JupyterHub vs JupyterLab: They solve different problems

Search “JupyterHub vs JupyterLab” and you’ll find side-by-side comparisons treating them as competing products. Pick one, move on. That framing is wrong, and it leads people to make confused infrastructure decisions.

JupyterHub and JupyterLab were created years apart, for entirely different problems, at different layers of the stack. JupyterHub (first released in 2015) was built to give many users access to notebooks through a shared, centralized service; originally for classrooms and research labs. JupyterLab (introduced in 2018) was built to replace the classic Notebook interface with something closer to a modern IDE. Comparing them directly is like comparing Kubernetes to VS Code. One decides who gets compute. The other defines how you work once you have it.

Understanding this distinction matters because the real decision most teams face isn’t “Hub or Lab.” It’s whether they want to run notebook infrastructure themselves at all.

What is JupyterHub? (Launched 2015, multi-user infrastructure)

JupyterHub was first released in 2015 to solve a specific problem: how do you give many users access to Jupyter notebooks through a shared, centralized service?

It emerged from academic and research environments where:

• Professors needed to serve notebooks to hundreds of students
• Research labs needed shared compute
• IT departments needed centralized authentication

JupyterHub is a multi-user server. It:

• Handles authentication (OAuth, LDAP, SSO, etc.)
• Manages user accounts
• Spawns isolated notebook servers per user
• Routes traffic to the correct server instance

It does not define the notebook interface. It is infrastructure; critical infrastructure; but infrastructure nonetheless.

💡 Pro Tip:
If you’re evaluating JupyterHub, test the admin flow; not just the user login. Most complexity lives in configuration, upgrades, and storage management.

What is JupyterLab? (Launched 2018, modern notebook interface)

JupyterLab was introduced in 2018 as the next-generation interface for Jupyter notebooks.

The classic Notebook interface was simple and linear. But as notebook workflows became more complex; multi-file projects, terminals, dashboards; users wanted something closer to a modern IDE.

JupyterLab provides:

• Tabbed notebooks
• A file browser
• Integrated terminals
• Drag-and-drop panels
• Extension ecosystem
• Debugger support

It runs inside a Jupyter server; whether local or spawned by JupyterHub.

💡 Pro Tip:

JupyterLab supports extensions; but every extension increases operational surface area in multi-user deployments. Keep production environments minimal.

How JupyterHub and JupyterLab work together

They are designed to work together.

The official JupyterLab documentation states that JupyterLab works out of the box with JupyterHub 1.0 and later.

The relationship looks like this:

• JupyterHub → manages users and compute
• JupyterLab → provides the interface
• Notebook server → runs per user session

Users log into JupyterHub. Hub spawns a server. That server loads JupyterLab. The UI is identical whether it’s local or centralized.

Typical Jupyter deployments (from solo to enterprise)

1. Solo developer

Runs:

jupyter lab

Local server. No authentication layer. No central management.

Minimal overhead.

2. University classroom (50–200 students)

Deploys JupyterHub on a Linux VM:

• Connects to campus SSO
• Configures shared storage
• Maintains container images
• Handles upgrades

Students see JupyterLab in the browser.

3. Enterprise deployment (500+ users)

Runs Zero to JupyterHub on Kubernetes:

• Auto-scaling pods
• Persistent volumes
• Container registry management
• GPU allocation
• Helm chart maintenance
• Dedicated DevOps support

The interface remains JupyterLab. The infrastructure complexity multiplies.

💡 Pro Tip:

Infrastructure complexity grows non-linearly with user count. The jump from 50 to 500 users is not incremental; it’s architectural.

When JupyterLab alone is enough

If you’re working solo or in a small team where everyone manages their own machine, JupyterLab on its own is the right call. There’s no shared authentication to worry about, no resource quotas to enforce, no environments to standardize across users. You install it, you run it, you work.

This covers a lot of real-world data work. A researcher exploring a dataset, a developer prototyping a model, an analyst building a one-off report; none of these need a multi-user server. JupyterLab gives them everything they need without any infrastructure overhead.

When JupyterHub becomes necessary

JupyterHub enters the picture when your problem shifts from “I need a notebook” to “many people need notebooks, and someone has to manage that.” The common triggers are centralized authentication (everyone logs in with university or company credentials), standardized environments (everyone gets the same Python version and packages), shared or constrained compute (GPU allocation, memory limits), and browser-only access (users shouldn’t need to install anything locally).

Classrooms are the classic example. A professor can’t ask 200 students to install Python, configure virtual environments, and troubleshoot dependency conflicts on their own machines. JupyterHub gives every student a working environment through the browser, identically configured, with one deployment.

The operational cost most comparisons ignore

Here’s the part that blog posts tend to gloss over: running JupyterHub is genuinely hard, and the difficulty scales non-linearly.

The Jupyter project offers two deployment paths. The Littlest JupyterHub (TLJH) runs on a single Linux VM and supports roughly 1 to 100 users. It’s the simpler option, but “simpler” still means provisioning a server, configuring HTTPS, managing user accounts, setting up storage, handling upgrades, and patching security issues. If the VM goes down, everyone loses access.

