{"eid":"kaggle","name":"Kaggle","domain":"kaggle.com","resolved_by":"domain","topics":["compute","storage"],"n_records":9,"best_vorder":0,"review":null,"aliases":["Kaggle","Kaggle Benchmarks","Kaggle Datasets (public)","Kaggle Models (distinct from Kaggle Datasets)","Kaggle Notebook outputs (/kaggle/working) — the free side-channel","Kaggle Notebooks","Kaggle Notebooks + Datasets","Kaggle dataset quota-request program (the >200 GB escape hatch)","kaggle notebooks as a server","kaggle.com"],"records":[{"rid":"compute/kaggle-notebooks","card":{"rid":"compute/kaggle-notebooks","eid":"kaggle","topic":"compute","slug":"kaggle-notebooks","provider":"Kaggle Notebooks","url":"https://www.kaggle.com/docs/notebooks","domain":"kaggle.com","category":"notebook runtime","tier":"free Notebooks (CPU, GPU, TPU)","verdict":"qualifies","vorder":0,"grp":"usable","confidence":"documented","binding":"volume-quota","identity_kind":"any-phone","identity_basis":"stated","needs_card":0,"needs_phone":1,"needs_id":0,"needs_institution":0,"country":null,"crypto":null,"interfaces":"rest,cli","egress_metered":null,"per_object_bytes":null,"retention_days":null,"verified_at":"2026-10-24T01:52:00.000Z","verified_basis":"live","checked_on":"2026-10-25","stale_on":"2026-11-24","n_evidence":11,"usd_month":0.0,"usd_per_tb_month":null,"summary":"4 CPU cores and 30 GB RAM, or 1 Tesla P100 with 29 GB RAM, per session; 12 hours of execution for CPU and GPU sessions and 9 hours for TPU; 20 GB of auto-sav...","duplicate_of":null,"n_unknown":1},"record":{"topic":"compute","per_object_cap":{"stated":"not applicable: the unit is a session, not an object"},"interface":["rest","cli"],"interface_detail":"a notebook in the browser; a Jupyter server that Colab can also attach to","tooling":"Jupyter, the Kaggle API for datasets and notebook versions","chaining":"30 GPU hours a week is the largest recurring GPU allowance in this class","limits_note":"the free accelerator is a single P100; TPU v3-8 is also offered","reliability_note":"interactive sessions are reaped after 20 minutes idle, and free GPU capacity queues at busy times","confidence":"documented","verified_at":"2026-10-24T01:52:00.000Z","price_per_tb_month":null,"slug":"kaggle-notebooks","provider":"Kaggle Notebooks","url":"https://www.kaggle.com/docs/notebooks","category":"notebook runtime","free_tier":{"stated":"4 CPU cores and 30 GB RAM, or 1 Tesla P100 with 29 GB RAM, per session; 12 hours of execution for CPU and GPU sessions and 9 hours for TPU; 20 GB of auto-saved disk in /kaggle/working; a weekly GPU quota of 30 hours or more","unit":"hours","metered_on":"wall-clock","source_url":"https://www.kaggle.com/docs/efficient-gpu-usage","notes":"The 30-hour figure is the GPU quota; the 12-hour figure is the per-session cap. 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The notebooks page is the more specific statement, so this record carries 20 minutes and the disagreement is flagged rather than averaged.","extras":{"shape":{"vcpu":4,"ram_gib":30,"disk_gib":20,"gpu_class":"Tesla P100","gpu_count":1,"note":"CPU shape is 4 cores / 30 GB; the GPU shape drops to 29 GB. 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Public total is therefore effectively unlimited per account, and the 200 GB/dataset ceiling is raised on request (see quota-program candidate).","unit":"GB","metered_on":"unstated","source_url":"https://www.kaggle.com/docs/datasets#technical-specifications","read_on":"2026-10-21","notes":null},"per_object_cap":{"stated":"not stated","source_url":"https://www.kaggle.com/docs/datasets#technical-specifications","notes":"not stated"},"retention":{"stated":"not stated","source_url":"https://www.kaggle.com/docs/datasets#technical-specifications","notes":"not stated"},"egress":{"terms":"Free and CDN-backed: the download endpoint 302s to a signed storage.googleapis.com URL (Google Cloud Storage), which is Fastly/Google-fronted. No egress quota documented, no per-GB charge, no auth needed for public datasets.","metered":false},"interface":["rest","rclone","cli"],"interface_detail":"rest","tooling":"kagglehub (pip install kagglehub, official, github.com/Kaggle/kagglehub) — resolves public datasets with NO credentials; kaggle-api CLI (`kaggle datasets download -d owner/slug`); plain HTTPS + curl/wget