The 5 best Google Colab alternatives in 2026

Google Colab has remained a popular choice for data scientists, students, and machine learning enthusiasts because it’s easy to start, easy to share, and accessible from almost anywhere. But as notebook workflows have matured, a wider set of alternatives has emerged; some better for collaboration, some better for persistent compute, and others better suited to specific kinds of projects. Google has updated Colab with an AI-first experience, paid plans, and a Data Science Agent in Colab Enterprise.

That’s why the Google Colab alternatives listed here fall into 3 different buckets. Some try to stay close to the hosted-notebook experience while giving you more persistence or more structure. Some trade notebook polish for cheaper, longer-running GPU infrastructure. Others focus on collaboration, governed sharing, and turning notebooks into something a team can actually ship.

At a glance

Deepnote

Deepnote treats the notebook as the shared runtime and record for both people and agents, not just a place where one person writes code. The same artifact can be edited collaboratively, scheduled, called through an API, connected to Slack, or turned into a deployable data app without switching tools.

Deepnote also ships an MCP server (@deepnote/mcp) and a CLI tool (@deepnote/convert) alongside the notebook itself. The CLI supports bidirectional conversion between Deepnote, Jupyter, Quarto, and marimo (.py) formats. The MCP server makes it possible to wire notebooks into external agents. Deepnote Agent works with project context rather than a single cell, which matters for teams building multi-step pipelines or agentic workflows. VS Code, Cursor, and Windsurf extension means people can work locally before scaling to Deepnote Cloud for collaboration and compute.

Students, teachers, and researchers can apply for the Education plan, which includes nearly all Team plan features at no cost. GPU and higher-spec machines require a paid tier or adding a payment method.

Pros:

• Deepnote Agent reasons across project context and works alongside humans in a real-time collaborative environment, sharing the same execution state..
• Scheduling, API execution, and Slack notifications make it usable for recurring workflows beyond interactive sessions.
• Path from notebook to data apps, dashboards, and scheduled work is built in.
• MCP server and CLI make it composable with external agents and convertible across notebook formats.
• Education plan gives students, teachers, and researchers near-full Team plan features for free.

Cons:

• GPU and higher-spec machines require a paid tier; Education plan users need to add payment for those machines.
• Deepnote isn't a BI tool; if pivot tables and dashboarding are the core use case, something purpose-built for that will serve you better.
• Solo users doing purely exploratory work may find it more structure than they need.

For readers comparing the 2 directly, this Colab vs. Deepnote comparison is a useful internal next step.

Kaggle Notebooks

Kaggle Notebooks are a no-cost Colab alternative when free compute is the main attraction. The real advantage is the ecosystem around it: datasets, competitions, models, packages, and groups all live close to the notebook itself.

Pros:

• Free GPU compute, with tooling that helps users manage active sessions and avoid wasting quota
• Tight integration with public datasets, competitions, and pre-trained models
• A growing reuse story through Kaggle Packages and programmatic access via the Kaggle API and kagglehub

Cons:

• No real path to recurring workflows, scheduled execution, or collaborative editing for internal projects.
• The public ecosystem framing makes it a poor fit for professional internal data teams.

The catch is that Kaggle still makes the most sense when your work benefits from Kaggle’s public ecosystem. It’s excellent for learning, benchmarking, prototyping, and competition-style workflows. It’s a weaker fit when you need private, long-running, team-managed notebook infrastructure.

Amazon SageMaker Studio Lab

Studio Lab is an accessible free JupyterLab environment: no AWS account required, 15GB of persistent storage, CPU and GPU compute (NVIDIA T4), and sessions that can run up to 12 hours. AWS updated it to JupyterLab 4 and it remains available as a standalone product. Studio Lab inherits the structural limitations that have always defined this model: session caps, no multiplayer editing, no scheduling, no path to recurring or agentic execution, and an environment AWS explicitly recommends against using for production workloads.

Studio Lab is intentionally a subset of the broader SageMaker platform: no pipelines, no deployment, no IAM-based access controls, no fine-grained VPC configuration. For anyone who has outgrown Colab and wants a low-friction JupyterLab environment to learn or prototype in, it serves that job. For teams that want collaboration, reproducibility, agentic workflows, or something that can serve as a system of record, the classic notebook constraints it carries make it harder to get there, not easier.

