman, or Kubernetes installations. Reserve SDK composition for custom adapters, routes, or runtimes. Treat the one-host path as an untested outline, not a validated production recipe.
- Configure durable storage. Choose a compatible S3 backend, verify conditional-write support, and set path-style addressing where required for the described MinIO or Ceph configuration.
- Choose kernel compute and exposure. Decide whether kernels run in Docker containers, Kubernetes Pods, or the documented Modal backend, then choose an exposure mode that fits your trust boundary.
- Set up OIDC. Configure issuer and client credentials, the exact HTTPS callback, session secret, and required email-domain allowlist. Confirm email verification behavior with the identity setup.
- Set collaboration and persistence policies. Pick
sharedorexclusiveediting and source-only or workspace persistence after reviewing who can read saved files. - Pin and install the Helm release if using Kubernetes. The Helm documentation says chart version, app version, and image tag match; pin the chart version rather than relying on a moving release. Keep one maintenance pod.
- Validate a complete user workflow. Sign in through OIDC, create a notebook, start a kernel, and save it. For Kubernetes, verify that the required kernel resources and network exposure work in addition to the Hub itself.
The Helm chart covers the Hub tier; it does not install cluster-specific ingress, certificate management, or kernel-namespace resources. Plan those separately, and manage deployment secrets through an appropriate secret-management process. Teams using the single-host outline should account for its one-replica constraint and the documentation’s untested status before depending on it.
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The marimo Kubernetes operator is a different deployment path for individual notebook servers; it is not a substitute name for marimohub. Choose marimohub when the requirement is a team platform with shared management, access control, version history, and kernel lifecycle. Consider the separate operator only when the desired unit of deployment is an individual notebook server rather than the Hub platform.
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