against /api/v1/datasets/download/<owner>/<slug>; rclone can front the signed GCS URL with an HTTP remote.","chaining":"200 GB x unlimited public datasets x unlimited free accounts. Live proof of the unlimited-public side: account sahangunasekara92 holds 23 public datasets totalling 1,197.7 GB with a largest single dataset of 163.0 GB (API-verified) — i.e. 6x over the 200 GB 'private quota' figure, so the private quota demonstrably does not apply to public datasets. Multiply again by Kaggle Organizations (a dataset can be owned by an org the account belongs to) and by the free quota-request program for >200 GB single datasets.","limits":[],"limits_note":"200 GB/dataset; 50 top-level files per dataset (use a directory tree or a ZIP — Kaggle unzips archives server-side and the unzipped size counts); each new version adds a full copy so versioned datasets consume quota per version and old versions cannot be deleted (documented user complaint, unresolved); dynamic rate limiting on the API (HTTP 429). Dataset ingestion of a >100 GB upload via the web UI/API is flaky — Kaggle staff recommend creating from a remote URL or GCS bucket instead.","identity_cost":{"kind":"institution","detail":"derived from the record's prose, not from an identity field, and no signup was executed: ling 1,197.7 GB with a largest single dataset of 163.0 GB (API-verified) — i.e. 6x over the 200 GB 'private quota' figure, so the private quota demonstrably does not apply to public","country":null},"reliability_note":"Stable, corporate-owned (Google), no history of quota clawback; the failure modes are ingestion flakiness on very large uploads and un-deletable old versions, not data loss.","confidence":"documented","confidence_note":"re-read from read-broker on 2026-10-22 (HTTP 200, 29944 bytes, sha256 1d2b0cd026b1e3a6). 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Quoted: --- title: \"Datasets Documentation\" words: 3998 upstream_status: 200 content_kind: page extraction: none source: provider contextdev title_source: provider final_url: \"https://www.kaggle.com/docs/datasets\" fetched_at: \"2026-10-22T02:59:53.786Z\" --- Kaggle uses cookies from Google to deliver and enhance the quality of its servi"},{"url":"https://www.kaggle.com/docs/datasets#technical-specifications","date":"2026-10-21","proved":"'200GB per dataset limit', '200GB max private datasets (if you exceed this, either make your datasets public or delete unused datasets)', 'A max of 50 top-level files'"},{"url":"https://www.kaggle.com/discussions/product-announcements/512322","date":"2026-10-21","proved":"Kaggle staff, 2 years ago: 'Users now have a limit of 200GB for private models and 200GB for private datasets. Kaggle has also doubled the maximum upload size of public models and datasets from 100GB to 200GB.'"},{"url":"https://www.kaggle.com/api/v1/datasets/download/rounakbanik/pokemon","date":"2026-10-21","proved":"probe: curl -I -> 302 Location: https://storage.googleapis.com:443/kaggle-data-sets/2756/4568/bundle/archive.zip?X-Goog-... (signed, no auth required)"},{"url":"https://www.kaggle.com/api/v1/datasets/download/wanghaohan/imagenetsketch","date":"2026-10-21","proved":"probe: curl -r 0-62914559 -> 206, 62,914,560 bytes in 17.7 s = ~3.5 MB/s; on a 170 GB dataset (sahangunasekara92/av2-sensor-train-group-3) same range -> 206, 104,857,600 bytes in 16.5 s = ~6.3 MB/s, anonymous"},{"url":null,"date":"2026-10-21","proved":"probe: GET /api/v1/datasets/list?user=sahangunasekara92&pageSize=100 -> 23 public datasets, 1,197.7 GB aggregate, max 163.0 GB single dataset"}],"verdict":"qualifies","verified_at":"2026-10-22T00:00:00.000Z","notes":"200 GB per dataset (hard, public or private), 50 top-level files per dataset, 200 GB max total PRIVATE datasets. There is NO documented aggregate cap on public datasets — the docs' 'Technical Specifications' section states only '200GB per dataset limit', '200GB max private datasets', 'A max of 50 top-level files'. 