Zero to JupyterHub on Kubernetes (Z2JH) supports 50 to 10,000+ users but requires a Kubernetes cluster, Helm charts, cloud infrastructure, container registry management, and ongoing DevOps maintenance. This is not a weekend project. It’s a production system that needs someone watching it.

Neither path natively supports Windows as a host. The project recommends Linux VMs or Docker-based setups for Windows environments, which adds another layer of configuration.

The result is a real gap in the ecosystem. JupyterHub solves the access problem; how do you give many people notebooks? Although, it hands you an infrastructure problem in return. For organizations with dedicated platform teams, that tradeoff is fine. For smaller teams, research groups, or companies where data people outnumber DevOps people, it’s a serious bottleneck.

JupyterHub doesn’t fail because the software is bad. It fails because running multi-user infrastructure reliably is a full-time job, and most teams adopting JupyterHub don’t have someone whose full-time job that is.

Where Deepnote fits

Deepnote sidesteps the JupyterHub question entirely. Instead of giving you tools to build and operate a multi-user notebook platform, it gives you a multi-user, AI-native notebook platform that’s managed, so you don’t need to take care of that or build data integrations yourself.

Teams get browser-based access, real-time collaboration with multi-cursor editing, role-based permissions, SSO integration, and managed compute; the same things JupyterHub provides, without provisioning servers, managing Kubernetes, or patching security updates. The notebook workflow stays familiar: cells, Python, data exploration, and visualization. Teams don’t have to operate or maintain the underlying infrastructure..

This reframes the decision. The question isn’t “JupyterHub or JupyterLab”; those two were never alternatives to begin with. The real question is whether you want to operate notebook infrastructure or use notebooks as a service.

For a solo practitioner, JupyterLab (locally) works well. In addition, JupyterHub (centralized) may work, but will require dedicated DevOps support. You get full control over your stack, and the operational cost is a known quantity you’ve chosen to accept.

For teams where the goal is collaborative data work and nobody wants to be the person maintaining the Hub deployment, Deepnote is the cleaner path. It’s not a replacement for JupyterHub’s architecture; it’s an alternative to needing that architecture in the first place.

Deepnote’s notebook format is also open source and converts to and from .ipynb, so moving work between Jupyter and Deepnote doesn’t create lock-in - all projects can be ported via drag and drop or CLI.

Real-world example: MoneyLion’s journey

MoneyLion, a digital financial platform serving millions of users, needed collaborative data workflows without maintaining complex notebook infrastructure.

Instead of operating JupyterHub internally, they adopted Deepnote to:

• Enable real-time collaboration and shared analytics workflows
• Standardize infrastructure without managing Kubernetes or unstable sessions
• Reduce platform maintenance and repetitive setup overhead

Results: With Deepnote, the team saved about 2 hours per analyst per week by avoiding manual chart setup and reruns, and about 8 hours per month per headcount by retiring JupyterHub Helm chart maintenance, according to their published results.

The key takeaway: the bottleneck wasn’t notebook capability; it was operational complexity and workflow friction.

You can read the full case study here.

Key takeaways

JupyterHub and JupyterLab are not competitors. Hub is the multi-user server. Lab is the single-user interface. They work together by design.

The harder question is whether running JupyterHub is worth the operational cost for your team. If you have the infrastructure expertise and want full control, it’s a solid choice. If what you actually need is multi-user notebooks without the infrastructure, Deepnote gives you that directly; and lets you skip the deployment entirely.

If you look at how the broader ecosystem categorizes these tools, the separation becomes obvious. JupyterLab competes in IDE categories. JupyterHub is infrastructure software. Deepnote is listed among collaborative data science and analytics platforms; a different layer entirely.

Real user comparisons on G2 reinforce that these tools occupy different layers in the ecosystem. JupyterLab and Jupyter Notebook are both ranked in the Python IDE category, where Notebook edges Deepnote slightly in overall rating but Deepnote wins praise for collaboration, team workflows, and ease of sharing. Deepnote’s rating and reviewer comments show frequent mention of real-time collaboration and built-in versioning, features not native to local Jupyter instances. Because Deepnote also appears under broader Analytics and Data Science & ML categories, users signal that it’s not just an IDE but a team-oriented platform; a distinction from Lab’s interface-centric role and Hub’s infrastructure role. In practice, that means many users report strong satisfaction with the Notebook/Lab combo for individual work, but point to Deepnote when collaboration, cross-team reproducibility, and shared results become priorities. (G2)

If this article helped clarify infrastructure vs interface, you might also want the complementary decision: when to default to Notebook vs JupyterLab in the first place.

References

• Project Jupyter | JupyterHub Overview
• JupyterHub Documentation
• JupyterLab Interface Docs
• JupyterLab on JupyterHub
• Zero to JupyterHub on Kubernetes
• The Littlest JupyterHub