Core ML VS PyTorch FSDP
As of 30 October 2026, PyTorch FSDP is named by 114 companies and Core ML by 166; Core ML skews to companies under 200 people (49% vs 59%), PyTorch FSDP to companies over 1,000 (18% vs 27%).
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: Core ML's script and mail records show on hundreds of thousands of small sites, PyTorch FSDP rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- Core ML: 2–200 employees
- 49%
- PyTorch FSDP: 2–200 employees
- 59%
- Core ML: 1,000+ employees
- 27%
- PyTorch FSDP: 1,000+ employees
- 18%
- Postings per company, 90 days
- 2.3 · 2.0
- 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 | Core ML | share | PyTorch FSDP | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 5 | 3% | 8 | 7% | PyTorch FSDP |
| 2-10 employees | 18 | 11% | 30 | 26% | PyTorch FSDP |
| 11-50 employees | 33 | 20% | 17 | 15% | Core ML |
| 51-200 employees | 30 | 18% | 20 | 18% | even |
| 201-500 employees | 21 | 13% | 13 | 11% | even |
| 501-1000 employees | 15 | 9% | 6 | 5% | Core ML |
| 1001-5000 employees | 21 | 13% | 9 | 8% | Core ML |
| 5001-10000 employees | 6 | 4% | 5 | 4% | even |
| 10,001+ employees | 17 | 10% | 6 | 5% | Core ML |
| Segment | Core ML | share | PyTorch FSDP | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 92 | 55% | 70 | 61% | PyTorch FSDP |
| Business & finance services | 33 | 20% | 10 | 9% | Core ML |
| Manufacturing & industry | 17 | 10% | 9 | 8% | Core ML |
| Consumer services & leisure | 7 | 4% | 2 | 2% | Core ML |
| Media & content | 4 | 2% | 4 | 4% | even |
| Transport, logistics & rental | 4 | 2% | 4 | 4% | even |
| Public, community & associations | 3 | 2% | 10 | 9% | PyTorch FSDP |
| Automotive | 2 | 1% | 3 | 3% | even |
| Commerce & trade | 2 | 1% | – | – | outside top 12 |
| Health care | 1 | 1% | – | – | outside top 12 |
| Hospitality & travel | 1 | 1% | – | – | outside top 12 |
| Property & construction | – | – | 1 | 1% | outside top 12 |
| Utilities & infrastructure | – | – | 1 | 1% | outside top 12 |
| Segment | Core ML | share | PyTorch FSDP | share | Leans |
|---|---|---|---|---|---|
| United States | 105 | 63% | 86 | 75% | PyTorch FSDP |
| United Kingdom | 21 | 13% | 5 | 4% | Core ML |
| India | 11 | 7% | 6 | 5% | even |
| Canada | 5 | 3% | 5 | 4% | even |
| Germany | 4 | 2% | 1 | 1% | even |
| Italy | 3 | 2% | – | – | outside top 12 |
| Lithuania | 2 | 1% | – | – | outside top 12 |
| Sweden | 2 | 1% | – | – | outside top 12 |
| United Arab Emirates | 1 | 1% | 1 | 1% | even |
| Austria | 1 | 1% | – | – | outside top 12 |
| Switzerland | 1 | 1% | 2 | 2% | even |
| Cyprus | 1 | 1% | – | – | outside top 12 |
| TH | – | – | 2 | 2% | outside top 12 |
| Greece | – | – | 1 | 1% | outside top 12 |
| South Korea | – | – | 1 | 1% | outside top 12 |
| Mexico | – | – | 1 | 1% | outside top 12 |
| Netherlands | – | – | 1 | 1% | outside top 12 |
| Segment | Core ML | share | PyTorch FSDP | share | Leans |
|---|---|---|---|---|---|
| Data & AI | 277 | 55% | 264 | 79% | PyTorch FSDP |
| Software engineering | 152 | 30% | 19 | 6% | Core ML |
| IT operations & business systems | 21 | 4% | 9 | 3% | even |
| Product, program & project management | 11 | 2% | – | – | outside top 12 |
| Engineering (non-software) | 4 | 1% | – | – | outside top 12 |
| Arts media and entertainment | 1 | 0% | – | – | outside top 12 |
| Customer service & success | 1 | 0% | – | – | outside top 12 |
| Human resources | 1 | 0% | – | – | outside top 12 |
| Legal, compliance & risk | 1 | 0% | – | – | outside top 12 |
| Unclassified | 1 | 0% | – | – | outside top 12 |
| Research and science | – | – | 8 | 2% | outside top 12 |
| Sales | – | – | 2 | 1% | outside top 12 |
| General management & consulting | – | – | 1 | 0% | outside top 12 |
The companies behind each column
Companies using Core ML →
Companies using PyTorch FSDP →
ml frameworks ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
PyTorch FSDP is named by 114 companies in job postings and Core ML by 166; 0 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/apple-core-ml-vs-pytorch-fsdp)
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.
Core ML vs PyTorch FSDP: questions
Which is more used, Core ML or PyTorch FSDP?
By companies naming it in their own job postings, Core ML (166) over PyTorch FSDP (114). By website-and-DNS footprint, PyTorch FSDP (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses Core ML?
49% of Core ML companies have 2 to 200 employees, against 59% for PyTorch FSDP. 27% of Core ML companies have over 1,000 employees, against 18% for PyTorch FSDP.
Do companies run both?
0 companies name both in their postings, usually Core ML in marketing and PyTorch FSDP 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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