Kaggle is a data-science collaboration platform built around competitions, datasets, notebooks, models, and discussion. The figures below describe its reported scale and operating features, with measurement periods retained where the source provides them.
Contents
- Platform scale
- Competitions and formats
- Community competition features
- Timelines and costs
- Problem types and hackathons
- Hosts, research, and collaboration
- Code, models, and platform history
Platform scale
The Meet Kaggle presentation, labeled as of February 2025, reports more than 25 million registered people on the platform. The same presentation reports more than 500,000 public datasets, more than 1.5 million public notebooks, and more than 10,000 models. These are scale indicators for the public Kaggle ecosystem, not a count of activity during a single month.
The deck also highlights more than 500 featured competitions. It separately says Kaggle has hosted more than 500 featured competitions. Those two statements describe the same broad order of magnitude, so they should not be added together. Both figures come from the Meet Kaggle presentation.
Kaggle’s competition host page gives a different user figure: more than 20 million users. Because the page and the February 2025 presentation use different wording and source contexts, the figures are best kept as separate reported measures rather than treated as a single exact total. Source: Kaggle host page.
| Reported measure | Figure | Source or period |
|---|---|---|
| Registered people | 25 million+ | Meet Kaggle, as of February 2025 |
| Users | 20 million+ | Kaggle host page |
| Public datasets | 500,000+ | Meet Kaggle, as of February 2025 |
| Public notebooks | 1.5 million+ | Meet Kaggle, as of February 2025 |
| Models | 10,000+ | Meet Kaggle, as of February 2025 |
| Featured competitions | 500+ | Meet Kaggle, as of February 2025 |
The difference between “registered people” and “users” matters when reading the figures. The supplied source record identifies the presentation as legacy research and not independently verified, so these values should be understood as reported source claims rather than newly audited measurements.
Competitions and formats
Kaggle’s host page lists three competition types: community, featured, and research. This three-part classification describes the platform’s hosting options. The same page says community competitions cost $0 to hosts, while the Meet Kaggle presentation says non-featured community competitions have a maximum prize of $10K.
The host page distinguishes the benefits attached to featured competitions. It says featured competitions can have cash prizes, while community competitions do not. It also says featured competitions receive marketing on Kaggle and social media, technical advice and support, comprehensive preparation for launch, and live competition monitoring. Research competitions, by contrast, have grants available, according to the host page.
Anybody can launch a competition through Kaggle’s self-service community competition platform, according to the community competitions page. That page advertises more than 40 cloneable competitions. Its audience is broad: the Meet Kaggle presentation names educators, small businesses, and machine-learning enthusiasts as three intended audience segments for community competitions.
Across the host page and community page, the platform describes a competition workflow that combines data, code, evaluation, and conversation. Hosts get no-cost compute notebooks, real-time submission scoring, and custom evaluation metrics, according to the host page. These capabilities connect a participant’s submission to the evaluation process while leaving the competition’s discussion and dataset resources in the same environment.
Community competition features
The community competitions page lists eight features: self-service setup, no cost to hosts, dataset hosting, real-time leaderboards, preloaded metrics, discussion forums, automated scoring, and Kaggle Notebooks. The list is useful because it covers both administration and participant-facing infrastructure.
The page specifically identifies discussion forums, automated scoring, real-time leaderboards, preloaded metrics, dataset hosting, and Kaggle Notebooks as supported features. Each is one of the eight features in that page’s list. Self-service setup and no-cost hosting complete the eight-feature description.
The Meet Kaggle presentation gives a closely related six-feature list: dataset hosting, real-time leaderboards, preloaded metrics, discussion forums, automated scoring, and Kaggle Notebooks. The presentation does not include self-service setup or no-cost hosting in that six-item list, although the community page does. This is a difference in presentation scope, not a reason to combine the counts.
The host page also describes six shared competition features: dataset hosting, no-cost compute notebooks, a competition forum, a live leaderboard, real-time scoring, and custom metrics. Comparing the three lists shows that Kaggle’s descriptions emphasize overlapping functions while grouping or naming them differently. For readers evaluating a competition platform, the recurring functions are data access, computational workspace, discussion, leaderboard feedback, and evaluation.
Timelines and costs
The Meet Kaggle presentation reports that typical prediction competitions last about two to three months. For hackathons, the reported setup period is one to four weeks. The same presentation gives a launch-to-close window of one day to six months for hackathons, followed by a judging period of one to six weeks.
The presentation also says judges should take no more than two weeks to finalize and announce winners. That is a recommended upper limit within the judging process, while the broader one-to-six-week figure describes the reported judging range. The two statements address different levels of the same schedule.
