compare_cohorts
Open Source vs Enterprise: governance from the community, clarity from the sales team
Open-source providers outscore enterprise ones on contracts and governance. Enterprise vendors win on access clarity by nearly 12 points.
Open Source
Each sun is drawn from the cohort's averages: six facet rings, the mean Kin Score and mean agent readiness.
The 1,160 providers tagged open source average 30.8, against 28.0 for 679 tagged enterprise. The facets show two different strengths. Open source leads on contract quality (29.0 against 20.0), operational transparency (25.8 against 19.0) and developer ergonomics (36.7 against 30.1). 37% of open-source providers publish Spectral rules, the highest rate of any cohort on this page, against 24% for enterprise.
Enterprise wins clearly on one facet: access clarity, 32.4 against 20.7. Enterprise vendors publish plans, pricing and sign-up paths. Open-source projects often have none, because the code is free and the commercial offer, if there is one, lives elsewhere. Enterprise also leads on OAuth scopes (13% against 9%) and Arazzo workflows (4% against 2%).
The 2.8-point gap is depth-confounded, with four more artifact directories on the median open-source member. Open-source projects publish their contracts in public repositories, and that is exactly what our crawl finds best.
The distributions
| Open Source | Enterprise | Delta | |
|---|---|---|---|
| Kin Score, mean | 30.8 | 28 | +2.8 |
| Kin Score, median | 30 | 24.8 | +5.2 |
| Kin Score, highest | 95.3 | 88.8 | |
| Kin Score, std dev | 15.8 | 18.6 | |
| Agent readiness, mean | 17.1 | 14.8 | +2.3 |
| Agent readiness, highest | 66.4 | 66.5 |
Delta is Open Source minus Enterprise, as the tool returns it.
Where the providers sit
Open Source
Enterprise
Facet averages
| Facet | Open Source | Enterprise | Delta |
|---|---|---|---|
| Discoverability | 61.7 | 60.7 | +1 |
| Contract Quality | 29 | 20 | +9 |
| Contract Governance | 11 | 9.6 | +1.4 |
| Operational Transparency | 25.8 | 19 | +6.8 |
| Developer Ergonomics | 36.7 | 30.1 | +6.6 |
| Access Clarity | 20.7 | 32.4 | -11.7 |
What they publish
Share of each cohort's providers publishing each artifact. mcp_pct counts any mcp/ artifact including ones API Evangelist derived from the provider OpenAPI; mcp_first_party_pct counts only servers the provider publishes. Prefer the latter.
| Artifact | Open Source | Enterprise | Delta |
|---|---|---|---|
| First-party MCP server | 16% | 15% | +1 |
| Any MCP artifact (incl. derived) | 18% | 16% | +2 |
| OAuth scopes | 9% | 13% | -4 |
| Spectral rules | 37% | 24% | +13 |
| Arazzo workflows | 2% | 4% | -2 |
| Agent skills | 0% | 0% | 0 |
| Security artifact | 97% | 99% | -2 |
How well we know each cohort
The coverage behind the averages. A cohort with a higher catalog gap and fewer artifact directories has been enriched less, so part of any difference is our coverage rather than the market.
| Open Source | Enterprise | |
|---|---|---|
| Declared members | 1187 | 756 |
| Scored (in the averages) | 1160 (98%) | 679 (90%) |
| Thinly covered | 879 | 565 |
| Median artifact directories | 14 | 10 |
| Mean catalog gap (points we could add) | 69.8 | 74.6 |
The full compare_cohorts response
{"a":{"kind":"tag","slug":"open-source","name":"Open Source","members":1187,"stats":{"kind":"tag","slug":"open-source","name":"Open Source","tier":"report","coverage":{"declared_members":1187,"resolved_members":1187,"scored_members":1160,"agent_scored_members":1160,"scored_share_pct":98,"unexamined_members":24,"enrichment_depth":{"mean_catalog_gap":69.8,"median_artifact_dirs":14,"n":1160,"thinly_covered":879,"thinly_covered_of":1160,"thin_coverage_note":"879 of 1160 scored members had under half their catalog-attributable points satisfied at all (catalog_earned / catalog_max < 0.5). They are INCLUDED in the mean above. A large share here means the cohort mean is partly a measurement of how far our enrichment reached, not only of how these providers publish.","note":"Rubric points API Evangelist could add to the average member with no action by the providers, and the median count of artifact directories we hold on them. A cohort with a higher gap and thinner artifacts has been enriched less, so part of any difference between its mean and another cohort's is our coverage rather than the market. Compare two cohorts only when this figure is comparable. It is an upper bound on our backlog: it says who CAN close a gap, not who did."},"unexamined_note":"Excluded from the distribution: these carry a catalog-owned needs_work flag (never enriched, or no resolvable host). They are listed as members and keep their own page and score; they do not vote in the averages, because their score measures our coverage rather than their posture."