The 7 best Kaggle alternatives for 2026

Kaggle has been a go-to place for learning machine learning in public for years. You get competitions, datasets, notebooks, and a huge community in one place. Over time, that ecosystem has grown to include hundreds of thousands of public datasets and tens of thousands of competitions, ranging from beginner tutorials to large-scale industry challenges. Kaggle today spans notebooks, competitions, hackathons, benchmarks, packages, and newer agent-facing tooling, which means a “Kaggle alternative” can mean very different things depending on what you actually use it for.

If you use Kaggle mostly as a browser notebook, one kind of shortlist makes sense. If the real draw is competitions, or public datasets, or a visible portfolio of models and demos, the answer changes. The list below is built around those different reasons people show up to Kaggle in the first place.

At a glance

Deepnote

Kaggle is built around public datasets, competitions, and community. Deepnote is built around the work that happens after the exploration: collaborative analysis, reproducible projects, and notebooks that can be scheduled, triggered via API, or published as apps without leaving the workspace.

It's the right move when the notebook has stopped being a personal scratchpad and started being something a team needs to run, review, and build on. Deepnote is built around live collaboration, reusable projects, and a more polished path from analysis to something you can actually share with a team.

Pros:

• Deepnote Agent can create and edit SQL, Python, R, and text blocks while staying aware of the full notebook / workspace context.
• File sync keeps a portable .deepnote file in Git, which makes notebook changes much easier to review.
• Data apps let you turn notebook work into something more polished than a public competition notebook.

For readers who want the direct side-by-side angle, the Kaggle vs. Deepnote page is a natural read.

Cons:

• Deepnote is a team-first product; solo users doing purely exploratory or competition work may find it more structure than they need.
• No built-in competition or leaderboard layer; if that's what you're after, Deepnote doesn't replace it.
• GPU and higher-spec machines require a paid tier

Google Colab

If you like Kaggle Notebooks and mostly want another browser tab that gets out of your way, Colab is still the obvious place to start.

Colab remains the closest notebook-style alternative to Kaggle. Google has reworked it into an AI-first experience, and Colab now includes a more agentic collaborator in the notebook workflow. There’s also a nice detail here: Kaggle itself now lets users link an active Colab Pro or Pro+ subscription to get additional weekly GPU hours on Kaggle. That says a lot about how close these 2 products now sit in practice.

Pros:

• Very similar browser-first notebook experience, with easy sharing and no local setup.
• Paid plans add more compute availability, and Pro+ supports background execution for up to 24 hours.
• Colab Enterprise now has a Data Science Agent for notebook-based analysis workflows

Cons:

• Sessions reset on idle or after the runtime limit; any files not explicitly saved to Google Drive are lost, and custom libraries need reinstalling each time.
• Real-time collaborative editing is limited; it's not built around multiple people working in the same notebook simultaneously.
• Everything lives inside Google's environment, which creates meaningful vendor lock-in over time.

Amazon SageMaker Studio Lab

Studio Lab makes sense for people who want a free hosted notebook, but want it to feel a little more like JupyterLab than Kaggle does.

Amazon SageMaker Studio Lab is a free service based on open-source JupyterLab and AWS explicitly says you can use it without signing up for an AWS account. That makes it one of the most straightforward Kaggle alternatives on this list. It is not the full SageMaker platform, which is exactly the point: it gives you a cleaner hosted notebook environment without forcing you into an enterprise ML stack. Custom-installed packages can persist across sessions, and persistent and non-persistent environments are both supported. GitHub integration is available, and it avoids the overhead of the full SageMaker platform entirely.

Pros:

• Free, browser-based JupyterLab 4 with AWS compute behind it.
• Persistent and non-persistent environments are both supported, and custom-installed packages can persist across sessions.
• GitHub and Amazon S3 integrations make it easier to work with external resources than a competition-centric notebook does.

Cons:

• Sessions cap at 12 hours for CPU; AWS explicitly flags Studio Lab as unsuitable for production use.
• No real-time collaboration or multiplayer notebook editing.
• No path to scheduling, API-triggered execution, or recurring automated workflows.

Studio Lab is a better fit than Kaggle when you want a free hosted notebook that feels a bit more like a real workspace and a bit less like a public arena.

Paperspace

Paperspace is for the moment when Kaggle stops feeling like a good place to keep serious notebook work running.

Paperspace Notebooks are a web-based Jupyter IDE with shared persistent storage for long-term development and inter-notebook collaboration, backed by accelerated compute. That is a very different promise from a competition notebook. It makes Paperspace a much better fit for longer-running experiments, more deliberate environment management, and projects you do not want to treat as short-lived sessions.

Pros:

• Shared persistent storage is built in, including team-accessible storage on a cluster.
• It stays close to the familiar Jupyter notebook model instead of introducing a whole new interface.
• Free and paid plans exist, with a broader range of hosted compute choices as projects 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 agent-aware execution.
• Hourly GPU billing on top of base plan costs can be hard to forecast.

For users who like working in notebooks but want more control over storage, sessions, and GPU-backed environments, Paperspace is a more natural next step.