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The open door for conference organisers, quoted: 'As a NeurIPS researcher, we provide additional complimentary compute and support by filling out. Just mention \"NeurIPS 2026\" in the form for expedited approvals.' The platform is self-serve, quoted: 'Getting started is quick and entirely self-serve. You can get started right away at www.kaggle.com/benchmarks'.","unit":"none","metered_on":"wall-clock","source_url":"https://www.kaggle.com/page/neurips-2026","read_on":"2026-10-22","notes":"allowance"},"per_object_cap":{"stated":"not applicable","bytes":null,"source_url":"https://www.kaggle.com/page/neurips-2026","notes":null},"retention":{"stated":"not applicable","days":null,"source_url":"https://www.kaggle.com/page/neurips-2026","notes":null},"egress":{"terms":"not stated","metered":false,"free_allowance":null,"price_per_tb":null,"source_url":"https://www.kaggle.com/page/neurips-2026"},"interface":["rest"],"interface_detail":"inferred from the tooling the provider documents; not probed","tooling":"not stated","chaining":"This is the one route where a solo person can obtain model access to six frontier providers on one quota: the benchmark runs against OpenAI, Google, Anthropic, Grok, Qwen and DeepSeek models from a single Kaggle account, which is otherwise six separate signups with six separate cards. It composes with the free notebook quota in dossier/compute/kaggle-notebooks.json, because the SDK runs inside Kaggle Notebooks.","limits":[{"kind":"write-gated","value":"the compute is attached to a published benchmark rather than to a person: the quota pays for evaluations that run on Kaggle, so it cannot be spent on a training run, and the larger NeurIPS allocation needs an accepted conference submission.","source":"https://www.kaggle.com/page/neurips-2026"}],"limits_note":"the compute is attached to a published benchmark rather than to a person: the quota pays for evaluations that run on Kaggle, so it cannot be spent on a training run, and the larger NeurIPS allocation needs an accepted conference submission.","binding":"write-gated","binding_note":"the compute is attached to a published benchmark rather than to a person: the quota pays for evaluations that run on Kaggle, so it cannot be spent on a training run, and the larger NeurIPS allocation needs an accepted conference submission.","identity_cost":{"kind":"email","detail":null,"country":null},"reliability_note":"no reliability event recorded","confidence":"documented","evidence":[{"url":"https://www.kaggle.com/page/neurips-2026","date":"2026-10-22","proved":"'High compute quota & model access: We provide a very high complimentary compute quota and access to leading models for your evaluations'; 'As a NeurIPS researcher, we provide additional complimentary compute and support by filling out. Just mention \"NeurIPS 2026\" in the form for expedited approvals'; 'Getting started is quick and entirely self-serve'; page 200 through a rendering provider, origin rate-limited with 'Too many requests to GetCurrentUser'"},{"url":"https://neurips.cc/Conferences/2026/CallForCompetitions","date":"2026-10-22","proved":"'NeurIPS 2026 will be hosting a Competition Track, with a dedicated workshop for each accepted competition', read through the search index for neurips.cc"}],"verdict":"qualifies-with-caveat","verified_at":"2026-10-22T00:00:00.000Z","notes":"The amount is unverified by design: 'a very high complimentary compute quota' carries no number on any page read, so the record claims the mechanism and not the size. Three explanations for why the quota exists were considered: it subsidises benchmark supply for Kaggle's leaderboards, it is developer marketing for the model providers, and it replaces engineering work the labs would otherwise do. The page states the first two in its own words ('Managed infrastructure', 'Grow exposure to the Kaggle community'). The gate that matters is that the compute is scoped to evaluations, so a reader should not plan a fine-tune on it.","extras":{"wave2":{"class":"benchmark-programme","free_tier_kind":"allowance","shape":{"vcpu":null,"ram_gib":null,"gpu_class":null,"gpu_count":null}}}},"claims":[{"path":"$.free_tier.stated","kind":"free-quota","num":null,"unit":null,"per":null,"currency":null,"usd":null,"fx_date":null,"parse":"unparsed"},{"path":"$.limits[0].value","kind":"limit:write-gated","num":null,"unit":null,"per":null,"currency":null,"usd":null,"fx_date":null,"parse":"unparsed"}],"unknown":[["$.egress.metered","egress-unknown"],["$.egress.terms","not-stated"],["$.identity_cost.detail","process"],["$.per_object_cap.notes","process"],["$.per_object_cap.stated","not-applicable"],["$.reliability_note","none-observed"],["$.retention.notes","process"],["$.retention.stated","not-applicable"],["$.tooling","not-stated"]],"history":[],"drift":[]},{"rid":"storage/kaggle-dataset-quota-request-program","card":{"rid":"storage/kaggle-dataset-quota-request-program","eid":"kaggle","topic":"storage","slug":"kaggle-dataset-quota-request-program","provider":"Kaggle