Pros:

• A free JupyterLab 4 environment rather than a custom notebook abstraction
• No AWS account required to get started
• Support for GitHub repos plus a persistent default environment option

Cons:

• No real-time collaboration or multiplayer editing.
• No scheduling, API-triggered execution, or agentic workflow support.
• Reaching non-AWS data sources requires manual configuration.
• A deliberate subset of SageMaker: no pipelines, no deployment, no IAM or VPC controls.

Paperspace

Paperspace Notebooks are a web-based Jupyter IDE with shared persistent storage for long-term development and inter-notebook collaboration, backed by accelerated compute. Windows VMs have been retired; the platform now focuses on Linux-based machines and GPU compute for AI and ML work.

Pros:

• Shared persistent storage for long-term development, not just short-lived runtimes
• Free and paid notebook plans, with configurable auto-shutdown on paid tiers
• A path from free/basic instances to faster GPUs and higher-end instances as needs grow

Cons

• The Paperspace product has been absorbed into DigitalOcean; what’s available for new users has changed materially.
• No real collaboration, shared notebook editing, or agentic workflow support.
• Hourly GPU billing on top of base plan costs can be hard to forecast.

Modal Notebooks

Modal Notebooks makes the most sense when the notebook is a control surface for GPU-backed Python and AI work rather than a team workspace for analysis and recurring workflows. It pairs a shared editor with Modal’s own infrastructure primitives – sandboxes, custom images, volumes, secrets: so the notebook sits directly on top of serverless AI compute. The result is fast startup, automatic idle shutdown, per-second billing, and direct access to Modal GPUs.

Pros:

• Real-time collaborative editing with shared context in the notebook
• Serverless pricing and automatic idle shutdown, which keeps the cost model closer to actual usage
• Access to Modal GPUs and cloud compute from inside the notebook environment

Cons

• No governance controls, audit trails, or identity-aware policy enforcement. It is not designed as an organizational system of record.
• No native path to scheduled, headless, or API-triggered notebook execution in a governed way.
• Per-second billing is efficient for bursty GPU work but harder to forecast for teams with frequent or long-running sessions.

Modal is a better fit than Colab when the notebook is only part of a larger cloud compute workflow, especially for teams already working with GPUs or production-minded Python workloads. It is less of a free learning sandbox than Kaggle or Studio Lab, but much more relevant for users who want collaborative notebooks without stepping away from serious infrastructure.

How to choose the right fit

The easiest way to choose a Google Colab alternative is to start with the reason you’re leaving Colab in the first place.

• For better collaboration: start with Deepnote
• For access to public datasets and a learning-focused environment: start with Kaggle Notebooks
• For ML-heavy workloads, training pipelines, and AWS-native workflows: start with Amazon SageMaker Studio Lab or SageMaker
• For long-running or dedicated GPU jobs: start with Paperspace

There’s also one important caveat: not every Colab user actually needs to switch. If your team already likes Colab and mostly wants stronger governance inside Google Cloud, Colab Enterprise may be closer to an upgrade than a replacement. Google positions it as a collaborative, managed notebook environment with Google Cloud security, and its Data Science Agent story is stronger than many people realize.

FAQs

• What is the best free alternative to Google Colab?
  For most people, the strongest free alternatives are Deepnote, Kaggle Notebooks, and Amazon SageMaker Studio Lab. Deepnote is the strongest option if collaboration matters most, especially for shared notebook work. Kaggle Notebooks is a better fit if you want free experimentation tied to datasets, competitions, and models. Amazon SageMaker Studio Lab makes more sense if you want a more classic JupyterLab-style environment and a gentle path into AWS.

• What is the Google Colab alternative for long-running tasks?
  For long-running GPU work, Modal notebooks and Paperspace are usually better fits than Colab. Paperspace is stronger when you still want a more notebook-centric experience with persistent storage and simpler paid plans.

• What should I use instead of Google Colab for lightweight browser-based notebooks?
  If you want something lightweight and zero-install, JupyterLite is worth a look. It runs entirely in the browser and is a better fit for quick demos, teaching, and small interactive notebook experiences than for heavier cloud-compute workflows.