JSON Schema VS Web Ontology Language
As of 30 October 2026, Web Ontology Language is named by 154 companies and JSON Schema by 127; JSON Schema skews to companies under 200 people (34% vs 39%), Web Ontology Language to companies over 1,000 (39% vs 35%).
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: JSON Schema'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.
- JSON Schema: 2–200 employees
- 34%
- Web Ontology Language: 2–200 employees
- 39%
- JSON Schema: 1,000+ employees
- 35%
- 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 | JSON Schema | share | Web Ontology Language | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 5 | 4% | – | – | outside top 12 |
| 2-10 employees | 6 | 5% | 16 | 10% | Web Ontology Language |
| 11-50 employees | 19 | 15% | 25 | 16% | even |
| 51-200 employees | 18 | 14% | 19 | 12% | even |
| 201-500 employees | 20 | 16% | 24 | 16% | even |
| 501-1000 employees | 15 | 12% | 10 | 6% | JSON Schema |
| 1001-5000 employees | 15 | 12% | 27 | 18% | Web Ontology Language |
| 5001-10000 employees | 8 | 6% | 7 | 5% | even |
| 10,001+ employees | 21 | 17% | 26 | 17% | even |
| Segment | JSON Schema | share | Web Ontology Language | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 68 | 54% | 77 | 50% | JSON Schema |
| Business & finance services | 23 | 18% | 22 | 14% | JSON Schema |
| Manufacturing & industry | 12 | 9% | 15 | 10% | even |
| Public, community & associations | 5 | 4% | 13 | 8% | Web Ontology Language |
| Media & content | 5 | 4% | 7 | 5% | even |
| Health care | 3 | 2% | 2 | 1% | even |
| Utilities & infrastructure | 3 | 2% | 3 | 2% | even |
| Automotive | 2 | 2% | 1 | 1% | even |
| Consumer services & leisure | 2 | 2% | 5 | 3% | even |
| Transport, logistics & rental | 2 | 2% | 2 | 1% | even |
| Commerce & trade | 1 | 1% | 2 | 1% | even |
| Hospitality & travel | 1 | 1% | – | – | outside top 12 |
| Property & construction | – | – | 4 | 3% | outside top 12 |
| Segment | JSON Schema | share | Web Ontology Language | share | Leans |
|---|---|---|---|---|---|
| United States | 90 | 71% | 69 | 45% | JSON Schema |
| United Kingdom | 12 | 9% | 13 | 8% | even |
| Australia | 4 | 3% | – | – | outside top 12 |
| Canada | 4 | 3% | 2 | 1% | even |
| Brazil | 3 | 2% | – | – | outside top 12 |
| India | 2 | 2% | 4 | 3% | even |
| Mexico | 2 | 2% | – | – | outside top 12 |
| Singapore | 2 | 2% | 3 | 2% | even |
| Austria | 1 | 1% | – | – | outside top 12 |
| Germany | 1 | 1% | 29 | 19% | Web Ontology Language |
| Denmark | 1 | 1% | – | – | outside top 12 |
| France | 1 | 1% | 4 | 3% | even |
| United Arab Emirates | – | – | 3 | 2% | outside top 12 |
| Belgium | – | – | 3 | 2% | outside top 12 |
| Poland | – | – | 3 | 2% | outside top 12 |
| BD | – | – | 2 | 1% | outside top 12 |
| Switzerland | – | – | 2 | 1% | outside top 12 |
| Segment | JSON Schema | share | Web Ontology Language | share | Leans |
|---|---|---|---|---|---|
| Software engineering | 129 | 42% | 91 | 16% | JSON Schema |
| Data & AI | 88 | 28% | 242 | 44% | Web Ontology Language |
| IT operations & business systems | 20 | 6% | 24 | 4% | JSON Schema |
| Legal, compliance & risk | 4 | 1% | – | – | outside top 12 |
| Engineering (non-software) | 3 | 1% | 4 | 1% | even |
| Product, program & project management | 3 | 1% | 4 | 1% | even |
| Design & UX | 2 | 1% | – | – | outside top 12 |
| Sales | 2 | 1% | – | – | outside top 12 |
| Administration & office | 1 | 0% | – | – | outside top 12 |
| Arts media and entertainment | 1 | 0% | – | – | outside top 12 |
| Marketing & communications | 1 | 0% | 4 | 1% | even |
| Real estate and property | 1 | 0% | – | – | outside top 12 |
| Finance & accounting | – | – | 8 | 1% | outside top 12 |
| Research and science | – | – | 8 | 1% | outside top 12 |
| Skilled trades and field service | – | – | 8 | 1% | outside top 12 |
| General management & consulting | – | – | 6 | 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 JSON Schema →
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 JSON Schema by 127; 0 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/json-schema-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.
JSON Schema vs Web Ontology Language: questions
Which is more used, JSON Schema or Web Ontology Language?
By companies naming it in their own job postings, Web Ontology Language (154) over JSON Schema (127). 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 JSON Schema?
34% of JSON Schema companies have 2 to 200 employees, against 39% for Web Ontology Language. 35% of JSON Schema companies have over 1,000 employees, against 39% for Web Ontology Language.
Do companies run both?
0 companies name both in their postings, usually JSON Schema 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.
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