dataset quota-request program (the >200 GB escape hatch)","url":"https://www.kaggle.com/discussions/product-feedback/346049","domain":"kaggle.com","category":"ml-hub","tier":null,"verdict":"qualifies-with-caveat","vorder":1,"grp":"usable","confidence":"reported","binding":"write-gated","identity_kind":"email","identity_basis":"defaulted","needs_card":null,"needs_phone":null,"needs_id":null,"needs_institution":null,"country":null,"crypto":null,"interfaces":"rest","egress_metered":0,"per_object_bytes":null,"retention_days":null,"verified_at":"2026-10-21T00:00:00.000Z","verified_basis":"inherited","checked_on":"2026-10-21","stale_on":"2026-11-04","n_evidence":2,"usd_month":null,"usd_per_tb_month":null,"summary":"Not a fixed number: Kaggle staff grant additional per-dataset storage quota on request via a short Google Form, explicitly to 'host large, important public d...","duplicate_of":null,"n_unknown":7},"record":{"topic":"storage","slug":"kaggle-dataset-quota-request-program","provider":"Kaggle dataset quota-request program (the >200 GB escape hatch)","url":"https://www.kaggle.com/discussions/product-feedback/346049","category":"ml-hub","free_tier":{"stated":"Not a fixed number: Kaggle staff grant additional per-dataset storage quota on request via a short Google Form, explicitly to 'host large, important public datasets'. This is the documented path past the 200 GB per-dataset ceiling.","unit":"GB","metered_on":"stored-bytes","source_url":"https://www.kaggle.com/discussions/product-feedback/346049","read_on":"2026-10-21","notes":null},"per_object_cap":{"stated":"not stated","source_url":"https://www.kaggle.com/discussions/product-feedback/346049","notes":"not stated"},"retention":{"stated":"not stated","source_url":"https://www.kaggle.com/discussions/product-feedback/346049","notes":"not stated"},"egress":{"terms":"Same free GCS-backed delivery as any other public dataset.","metered":false},"interface":["rest"],"interface_detail":"rest","tooling":"Same as Kaggle Datasets. The request form is forms.gle/jh4uBjtP7CsfH97C9 (linked from the staff post).","chaining":"This is what turns 200 GB/dataset into multi-TB single objects. Kaggle's own advice is to shard by meaningful subset, which is exactly the sharding pattern for a user needs anyway — combine: N shards x 200 GB each (no request needed) OR one request for a multi-TB dataset.","limits":[{"kind":"write-gated","value":"review, prioritised for datasets of 'general interest to our community and the ML rese","source":"https://www.kaggle.com/discussions/product-feedback/346049"}],"limits_note":"Human review, prioritised for datasets of 'general interest to our community and the ML research community'; not a self-service lever; no published ceiling.","identity_cost":{"kind":"email","detail":null,"country":null},"reliability_note":"Grants are discretionary and could in principle be withdrawn, but nothing vanishes on its own.","confidence":"reported","confidence_note":"One page read moves it to documented.","evidence":[{"url":"https://www.kaggle.com/discussions/product-feedback/346049","date":"2026-10-21","proved":"Kaggle staff (Meg Risdal): 'we invite you to fill out this short Google Form ... We will prioritize granting additional storage quota for datasets that are likely to be of general interest'"},{"url":"https://www.kaggle.com/discussions/product-feedback/346049","date":"2026-10-21","proved":"FAQ: 'Our default limits today are 200GB per dataset.'"