Azure Machine Learning VS Weights & Biases
As of 30 October 2026, Weights & Biases is named by 283 companies and Azure Machine Learning by 572; Azure Machine Learning skews to companies under 200 people (33% vs 51%), Weights & Biases to companies over 1,000 (23% vs 45%).
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: Azure Machine Learning's script and mail records show on hundreds of thousands of small sites, Weights & Biases rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- Azure Machine Learning: 2–200 employees
- 33%
- Weights & Biases: 2–200 employees
- 51%
- Azure Machine Learning: 1,000+ employees
- 45%
- Weights & Biases: 1,000+ employees
- 23%
- Postings per company, 90 days
- 2.4 · 2.1
- 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 | Azure Machine Learning | share | Weights & Biases | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 4 | 1% | 9 | 3% | Weights & Biases |
| 2-10 employees | 32 | 6% | 42 | 15% | Weights & Biases |
| 11-50 employees | 72 | 13% | 43 | 15% | Weights & Biases |
| 51-200 employees | 85 | 15% | 58 | 20% | Weights & Biases |
| 201-500 employees | 67 | 12% | 38 | 13% | even |
| 501-1000 employees | 57 | 10% | 28 | 10% | even |
| 1001-5000 employees | 112 | 20% | 41 | 14% | Azure Machine Learning |
| 5001-10000 employees | 33 | 6% | 9 | 3% | Azure Machine Learning |
| 10,001+ employees | 110 | 19% | 15 | 5% | Azure Machine Learning |
| Segment | Azure Machine Learning | share | Weights & Biases | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 253 | 44% | 135 | 48% | Weights & Biases |
| Business & finance services | 143 | 25% | 47 | 17% | Azure Machine Learning |
| Manufacturing & industry | 72 | 13% | 36 | 13% | even |
| Utilities & infrastructure | 18 | 3% | 2 | 1% | Azure Machine Learning |
| Commerce & trade | 17 | 3% | 5 | 2% | even |
| Public, community & associations | 12 | 2% | 13 | 5% | Weights & Biases |
| Media & content | 12 | 2% | 16 | 6% | Weights & Biases |
| Health care | 11 | 2% | 3 | 1% | even |
| Consumer services & leisure | 8 | 1% | 12 | 4% | Weights & Biases |
| Automotive | 7 | 1% | 2 | 1% | even |
| Transport, logistics & rental | 7 | 1% | 8 | 3% | even |
| Property & construction | 6 | 1% | 3 | 1% | even |
| Segment | Azure Machine Learning | share | Weights & Biases | share | Leans |
|---|---|---|---|---|---|
| United States | 320 | 56% | 196 | 69% | Weights & Biases |
| United Kingdom | 45 | 8% | 18 | 6% | even |
| Germany | 30 | 5% | 10 | 4% | even |
| Canada | 22 | 4% | 9 | 3% | even |
| India | 21 | 4% | 12 | 4% | even |
| Australia | 17 | 3% | – | – | outside top 12 |
| France | 16 | 3% | 6 | 2% | even |
| Netherlands | 12 | 2% | – | – | outside top 12 |
| Switzerland | 7 | 1% | 2 | 1% | even |
| Poland | 7 | 1% | 2 | 1% | even |
| Sweden | 7 | 1% | – | – | outside top 12 |
| Denmark | 6 | 1% | – | – | outside top 12 |
| Singapore | – | – | 5 | 2% | outside top 12 |
| United Arab Emirates | – | – | 3 | 1% | outside top 12 |
| Ireland | – | – | 2 | 1% | outside top 12 |
| Italy | – | – | 2 | 1% | outside top 12 |
| Segment | Azure Machine Learning | share | Weights & Biases | share | Leans |
|---|---|---|---|---|---|
| Data & AI | 1,261 | 67% | 593 | 69% | even |
| Software engineering | 219 | 12% | 86 | 10% | even |
| IT operations & business systems | 116 | 6% | 31 | 4% | Azure Machine Learning |
| General management & consulting | 32 | 2% | 5 | 1% | even |
| Product, program & project management | 27 | 1% | 11 | 1% | even |
| Sales | 23 | 1% | 18 | 2% | even |
| Finance & accounting | 6 | 0% | 4 | 0% | even |
| Logistics warehouse and transport | 5 | 0% | – | – | outside top 12 |
| Business development & strategy | 4 | 0% | – | – | outside top 12 |
| Legal, compliance & risk | 3 | 0% | 7 | 1% | even |
| Marketing & communications | 3 | 0% | – | – | outside top 12 |
| Research and science | 3 | 0% | 4 | 0% | even |
| Engineering (non-software) | – | – | 4 | 0% | outside top 12 |
| Education & training | – | – | 3 | 0% | outside top 12 |
| Administration & office | – | – | 1 | 0% | outside top 12 |
The companies behind each column
Companies using Azure Machine Learning →
Companies using Weights & Biases →
mlops platforms ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
Weights & Biases is named by 283 companies in job postings and Azure Machine Learning by 572; 45 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/azure-machine-learning-vs-weights-and-biases)
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.
Azure Machine Learning vs Weights & Biases: questions
Which is more used, Azure Machine Learning or Weights & Biases?
By companies naming it in their own job postings, Azure Machine Learning (572) over Weights & Biases (283). By website-and-DNS footprint, Weights & Biases (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses Azure Machine Learning?
33% of Azure Machine Learning companies have 2 to 200 employees, against 51% for Weights & Biases. 45% of Azure Machine Learning companies have over 1,000 employees, against 23% for Weights & Biases.
Do companies run both?
45 companies name both in their postings, usually Azure Machine Learning in marketing and Weights & Biases 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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