Markdown VS Web Ontology Language
As of 30 October 2026, Web Ontology Language is named by 154 companies and Markdown by 252; Markdown skews to companies under 200 people (48% vs 39%), Web Ontology Language to companies over 1,000 (39% vs 31%).
As of 30 October 2026 · refreshed daily · counts move as websites, DNS and career sites are re-read.
The website-and-DNS footprint is not comparable between a marketing suite and a CRM: Markdown's script and mail records show on hundreds of thousands of small sites, Web Ontology Language rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- Markdown: 2–200 employees
- 48%
- Web Ontology Language: 2–200 employees
- 39%
- Markdown: 1,000+ employees
- 31%
- Web Ontology Language: 1,000+ employees
- 39%
- Postings per company, 90 days
- 1.9 · 3.0
- Website or DNS footprint
- 0 · 0
Who chooses which
Share of each tool's companies in every segment. "Leans" marks a gap of more than two points.
| Segment | Markdown | share | Web Ontology Language | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 2 | 1% | – | – | outside top 12 |
| 2-10 employees | 19 | 8% | 16 | 10% | Web Ontology Language |
| 11-50 employees | 40 | 16% | 25 | 16% | even |
| 51-200 employees | 62 | 25% | 19 | 12% | Markdown |
| 201-500 employees | 32 | 13% | 24 | 16% | Web Ontology Language |
| 501-1000 employees | 19 | 8% | 10 | 6% | even |
| 1001-5000 employees | 36 | 14% | 27 | 18% | Web Ontology Language |
| 5001-10000 employees | 14 | 6% | 7 | 5% | even |
| 10,001+ employees | 28 | 11% | 26 | 17% | Web Ontology Language |
| Segment | Markdown | share | Web Ontology Language | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 148 | 59% | 77 | 50% | Markdown |
| Business & finance services | 33 | 13% | 22 | 14% | even |
| Manufacturing & industry | 26 | 10% | 15 | 10% | even |
| Commerce & trade | 11 | 4% | 2 | 1% | Markdown |
| Public, community & associations | 9 | 4% | 13 | 8% | Web Ontology Language |
| Media & content | 9 | 4% | 7 | 5% | even |
| Consumer services & leisure | 4 | 2% | 5 | 3% | even |
| Health care | 4 | 2% | 2 | 1% | even |
| Property & construction | 3 | 1% | 4 | 3% | even |
| Transport, logistics & rental | 3 | 1% | 2 | 1% | even |
| Hospitality & travel | 1 | 0% | – | – | outside top 12 |
| Utilities & infrastructure | 1 | 0% | 3 | 2% | even |
| Automotive | – | – | 1 | 1% | outside top 12 |
| Segment | Markdown | share | Web Ontology Language | share | Leans |
|---|---|---|---|---|---|
| United States | 166 | 66% | 69 | 45% | Markdown |
| United Kingdom | 22 | 9% | 13 | 8% | even |
| Germany | 19 | 8% | 29 | 19% | Web Ontology Language |
| France | 9 | 4% | 4 | 3% | even |
| Canada | 6 | 2% | 2 | 1% | even |
| Poland | 4 | 2% | 3 | 2% | even |
| Australia | 3 | 1% | – | – | outside top 12 |
| Austria | 2 | 1% | – | – | outside top 12 |
| Belgium | 2 | 1% | 3 | 2% | even |
| Brazil | 2 | 1% | – | – | outside top 12 |
| Czechia | 2 | 1% | – | – | outside top 12 |
| Italy | 2 | 1% | – | – | outside top 12 |
| India | – | – | 4 | 3% | outside top 12 |
| United Arab Emirates | – | – | 3 | 2% | outside top 12 |
| Singapore | – | – | 3 | 2% | outside top 12 |
| BD | – | – | 2 | 1% | outside top 12 |
| Switzerland | – | – | 2 | 1% | outside top 12 |
| Segment | Markdown | share | Web Ontology Language | share | Leans |
|---|---|---|---|---|---|
| Arts media and entertainment | 158 | 26% | – | – | outside top 12 |
| Software engineering | 89 | 15% | 91 | 16% | even |
| Data & AI | 60 | 10% | 242 | 44% | Web Ontology Language |
| IT operations & business systems | 39 | 6% | 24 | 4% | Markdown |
| Product, program & project management | 36 | 6% | 4 | 1% | Markdown |
| Retail & store operations | 23 | 4% | – | – | outside top 12 |
| Marketing & communications | 19 | 3% | 4 | 1% | Markdown |
| General management & consulting | 12 | 2% | 6 | 1% | even |
| Engineering (non-software) | 10 | 2% | 4 | 1% | even |
| Finance & accounting | 9 | 1% | 8 | 1% | even |
| Customer service & success | 7 | 1% | – | – | outside top 12 |
| Education & training | 7 | 1% | – | – | outside top 12 |
| Research and science | – | – | 8 | 1% | outside top 12 |
| Skilled trades and field service | – | – | 8 | 1% | outside top 12 |
| Healthcare clinical | – | – | 5 | 1% | outside top 12 |
| Logistics warehouse and transport | – | – | 5 | 1% | outside top 12 |
The companies behind each column
Companies using Markdown →
Companies using Web Ontology Language →
api specs data formats ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
Web Ontology Language is named by 154 companies in job postings and Markdown by 252; 0 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/markdown-vs-owl)
Read from the source, not self-reported
Every number on this page can be traced to a page we crawled or a posting we read. Full methodology →
- Job postingsRead hourly from career sites. A tool counts when the employer's own text names it as the team's system; both tools can count for one company.
- Website and DNSScripts and embeds on the company's pages, plus SPF, DKIM, MX and CNAME records of its domain that point at the vendor.
- SegmentsCountry, size and industry from the company profile built from its website.
- Not a reviewNo ratings, prices or feature claims. For those, read the vendors.
Markdown vs Web Ontology Language: questions
Which is more used, Markdown or Web Ontology Language?
By companies naming it in their own job postings, Markdown (252) over Web Ontology Language (154). By website-and-DNS footprint, Web Ontology Language (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses Markdown?
48% of Markdown companies have 2 to 200 employees, against 39% for Web Ontology Language. 31% of Markdown companies have over 1,000 employees, against 39% for Web Ontology Language.
Do companies run both?
0 companies name both in their postings, usually Markdown in marketing and Web Ontology Language in sales, or during a migration.
Where do these numbers come from?
From the employer's own text in job postings read hourly from career sites, and from scripts found on company websites in our crawl. No vendor data, no surveys.
Get either list your way
The same data as a search, over MCP and the API, as bulk files, or pushed to you as it changes.
Every company on either tool,
with the person who owns it
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