DrivenData

DrivenData belongs here because some people do not want a better notebook. They want another reason to compete.

Kaggle still dominates the public mindshare around data science competitions, but DrivenData is one of the clearest alternatives when the competition layer is what matters most. DrivenData says it runs machine learning competitions with social impact and openly shares prize-winning solutions on GitHub so others can learn from them and build on them. That gives it a noticeably different feel from Kaggle’s broader everything-platform identity.

Pros:

• Competitions are the center of the experience, not just one feature among many.
• The problems skew toward public health, conservation, education, and other mission-driven areas.
• Winning solutions are shared openly, which keeps the learning loop strong.

Cons:

• Tiny catalog of active competitions relative to Kaggle
• No notebook environment, no dataset hosting, no community features beyond the competition context itself.
• A weak fit for anyone who wants more than a competition platform.

If the thing you love about Kaggle is the leaderboard, the pressure, and the chance to learn from strong public solutions, DrivenData is more relevant than another generic notebook service.

Hugging Face

A lot of Kaggle users are really using the platform as a public place to discover datasets, share work, and publish something others can reuse. Hugging Face is often the stronger answer to that workflow and the dataset scale difference is worth stating plainly. The Hub hosts over 500,000 public datasets spanning more than 8,000 languages across NLP, computer vision, audio, time series, and more. Kaggle's catalog is large for a competition platform; it's a different order of magnitude from what the Hub covers, and the dataset types reflect it; Hugging Face is where the raw training data for modern ML actually lives.

Beyond datasets, the Hub hosts over 2 million public models and a Spaces ecosystem where demos and tools are deployed and shared. Spaces are Git-based, rebuild on new commits, and support public, protected, and private visibility. Any Gradio app on Spaces is MCP-compatible, which means it can be called as a tool by an LLM; something Kaggle's notebook environment has no equivalent for.

Pros:

• Dataset catalog is a different order of magnitude from Kaggle, covering modalities and domains competition platforms don't prioritize.
• Models, datasets, and apps are first-class objects, not side effects of notebook or competition use.
• MCP compatibility across Gradio Spaces makes it directly useful for teams building agent tooling.

Cons:

• No competition or leaderboard layer; if that's what you value about Kaggle, Hugging Face doesn't fill it.
• No built-in notebook-first workflow for exploration and analysis; the Hub is for sharing and discovery.
• Free compute through ZeroGPU on Spaces is available but limited; heavier inference requires paid endpoints.

If the appeal of Kaggle is visibility, reuse, and public-facing work, Hugging Face is often the stronger home.

CoCalc

CoCalc is the right fit for readers whose Kaggle usage has always been more academic than competitive. It supports Jupyter Notebooks, LaTeX files, SageMath worksheets, whiteboards, and a shared per-project environment with synchronized file changes and automatic revision history. Teaching infrastructure is genuinely strong, with course management and notebook-based grading support built in things Kaggle doesn't try to serve at all.

Pros:

• Real-time collaboration is built into the document model, not added on later.
• It supports more than just notebooks, including LaTeX and SageMath workflows.
• Teaching infrastructure is unusually strong, with course management and grading tools around Jupyter notebooks.

Cons:

• No GPU compute for serious ML work; it's not built for model training or heavy data processing.
• Small community relative to Kaggle or Colab; fewer examples, fewer integrations, less ecosystem momentum.
• A weak fit for anyone outside academic or research workflows.

CoCalc is not an answer for public ML competitions. It is a very good answer for collaborative academic work that happens to use notebooks.

How to choose the right fit

The fastest way to narrow this down is to ask what part of Kaggle you'd actually miss.

• For team notebooks, AI agents, and more structured collaboration: Deepnote
• For the closest browser-notebook feel: Google Colab.
• For a free JupyterLab-style environment: Amazon SageMaker Studio Lab.
• For competitions and leaderboard-style learning: DrivenData.
• For public models, datasets, and demos: Hugging Face.
• For teaching, courses, and collaborative research: CoCalc.

FAQs

What is the best free alternative to Kaggle?

Deepnote is the strongest option when you want a free notebook environment that can grow beyond solo exploration. It has a free plan, an Education plan for students and researchers, real-time collaboration, project-aware AI, Git-friendly file sync, and a clear path from analysis to shareable data apps and scheduled workflows. That makes it useful when the notebook work needs to be collaborative, persistent, or become something others can use.

What is the Kaggle alternative for reproducible data projects?

Deepnote is a strong fit when a notebook needs to become more than a competition entry or one-off exploration. Its open notebook format, file sync, scheduling, and API execution help teams keep analysis reproducible, reviewable, and reusable. That matters when the work needs to live beyond a public dataset, leaderboard, or individual experiment.

What is the Kaggle alternative for sharing data apps and analysis?

Deepnote is a better fit when the thing you want to share is not just a notebook, but an interactive data app or analysis workflow. Teams can turn notebooks into shareable data apps, connect them to live data, and keep the code, outputs, and context in the same workspace. For model-first demos or public model hosting, Hugging Face can still be useful, but Deepnote is the better fit for data-centric apps and analysis.