The Best Jupyter Notebooks Products in 2025

Jupyter is an open-source, computational notebook used by ML researchers and data scientists for interactive, exploratory computing. A spinoff of a predecessor, IPython, Jupyter was named for the languages it supported (Julia, Python, and R) and has since seen meteoric growth, with ~200,000 Jupyter notebooks available on GitHub in 2015 to 2.5 million in 2018 and 10 million in January 2021.

Technically, Jupyter notebooks are made up of two components: (i) a web frontend where users write code in cells and (ii) backend ‘kernels’ which run the code and return the results.

Jupyter notebooks can be served locally, but cloud-hosted versions exist as well. Despite their power and popularity, many cloud-hosted Jupyter providers are not well-suited for the kinds of compute-heavy tasks that modern AI workflows require. Common pain points include slow startup times, lack of access to GPUs, idle kernels that rack up unnecessary costs, and collaborative features that feel like an afterthought.

In this article, we’ll evaluate how the top cloud Jupyter notebook providers—Google Colab, AWS SageMaker, Databricks Notebooks, Deepnote, and a new kid on the block (psst: it’s Modal)—address these different challenges.

1. Performance

Notebooks can often take a long time to start executing code, especially if they’re using a GPU kernel or building large images. This both incurs idle time costs and reduces researchers’ iteration speed.

Google Colab

Basic Google Colab kernels usually start up in a few seconds. This can be a bit slower with GPU kernels, especially during peak usage hours when there’s resource contention with other users. For notebooks using large images, time-to-execution can take many minutes.

AWS SageMaker Studio

Initial startup times for new projects take quite a while—several minutes at the minimum. Subsequent launch times are much faster, if you’re using a basic machine. Similar to Colab, selecting GPU machines or using large ML images will extend startup times each time to several minutes or more.

Databricks

Databricks Notebooks face the steepest startup costs, requiring 4-5 minutes for cluster initializations (sometimes more). While this is more acceptable for launching large-scale, distributed workloads, it creates friction for exploratory work where you might want to quickly test an idea.

Deepnote

Deepnote notebooks take less than a minute to boot up—”20-30 seconds” was cited by a company in one of Deepnote’s case studies. Notebooks using default environments leverage pre-warmed instances to make startup times fast. Custom environments increase this startup time.

Modal Notebooks

Modal Notebooks are built on top of Modal’s custom container filesystem infrastructure, which enables lightning-fast boot times. Kernels launch in under 5 seconds, on arbitrary images and hardware ranging up to 256 vCPUs and 8 H100/B200 GPUs. Even images containing heavy ML dependencies can be booted up in a fraction of the time compared to typical Docker-based systems.

The difference becomes stark when you consider that research workflows involve constant iteration. A 3-minute startup time might seem acceptable until you realize you’ll hit it dozens of times in a productive research session.

2. Compute options

Today, AI research requires access to powerful GPUs to run generative AI models. Ideally, researchers should be able to move fluidly between different CPU and GPU options as part of their explorations.

Google Colab

As of October 2025, on Colab’s free tier, users can access v5e TPUs and T4 GPUs. On this tier, SLAs on GPU access are not guaranteed, and the types of GPUs available are not guaranteed either.

Users can purchase compute units to guarantee access to compute, with pricing ranging from $0.41 to $4.71 per GPU hour depending on the model. This also enables access to more powerful GPUs, though users cannot attach multiple GPUs to a notebook, and the most powerful GPU that’s available is the A100.

Note that there’s more flexible GPU options in Vertex AI. Vertex AI is Google’s fully-managed AI development platform, which comes with a couple different notebook products.

AWS SageMaker Studio

Several GPU types are supported in SageMaker. Note that not all GPUs come in flexible configurations. If you want to use an A100 (p4d instance on AWS), you have to use 8 at once! This incurs a significant amount of wasted expense, especially for the priciest GPU types.

Keep in mind that the most powerful GPUs are also locked behind a manual quota request system, which hampers development velocity.

Databricks

Databricks provides full GPU families—exact instance types are dependent on which underlying hyperscaler you use Databricks on. If you’re using Databricks on AWS, for example, you may still encounter challenges with GPU configuration flexibility and having to use larger instances than what you actually need.

Deepnote

Deepnote offers limited GPU options on team and enterprise plans with two current configurations: a K80 GPU option with 4 vCPUs and 64 GB memory, and a V100 GPU with 16GB VRAM, 8vCPUs, and 64 GB memory. GPU selection can be changed at the notebook level within projects. For more powerful GPU requirements, users need to work with sales.