}],"verdict":"qualifies-with-caveat","verified_at":"2026-10-21T00:00:00.000Z","notes":"Not a fixed number: Kaggle staff grant additional per-dataset storage quota on request via a short Google Form, explicitly to 'host large, important public datasets'. This is the documented path past the 200 GB per-dataset ceiling.","extras":{},"binding":"write-gated","binding_note":"the write is a queue with a person or a rule in it, so the upload rate is set by somebody else"},"claims":[{"path":"$.free_tier.stated","kind":"free-quota","num":200.0,"unit":"GB","per":null,"currency":null,"usd":null,"fx_date":null,"parse":"ok"},{"path":"$.limits[0].value","kind":"limit:write-gated","num":null,"unit":null,"per":null,"currency":null,"usd":null,"fx_date":null,"parse":"unparsed"}],"unknown":[["$.free_tier.notes","process"],["$.identity_cost.detail","defaulted"],["$.identity_cost.kind","identity-defaulted"],["$.per_object_cap.notes","not-extracted"],["$.per_object_cap.stated","not-extracted"],["$.retention.notes","not-extracted"],["$.retention.stated","not-extracted"]],"history":[],"drift":[]},{"rid":"storage/kaggle-models","card":{"rid":"storage/kaggle-models","eid":"kaggle","topic":"storage","slug":"kaggle-models","provider":"Kaggle Models (distinct from Kaggle Datasets)","url":"https://www.kaggle.com/models","domain":"kaggle.com","category":"model-hub","tier":null,"verdict":"qualifies-with-caveat","vorder":1,"grp":"usable","confidence":"reported","binding":"per-object-cap","identity_kind":"email","identity_basis":"stated","needs_card":0,"needs_phone":0,"needs_id":0,"needs_institution":0,"country":null,"crypto":null,"interfaces":"rest,git","egress_metered":0,"per_object_bytes":200000000000,"retention_days":null,"verified_at":"2026-10-21T00:00:00.000Z","verified_basis":"inherited","checked_on":"2026-10-21","stale_on":"2026-11-04","n_evidence":5,"usd_month":null,"usd_per_tb_month":null,"summary":"200 GB for private models, alongside 200 GB for private datasets — from Kaggle's own product announcement 'Doubling of Private Quota and Public File Size Lim...","duplicate_of":null,"n_unknown":6},"record":{"topic":"storage","slug":"kaggle-models","provider":"Kaggle Models (distinct from Kaggle Datasets)","url":"https://www.kaggle.com/models","category":"model-hub","free_tier":{"stated":"200 GB for private models, alongside 200 GB for private datasets — from Kaggle's own product announcement 'Doubling of Private Quota and Public File Size Limit': 'Users now have a limit of 200GB for private models and 200GB for private datasets. Kaggle has also doubled the maximum upload size of public models and datasets from 100GB to 200GB.' Public models and public datasets have a 200 GB per-item ceiling and no published aggregate cap.","unit":"GB","metered_on":"unstated","source_url":"https://www.kaggle.com/product-announcements/512322","read_on":"2026-10-21","notes":null},"per_object_cap":{"stated":"File Size Limit': 'Users now have a limit of 200GB","bytes":200000000000,"source_url":"https://www.kaggle.com/product-announcements/512322","notes":"not stated"},"retention":{"stated":"not stated","source_url":"https://www.kaggle.com/product-announcements/512322","notes":"not stated"},"egress":{"terms":"Free — files come from Kaggle's Google-hosted CDN (storage.googleapis.com) with no egress meter. Downloads require a Kaggle API token.","metered":false},"interface":["rest","git"],"interface_detail":"rest + git","tooling":"kagglehub and the Kaggle API. GET /api/v1/models/google/bert/get returned 200, 72,158 bytes. Datasets: /api/v1/datasets/list returns 200 JSON with totalBytesNullable per dataset. Note the upload side is narrower than the read side — 'The ability to upload models via the Kaggle API was introduced to a select group of Kaggle accounts starting in version 1.5.18', and kagglehub caps at 50 files per repo and auto-zips beyond that.","chaining":"Models and datasets are separate quota pools, so the ceiling is 200 GB private models + 200 GB private datasets + 200 GB per public model × N public models + 200 GB per public dataset × N public datasets. Public items are the real multiplier. Each model can carry multiple variations and frameworks, but they share the model's 200 GB.","limits":[{"kind":"per-object-cap","value":"File Size Limit': 'Users now have a limit of 200GB","source":"https://www.kaggle.com/product-announcements/512322"}],"limits_note":"per-object-cap 200 GB and 50 top-level files per dataset — 'A max of 50 top-level files (if you have more, use a directory structure and upload an archive)'. Uploading via the API is write-gated for most accounts. Practical caveat from the docs: archives are uncompressed