The cost figures in the presentation are stated as fees and minimum prizes, and should not be interpreted as a computed total budget. For featured hackathons, it reports $100K in fees, $50K in featured hackathon hosting fees, and a minimum featured hackathon prize of $50K. For featured prediction competitions, it reports $150K in fees, $100K in hosting fees, and a minimum prize of $50K.
| Competition arrangement | Reported timing or financial figure |
|---|---|
| Typical prediction competition | About 2–3 months |
| Hackathon setup | 1–4 weeks |
| Hackathon launch to close | 1 day–6 months |
| Hackathon judging | 1–6 weeks |
| Judges’ finalization target | No more than 2 weeks |
| Non-featured community maximum prize | $10K |
| Featured hackathon fees | $100K |
| Featured hackathon hosting fees | $50K |
| Featured hackathon minimum prize | $50K |
| Featured prediction fees | $150K |
| Featured prediction hosting fees | $100K |
| Featured prediction minimum prize | $50K |
The community competition page separately says self-service competitions are no-cost. The host page says community competitions cost $0 to hosts. These statements concern hosting cost, while the presentation’s featured figures concern featured arrangements and reported fees or prizes.
Problem types and hackathons
The Meet Kaggle presentation lists 13 example prediction problem types. They include pet adoption popularity, Vesuvius scrolls ink detection, sign-language fingerspelling recognition, Great Barrier Reef invasive-species recognition, human vasculature segmentation, resource-gathering allocation optimization, human psychology and temporal-memory games, efficient chess AI, retail sales forecasting, Zillow’s home value, fraud detection, default prediction, and large-language-model text detection.
These 13 examples span recognition, segmentation, forecasting, allocation, games, valuation, fraud, default prediction, and text detection. The list is a set of examples in the presentation, not a claim that these are the only problem types hosted on Kaggle.
The same presentation lists 12 hackathon examples across dataless and data-backed events. It identifies six dataless hackathon examples and six hackathon-with-data examples. The split is therefore even within the 12-example set: six in each category. The source presents these as examples rather than as a complete inventory of all hackathons.
Hackathons also have six listed platform features in the presentation: rich editing, media attachments, community voting, commenting, single or team authorship, and CC BY 4.0 for public writeups. Together, these features describe a format that supports both individual and team work, as well as public presentation of results.
Hosts, research, and collaboration
The host page presents community, featured, and research competitions as distinct options. Community competitions are positioned for educators, small businesses, and machine-learning enthusiasts. Featured competitions add marketing on Kaggle and social media, technical advice and support, comprehensive launch preparation, and live monitoring. Research competitions have grants available.
The community page’s self-service model is described with eight capabilities: self-service setup, no-cost hosting, dataset hosting, real-time leaderboards, preloaded metrics, discussion forums, automated scoring, and Kaggle Notebooks. The page also advertises more than 40 cloneable competitions, giving prospective hosts a stated library of starting points.
The host page adds that competition hosts receive no-cost compute notebooks, real-time submission scoring, and custom evaluation metrics. These three host benefits are distinct from the host-page list of three competition types and from the community page’s eight-feature list. Keeping the categories separate prevents feature counts from being mistaken for counts of competition formats.
The presentation’s hackathon feature list adds a collaboration layer: community voting and commenting sit alongside rich editing and media attachments; authorship can be single or team-based; and public writeups can use CC BY 4.0. The six items describe publishing and participation features, rather than competition infrastructure such as scoring or leaderboards.
Code, models, and platform history
Kaggle says its Meta Kaggle Code release contains raw source code from hundreds of thousands of public Python and R notebook versions. It also says the release spans nearly a decade of Kaggle notebook history. The release is described as a companion to Meta Kaggle, which was originally released in 2016.
Kaggle says the community has shared nearly 1,000 public code examples on top of Meta Kaggle. It also says the Meta Kaggle dataset publishes daily-updated public tables covering competitions, datasets, kernels, discussions, and more. “Daily-updated” describes the source’s update cadence; it does not provide a separate historical count for each table.
On the model side, Kaggle says TensorFlow Hub integration gives access to more than 2,300 TensorFlow models on Kaggle Models. This model figure is separate from the Meet Kaggle presentation’s broader figure of more than 10,000 models, because the sources describe different model groupings.
Kaggle’s winners’ posts dataset says Kaggle launched its first competition in 2010. It says that first competition was won by Jure Zbontar and that the winning solution was a simple linear model. These are historical claims from the winners’ posts dataset, tied to the first competition rather than to current platform scale.
Taken together, the reported figures describe a platform with more than 20 million users on the host page, more than 25 million registered people in the February 2025 presentation, more than 500,000 public datasets, more than 1.5 million public notebooks, and more than 10,000 models. The platform’s documented history also reaches back to a first competition in 2010, while Meta Kaggle was originally released in 2016.