Modal Notebooks

Modal Notebooks give you full flexibility on accessing most powerful GPU types without having to request quota. You can run anything from 0.125 CPUs to 8 NVIDIA A100/H100/B200 GPUs. GPUs are available in configurations of 1 to 8 so you don’t have to overcommit to compute you won’t use. You can also switch hardware type instantly without long boot-up times in between.

For CPU notebooks, you can burst above configured CPU usage and only pay for active compute cycles, so there’s no need to guess your research requirements upfront.

3. Collaboration

True collaborative research requires more than the ability to share notebook files; it demands real-time interaction, shared computational environments, and seamless handoffs between team members.

Google Colab

Colab notebooks can be shared directly with other users, but they don’t support simultaneous editing.

AWS SageMaker Studio

Real-time coediting of notebooks is supported.

Databricks

Real-time coediting of notebooks is supported.

DeepNote

Notebooks can be shared, but multiple collaborators cannot write into the same block at the same time.

Modal Notebooks

Real-time coediting of notebooks is supported, with multiple cursors, live presence indicators, and seamless real-time interaction.

The collaboration extends beyond editing to the computational environment itself. Because Modal Notebooks share Volumes and Secrets across the Modal workspace, research results are automatically reproducible regardless of who runs the notebooks.

4. Cost Structure

Cost predictability matters for research teams, but many cloud notebook platforms have opaque pricing models that make budgeting difficult and optimization nearly impossible.

Google Colab

Colab has a free tier, but resources aren’t guaranteed on that tier. To guarantee access, users can purchase compute units to get prioritized access to CPU or GPU resources. On the pay as you go tier, compute units cost $9.99 for 100 units. More powerful compute options consume more units, but Google doesn’t publish rates per instance type.

Colab also has addition paid subscription tiers (Pro, Pro+, Enterprise), which come with credits included and priority access to premium GPUs.

AWS SageMaker Studio

AWS SageMaker Studio follows a traditional cloud pricing model, where different instance types come with different hourly costs. SageMaker instances come at a premium compared to the equivalent EC2 instances. For example, a p4d.24xlarge (8xA100) in us-east-2 is $25.25/hr in SageMaker, but $21.96 in EC2.

Beyond the unit prices, it’s important to note that SageMaker instances aren’t serverless, so you’re incurring costs as instances spin up and down. In addition, many of the more powerful GPUs only come in larger configurations, which can also lead to low utilization and cost inefficiency.

Databricks

Databricks has its own pricing abstractions, called DBUs. Interactive notebook workloads cost $0.40-0.55 per DBU plus underlying EC2 costs. The rate of DBU consumption increases the more powerful the instance type you choose. As an example, using a p4d.24xlarge instance would cost $24.45/hr (DBUs) plus $21.96/hr (EC2) = $46.41/hr.

DeepNote

DeepNote pricing is more transparent. It has a generous free tier (3 editors, unlimited basic machines), and a team plan at $39 per editor per month which includes $280 worth of CPU and $50 worth of GPU. Pricing for overage usage is not published.

Modal Notebooks

Modal Notebooks use a serverless pricing model. You only pay when kernels are actively running, with configurable idle timeouts that can scale to zero when not in use. GPU costs range from $0.000164/second (T4) to $0.001736/second (B200), with the free Starter plan including $30 in credits and Team plan including $100 in credits.

The serverless model eliminates the biggest source of cloud notebook cost waste: idle instances. Traditional platforms charge for the entire time your instance is allocated, with the onus on the user to manually turn instances on and off. Who amongst us hasn’t forgotten to shut off an expensive notebook kernel?

Choosing the right cloud notebook provider

Cloud versions of Jupyter Notebooks have existed for a while now, but AI/ML research needs are changing rapidly. Powerful compute instances are needed for the most cutting edge exploratory AI work, and notebook products need to make it easy to work with GPUs and hefty ML environments.

For basic exploratory data work, Colab is by far the most prevalent choice, especially because of its generous free tier.

For compute-intensive explorations that require powerful GPUs, we think we have the most performant notebook product on the market. Sub-5-second cold starts even for custom images make computational experimentation as fluid as text editing. Access to our global compute pool ensures that you can always get GPUs with no wait, in whatever configuration you need. Finally, automatic idle shutdown eliminates cost anxiety around leaving instances running.