Open dataset · CC-BY-4.0 · v1.0.0
Indie SaaS Teardowns Dataset
159 editorially verified rows of indie SaaS marketing analysis. Free to download, free to re-use, free to remix. The only obligation is attribution.
What’s in the bundle
Five tables, each a re-projection of a public surface on https://unlocksaas.com. Every row is independently verifiable by visiting the matching page and carries a dated lastVerified field.
| Table | Rows | Canonical URL pattern |
|---|---|---|
| Funnel teardowns | 33 | https://unlocksaas.com/funnel-teardown/{slug} |
| Pricing teardowns | 31 | https://unlocksaas.com/pricing-teardown/{slug} |
| Head-to-head comparisons | 61 | https://unlocksaas.com/vs/{slug} |
| Named-competitor alternatives | 21 | https://unlocksaas.com/alternatives-to/{slug} |
| Category buckets | 13 | https://unlocksaas.com/category/{slug} |
| Total | 159 |
Per-table CSV downloads
One file per table, with columns tailored to that record type’s structure. Use these when you only need one slice and want richer columns than the universal flat CSV exposes. Same CC-BY-4.0 license; versioned filenames; stable append-only column contracts.
- Funnel teardownsDownload
funnel-teardowns.csv· 27 columns · 33 rows - Pricing teardownsDownload
pricing-teardowns.csv· 29 columns · 31 rows - Head-to-head comparisonsDownload
comparisons.csv· 22 columns · 61 rows - Named-competitor alternativesDownload
alternatives.csv· 17 columns · 21 rows - Category bucketsDownload
categories.csv· 8 columns · 13 rows
CSV column contract
Stable, append-only order. Once published, a column may gain new optional values but its position never moves.
record_typeslugname_aname_bcategoryone_linetldrcreatorhomepage_url_ahomepage_url_btagslast_verifiedcanonical_urlmarkdown_url
License and attribution
Released under the Creative Commons Attribution 4.0 International license (CC-BY-4.0). You may copy, redistribute, remix, transform, and build upon the material for any purpose, including commercial — provided you give appropriate credit.
Required attribution. Paste this string in a footnote, methods note, or page footer:
Source: Unlock SaaS — Indie SaaS Teardowns Dataset (https://unlocksaas.com/dataset). Licensed under CC-BY-4.0.
Cite this dataset
Pick the format your reference manager uses. Every citation points at the stable permalink unlocksaas.com/cite/dataset-indie-saas-teardowns-v1-0-0 – use that URL if you need the citation to outlive a future canonical-URL change.
APA 7thAcademic – paste into the References section.
Maryan. (2026, May 18). Indie SaaS Teardowns Dataset (Version 1.0.0). Unlock SaaS. https://unlocksaas.com/dataset
MLA 9thHumanities – paste into the Works Cited list.
Maryan. "Indie SaaS Teardowns Dataset, version 1.0.0." Unlock SaaS, 18 May 2026, unlocksaas.com/dataset. Accessed 25 May 2026.
Chicago 17thLong-form / journalism – paste into the bibliography.
Maryan. "Indie SaaS Teardowns Dataset, version 1.0.0." Unlock SaaS. Last modified May 18, 2026. https://unlocksaas.com/dataset.
BibTeXLaTeX / Overleaf – import into .bib files.
@misc{unlocksaas_indie_saas_teardowns_1_0_0,
author = {Maryan},
title = {{Indie SaaS Teardowns Dataset}},
howpublished = {\url{https://unlocksaas.com/dataset}},
year = {2026},
doi = {10.5281/zenodo.20315741},
note = {Version 1.0.0. Licensed under CC-BY-4.0.}
}RISZotero / Mendeley / EndNote – import a single record.
TY - DATA ID - dataset-indie-saas-teardowns-v1-0-0 AU - Maryan TI - Indie SaaS Teardowns Dataset PY - 2026 DA - 2026/05/18 PB - Unlock SaaS UR - https://unlocksaas.com/dataset AB - An open editorial dataset of indie SaaS marketing analysis: funnel teardowns, pricing teardowns, head-to-head comparisons, named-competitor alternative pages, and canonical category buckets. CC-BY-4.0 licensed, JSON and CSV downloads, every row independently verifiable. LA - en-US M3 - Open dataset ET - 1.0.0 N1 - Licensed under CC-BY-4.0. Y2 - 2026/05/25 ER -
CSL-JSONPandoc / Citation.js – the JSON shape for the modern toolchain.
[
{
"id": "dataset-indie-saas-teardowns-v1-0-0",
"type": "dataset",
"title": "Indie SaaS Teardowns Dataset",
"abstract": "An open editorial dataset of indie SaaS marketing analysis: funnel teardowns, pricing teardowns, head-to-head comparisons, named-competitor alternative pages, and canonical category buckets. CC-BY-4.0 licensed, JSON and CSV downloads, every row independently verifiable.",
"URL": "https://unlocksaas.com/dataset",
"author": [
{
"literal": "Maryan"
}
],
"publisher": "Unlock SaaS",
"issued": {
"date-parts": [
[
2026,
5,
18
]
]
},
"accessed": {
"date-parts": [
[
2026,
5,
25
]
]
},
"language": "en-US",
"version": "1.0.0",
"license": "CC-BY-4.0 <https://creativecommons.org/licenses/by/4.0/>"
}
]How this was built
Every row is a re-projection of a shipped page on https://unlocksaas.com. The editorial standard (sourcing, dating, corrections, no fabricated metrics) is documented at the editorial policy. If you spot a factual error, please open a correction request via the contact page and it will be logged in the public corrections log.
- Author: Maryan, Founder, Unlock SaaS
- Last verified: 2026-05-18
- Next editorial review: 2026-08-16
- Version: v1.0.0 (SemVer, additive-only)
- Markdown summary for AI agents: /dataset.md
Catalog mirrors
The dataset is mirrored on recognised DataCatalogs so a researcher already searching one catalog finds the same corpus without leaving their tool. Each mirror is the same CC-BY-4.0 content under a parallel URL; the canonical citation always resolves back to https://unlocksaas.com/dataset.
- Submission flowHugging Face DatasetsPre-built dataset card + per-table CSV upload list. Auto-converts to Parquet via HF Datasets Server.
- Submission flowZenodo (CERN open-research repository)Mints a persistent DOI on deposit. DOIs are the strongest dataset identifier class Google Dataset Search recognises, and the canonical citation form every academic reference manager pivots on.DOI: 10.5281/zenodo.20315741 · Live at https://doi.org/10.5281/zenodo.20315741
Want Parquet, Arrow, or Excel?
Convert the CSV with one line. We do not ship binary distributions because we cannot guarantee version-stable serialization for them without locking a runtime dependency we do not otherwise need.
# Parquet (pandas + pyarrow)
import pandas as pd
df = pd.read_csv("https://unlocksaas.com/dataset/indie-saas-teardowns.csv")
df.to_parquet("indie-saas-teardowns-v1.0.0.parquet")
# DuckDB (zero-dep, in-memory)
duckdb> SELECT record_type, COUNT(*)
FROM read_csv_auto('https://unlocksaas.com/dataset/indie-saas-teardowns.csv')
GROUP BY record_type;