},"sufficient":true,"kin_score":{"mean":30.8,"median":30,"min":0,"max":95.3,"stdev":15.8},"agent_readiness":{"mean":17.1,"median":19,"min":0,"max":66.4,"stdev":14.6},"band_distribution":{"exemplar":29,"strong":66,"developing":209,"thin":400,"emerging":330,"minimal":126},"facet_averages":{"discoverability":{"mean":61.7,"n":1160},"contract_quality":{"mean":29,"n":1160},"contract_governance":{"mean":11,"n":1160},"operational_transparency":{"mean":25.8,"n":1160},"developer_ergonomics":{"mean":36.7,"n":1160},"access_clarity":{"mean":20.7,"n":1160}},"adoption":{"mcp_pct":18,"mcp_first_party_pct":16,"mcp_note":"mcp_pct counts any mcp/ artifact including ones API Evangelist derived from the provider OpenAPI; mcp_first_party_pct counts only servers the provider publishes. Prefer the latter.","skills_pct":0,"scopes_pct":9,"security_pct":97,"arazzo_pct":2,"rules_pct":37}}},"b":{"kind":"tag","slug":"enterprise","name":"Enterprise","members":756,"stats":{"kind":"tag","slug":"enterprise","name":"Enterprise","tier":"report","coverage":{"declared_members":756,"resolved_members":756,"scored_members":679,"agent_scored_members":679,"scored_share_pct":90,"unexamined_members":69,"enrichment_depth":{"mean_catalog_gap":74.6,"median_artifact_dirs":10,"n":679,"thinly_covered":565,"thinly_covered_of":679,"thin_coverage_note":"565 of 679 scored members had under half their catalog-attributable points satisfied at all (catalog_earned / catalog_max < 0.5). They are INCLUDED in the mean above. A large share here means the cohort mean is partly a measurement of how far our enrichment reached, not only of how these providers publish.","note":"Rubric points API Evangelist could add to the average member with no action by the providers, and the median count of artifact directories we hold on them. A cohort with a higher gap and thinner artifacts has been enriched less, so part of any difference between its mean and another cohort's is our coverage rather than the market. Compare two cohorts only when this figure is comparable. It is an upper bound on our backlog: it says who CAN close a gap, not who did."},"unexamined_note":"Excluded from the distribution: these carry a catalog-owned needs_work flag (never enriched, or no resolvable host). They are listed as members and keep their own page and score; they do not vote in the averages, because their score measures our coverage rather than their posture."},"sufficient":true,"kin_score":{"mean":28,"median":24.8,"min":0,"max":88.8,"stdev":18.6},"agent_readiness":{"mean":14.8,"median":8.6,"min":0,"max":66.5,"stdev":16.3},"band_distribution":{"exemplar":17,"strong":51,"developing":139,"thin":110,"emerging":212,"minimal":150},"facet_averages":{"discoverability":{"mean":60.7,"n":679},"contract_quality":{"mean":20,"n":679},"contract_governance":{"mean":9.6,"n":679},"operational_transparency":{"mean":19,"n":679},"developer_ergonomics":{"mean":30.1,"n":679},"access_clarity":{"mean":32.4,"n":679}},"adoption":{"mcp_pct":16,"mcp_first_party_pct":15,"mcp_note":"mcp_pct counts any mcp/ artifact including ones API Evangelist derived from the provider OpenAPI; mcp_first_party_pct counts only servers the provider publishes. Prefer the latter.","skills_pct":0,"scopes_pct":13,"security_pct":99,"arazzo_pct":4,"rules_pct":24}}},"comparable":true,"delta":{"kin_score_mean":2.8,"kin_score_median":5.2,"agent_readiness_mean":2.3,"facets":{"discoverability":1,"contract_quality":9,"contract_governance":1.4,"operational_transparency":6.8,"developer_ergonomics":6.6,"access_clarity":-11.7},"adoption":{"mcp_pct":2,"mcp_first_party_pct":1,"mcp_note":null,"skills_pct":0,"scopes_pct":-4,"security_pct":-2,"arazzo_pct":-2,"rules_pct":13},"enrichment_depth":{"mean_catalog_gap":-4.8,"median_artifact_dirs":4}},"depth_confounded":true,"depth_note":"Open Source scores 2.8 points higher than Enterprise AND is the better-enriched cohort: 4.8 points less catalog_gap and 4 more artifact directories on the median member. The market difference and our coverage difference point the same way, so this delta cannot be attributed to the markets on its own. Close the enrichment gap, or report both figures together."}
Result read 2026-10-27. Open Source and Enterprise each have their own page with the full roster and leaderboard.
Run this comparison yourself
Everything above came from one call to compare_cohorts. Swap in any two tags, industries, regions, areas or VC portfolios and you get the same answer for your own shortlist. It is an Understanding tool, included in the Understanding and Influence plans, or pay per call.
https://apis.io/mcp:
{"name": "compare_cohorts", "arguments": {"a": "tag:open-source", "b": "tag:enterprise"}}
Over the REST API:
curl "https://apis.io/api/v1/cohorts/compare?a=tag:open-source&b=tag:enterprise" \
-H "X-API-Key: $APIS_IO_KEY"
See the Understanding plan
Connect the MCP server →
More comparisons
Stripe vs Adyen
81.1 vs 71.1 · compare_providers
Twilio vs Vonage
91.6 vs 71.3 · compare_providers
OpenAI vs Anthropic
84.5 vs 89 · compare_providers
Plaid vs MX Technologies
84.6 vs 64.9 · compare_providers
GitHub vs GitLab
79 vs 81 · compare_providers
Shopify vs BigCommerce
83 vs 82.5 · compare_providers
HubSpot vs Salesforce
93.2 vs 88.8 · compare_providers
Payments & Money Movement vs Banking & Open Banking
29.3 vs 25.4 average · compare_cohorts
North America vs Europe
17.8 vs 21.4 average · compare_cohorts
Y Combinator vs Andreessen Horowitz
21.8 vs 23.9 average · compare_cohorts
Healthcare vs Insurance
15.7 vs 18.9 average · compare_cohorts
Artificial Intelligence vs Machine Learning
22.9 vs 24.1 average · compare_cohorts
Cybersecurity & Identity vs Developer Tools & DevOps
31.1 vs 33.4 average · compare_cohorts
MCP vs AI Agents
42.6 vs 31.5 average · compare_cohorts
Work with this as data
Every comparison here is available over the APIs.io API and to AI agents over MCP.