server-side so their contents are usable in Notebooks, which means the 200 GB is measured on the expanded tree.","identity_cost":{"kind":"email","detail":"email","country":null},"reliability_note":"Kaggle is Google-owned and long-lived; the risk is policy drift on public storage, not disappearance. The 200 GB private pool is enforced hard ('if you exceed this, either make your datasets public or delete unused datasets').","confidence":"reported","confidence_note":"One page read moves it to documented.","evidence":[{"url":"https://www.kaggle.com/product-announcements/512322","date":"2026-10-21","proved":"opened: 'Users now have a limit of 200GB for private models and 200GB for private datasets. Kaggle has also doubled the maximum upload size of public models and datasets from 100GB to 200GB.' Both numbers on one page."},{"url":"https://www.kaggle.com/docs/datasets","date":"2026-10-21","proved":"opened, Technical Specifications: '200GB per dataset limit'; '200GB max private datasets (if you exceed this, either make your datasets public or delete unused datasets)'; 'A max of 50 top-level files'; archives are uncompressed on their side."},{"url":"https://www.kaggle.com/api/v1/models-onboarding-packet","date":"2026-10-21","proved":"surfaced: 'The ability to upload models via the Kaggle API was introduced to a select group of Kaggle accounts starting in version 1.5.18'. Read path is open, write path is not — rule 2 in reverse."},{"url":null,"date":"2026-10-21","proved":"PROBED: curl '/api/v1/models/list?search=bert&pageSize=1' -> 200, 184,016 bytes, real model objects. curl '/api/v1/models/google/bert/get' -> 200, 72,158 bytes. curl '/api/v1/datasets/list?search=titanic&pageSize=2' -> 200 with totalBytesNullable present."},{"url":"https://github.com/Kaggle/kagglehub/issues/116","date":"2026-10-21","proved":"opened: 'We have increased the limit on our backend to 50 files. If you are uploading more than 50 files, than kagglehub will automatically create a zip for you.' Confirms the 50-file ceiling is live."}],"verdict":"qualifies-with-caveat","verified_at":"2026-10-21T00:00:00.000Z","notes":"200 GB for private models, alongside 200 GB for private datasets — from Kaggle's own product announcement 'Doubling of Private Quota and Public File Size Limit': 'Users now have a limit of 200GB for private models and 200GB for private datasets. Kaggle has also doubled the maximum upload size of public models and datasets from 100GB to 200GB.' Public models and public datasets have a 200 GB per-item ceiling and no published aggregate cap.","extras":{},"binding":"per-object-cap","binding_note":"the per-object cap sets the shard size and therefore the object count; it bounds the total more tightly than any headline quota"},"claims":[{"path":"$.free_tier.stated","kind":"free-quota","num":200.0,"unit":"GB","per":null,"currency":null,"usd":null,"fx_date":null,"parse":"ok"},{"path":"$.limits[0].value","kind":"limit:per-object-cap","num":200.0,"unit":"GB","per":null,"currency":null,"usd":null,"fx_date":null,"parse":"ok"}],"unknown":[["$.evidence[3].url","private-url"],["$.free_tier.notes","process"],["$.per_object_cap.notes","not-extracted"],["$.retention.notes","not-extracted"],["$.retention.stated","not-extracted"],["$.tooling","partly-held"]],"history":[],"drift":[]},{"rid":"storage/kaggle-notebook-outputs-the-free-side-channel","card":{"rid":"storage/kaggle-notebook-outputs-the-free-side-channel","eid":"kaggle","topic":"storage","slug":"kaggle-notebook-outputs-the-free-side-channel","provider":"Kaggle Notebook outputs (/kaggle/working) — the free side-channel","url":"https://www.kaggle.com/docs/notebooks","domain":"kaggle.com","category":"ml-hub","tier":null,"verdict":"qualifies-with-caveat","vorder":1,"grp":"usable","confidence":"reported","binding":"none","identity_kind":"email","identity_basis":"defaulted","needs_card":null,"needs_phone":null,"needs_id":null,"needs_institution":null,"country":null,"crypto":null,"interfaces":"rest,cli","egress_metered":0,"per_object_bytes":null,"retention_days":null,"verified_at":"2026-10-21T00:00:00.000Z","verified_basis":"inherited","checked_on":"2026-10-21","stale_on":"2026-11-04","n_evidence":2,"usd_month":null,"usd_per_tb_month":null,"summary":"Up to 20 GB of output per notebook version saved to /kaggle/working, and every 'Save & Run All' creates a new version. Outputs are themselves addable as data...","duplicate_of":null,"n_unknown":6},"record":{"topic":"storage","slug":"kaggle-notebook-outputs-the-free-side-channel","provider":"Kaggle Notebook outputs (/kaggle/working) — the free side-channel","url":"https://www.kaggle.com/docs/notebooks","category":"ml-hub","free_tier":{"stated":"Up to 20 GB of output per notebook version saved to /kaggle/working, and every 'Save & Run All' creates a new version. Outputs are themselves addable as dataset inputs to any future notebook. No documented cap on the number of notebook versions or notebooks per account.","unit":"GB","metered_on":"unstated","source_url":"https://www.kaggle.com/docs/notebooks","read_on":"2026-10-21","notes":null},"per_object_cap":{"stated":"not stated","source_url":"https://www.kaggle.com/docs/notebooks","notes":"not stated"},"retention":{"stated":"| Outputs persist indefinitely and are downloadable years later; the flaky part is the commit step (a timed-out run loses the output).","source_url":"https://www.kaggle.com/docs/notebooks","notes":"not stated"},"egress":{"terms":"Free download of notebook output files from the version's Output tab, same GCS-backed delivery.","metered":false},"interface":["rest","cli"],"interface_detail":"rest","tooling":"kaggle CLI (`kaggle kernels output <owner>/<kernel> -p ./dir`) and kagglehub; the notebook itself runs on free P100/TPU compute so the write happens server-side with no upload from the client at all.","chaining":"20 GB per notebook version. Notebook versions, notebooks and accounts each carry no stated limit. The reachable total therefore scales with versions and notebooks per account, and a second account carries a second allowance. The whole write path is server-side (the data is produced or fetched inside the notebook, so there is no client uplink bottleneck). This is the standard community alternative route around the 200 GB private quota: a Kaggle forum answer describes it as 'create a new notebook from your dataset and copy files to /kaggle/working'.","limits":[],"limits_note":"20 GB per version is hard; 9 h / 12 h session limits per run; output is only persisted on commit ('Save & Run All'), not on interactive save; GPU/TPU hours are rationed (~30 h/week) but CPU-only runs are not the constraint for pure storage.","identity_cost":{"kind":"email","detail":null,"country":null},"reliability_note":"Outputs persist indefinitely and are downloadable years later; the flaky part is the commit step (a timed-out run loses the output).","confidence":"reported","confidence_note":"One page read moves it to documented.","evidence":[{"url":"https://www.kaggle.com/docs/notebooks","date":"2026-10-21","proved":"'Up to 20 GBs of output from a Notebook may be saved to disk in /kaggle/working. This data is saved automatically and you can then reuse that data in any future Notebook'"},{"url":"https://www.kaggle.com/competitions/us-patent-phrase-to-phrase-matching/discussion/325968","date":"2026-10-21","proved":"community: 'Use kaggle kernels to keep the datasets. It's unlimited and ofcourse free. Just create a new notebook from your dataset and copy files to /kaggle/working'"}],"verdict":"qualifies-with-caveat","verified_at":"2026-10-21T00:00:00.000Z","notes":"Up to 20 GB of output per notebook version saved to /kaggle/working, and every 'Save & Run All' creates a new version. Outputs are themselves addable as dataset inputs to any future notebook. No documented cap on the number of notebook versions or notebooks per account.","extras":{},"binding":"none","binding_note":"no constraint in the record binds at this scale; the record carries no limit of a kind the vocabulary names"},"claims":[{"path":"$.free_tier.stated","kind":"free-quota","num":20.0,"unit":"GB","per":null,"currency":null,"usd":null,"fx_date":null,"parse":"ok"}],"unknown":[["$.free_tier.notes","process"],["$.identity_cost.detail","defaulted"],["$.identity_cost.kind","identity-defaulted"],["$.per_object_cap.notes","not-extracted"],["$.per_object_cap.stated","not-extracted"],["$.retention.notes","not-extracted"]],"history":[],"drift":[]},{"rid":"storage/kaggle-notebooks-datasets","card":{"rid":"storage/kaggle-notebooks-datasets","eid":"kaggle","topic":"storage","slug":"kaggle-notebooks-datasets","provider":"Kaggle Notebooks + Datasets","url":"https://www.kaggle.com","domain":"kaggle.com","category":"iaas","tier":null,"verdict":"qualifies-with-caveat","vorder":1,"grp":"usable","confidence":"reported","binding":"none","identity_kind":"card","identity_basis":"defaulted","needs_card":1,"needs_phone":null,"needs_id":null,"needs_institution":null,"country":null,"crypto":null,"interfaces":"rest,rclone,cli","egress_metered":0,"per_object_bytes":null,"retention_days":null,"verified_at":"2026-10-21T00:00:00.000Z","verified_basis":"inherited","checked_on":"2026-10-21","stale_on":"2026-11-04","n_evidence":4,"usd_month":null,"usd_per_tb_month":null,"summary":"Notebooks: '20 Gigabytes of auto-saved disk space (/kaggle/working)' per notebook version. Datasets: '200GB per dataset limit', '200GB max private datasets' ...","duplicate_of":null,"n_unknown":6},"record":{"topic":"storage","slug":"kaggle-notebooks-datasets","provider":"Kaggle Notebooks + Datasets","url":"https://www.kaggle.com","category":"iaas","free_tier":{"stated":"Notebooks: '20 Gigabytes of auto-saved disk space (/kaggle/working)' per notebook version. Datasets: '200GB per dataset limit', '200GB max private datasets' — the 200 GB private cap is the only account-level storage number published; public datasets are capped per-dataset, not per-account.","unit":"GB","metered_on":"stored-bytes","source_url":"https://www.kaggle.com/docs/notebooks","read_on":"2026-10-21","notes":null},"per_object_cap":{"stated":"not stated","source_url":"https://www.kaggle.com/docs/notebooks","notes":"not stated"},"retention":{"stated":"Notebook output survives session end as a versioned artifact — the closest thing here to real object storage — but it is write-once per version, and you cannot run a listener (no inbound ports, no server).","source_url":"https://www.kaggle.com/docs/notebooks","notes":"not stated"},"egress":{"terms":"Public dataset/notebook downloads are served without auth and without a published egress meter.","metered":false},"interface":["rest","rclone","cli"],"interface_detail":"rest","tooling":"`kaggle` CLI + REST API (`/api/v1/datasets/download/...`, `/api/v1/kernels/output/...`); `kagglehub` Python client. No rclone backend, but the CLI is scriptable and gsutil-style uploads work through the API.","chaining":"20 GB per notebook output x unlimited notebook versions (each version is an immutable snapshot, addressable and re-mountable as 'Input' in another notebook) => effectively hundreds of GB per account. Plus unlimited public datasets at 200 GB each. Both layers multiply by N free accounts.","limits":[],"limits_note":"12 h session for CPU/GPU notebooks, 9 h for TPU; output only auto-saves to /kaggle/working when you 'Save & Run All'; 50 top-level files per dataset.","identity_cost":{"kind":"card","detail":"inferred from the category 'iaas'; the source record states no identity cost, so this is a class assumption, not evidence","country":null},"reliability_note":"Notebook output survives session end as a versioned artifact — the closest thing here to real object storage — but it is write-once per version, and you cannot run a listener (no inbound ports, no server).","confidence":"reported","confidence_note":"One page read moves it to documented.","evidence":[{"url":"https://www.kaggle.com/docs/notebooks","date":"2026-10-21","proved":"'20 Gigabytes of auto-saved disk space (/kaggle/working)'; '12 hours execution time for CPU and GPU notebook sessions and 9 hours for TPU'"},{"url":"https://www.kaggle.com/docs/datasets","date":"2026-10-21","proved":"'200GB per dataset limit'; '200GB max private datasets'"},{"url":"https://www.kaggle.com/product-announcements/512322","date":"2026-10-21","proved":"'limit of 200GB for private models and 200GB for private datasets'; public upload size doubled to 200GB"},{"url":null,"date":"2026-10-21","proved":"probe: GET /api/v1/datasets/download/uciml/iris?datasetVersionNumber=2 -> 302 then 200, Content-Type: application/zip, Content-Length: 3687 (anonymous, no auth)"}],"verdict":"qualifies-with-caveat","verified_at":"2026-10-21T00:00:00.000Z","notes":"Notebooks: '20 Gigabytes of auto-saved disk space (/kaggle/working)' per notebook version. 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