Amazon SageMaker VS MLflow
As of 4 November 2026, MLflow is named by 1,756 companies and Amazon SageMaker by 1,384; Amazon SageMaker skews to companies under 200 people (38% vs 41%), MLflow to companies over 1,000 (35% vs 36%).
As of 4 November 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: Amazon SageMaker's script and mail records show on hundreds of thousands of small sites, MLflow rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- Amazon SageMaker: 2–200 employees
- 38%
- MLflow: 2–200 employees
- 41%
- Amazon SageMaker: 1,000+ employees
- 36%
- MLflow: 1,000+ employees
- 35%
- Postings per company, 90 days
- 2.6 · 2.4
- 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 | Amazon SageMaker | share | MLflow | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 36 | 3% | 50 | 3% | even |
| 2-10 employees | 99 | 7% | 168 | 10% | MLflow |
| 11-50 employees | 159 | 11% | 221 | 13% | even |
| 51-200 employees | 272 | 20% | 327 | 19% | even |
| 201-500 employees | 184 | 13% | 217 | 12% | even |
| 501-1000 employees | 138 | 10% | 152 | 9% | even |
| 1001-5000 employees | 226 | 16% | 278 | 16% | even |
| 5001-10000 employees | 67 | 5% | 89 | 5% | even |
| 10,001+ employees | 203 | 15% | 254 | 14% | even |
| Segment | Amazon SageMaker | share | MLflow | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 695 | 50% | 843 | 48% | Amazon SageMaker |
| Business & finance services | 338 | 24% | 390 | 22% | Amazon SageMaker |
| Manufacturing & industry | 107 | 8% | 174 | 10% | MLflow |
| Media & content | 53 | 4% | 59 | 3% | even |
| Utilities & infrastructure | 39 | 3% | 37 | 2% | even |
| Commerce & trade | 34 | 2% | 55 | 3% | even |
| Consumer services & leisure | 31 | 2% | 45 | 3% | even |
| Public, community & associations | 27 | 2% | 48 | 3% | even |
| Health care | 14 | 1% | 24 | 1% | even |
| Automotive | 13 | 1% | 18 | 1% | even |
| Hospitality & travel | 11 | 1% | – | – | outside top 12 |
| Property & construction | 9 | 1% | 19 | 1% | even |
| Transport, logistics & rental | – | – | 26 | 1% | outside top 12 |
| Segment | Amazon SageMaker | share | MLflow | share | Leans |
|---|---|---|---|---|---|
| United States | 880 | 64% | 997 | 57% | Amazon SageMaker |
| United Kingdom | 98 | 7% | 123 | 7% | even |
| Germany | 45 | 3% | 74 | 4% | even |
| India | 41 | 3% | 47 | 3% | even |
| Canada | 38 | 3% | 53 | 3% | even |
| France | 28 | 2% | 68 | 4% | even |
| Australia | 20 | 1% | 25 | 1% | even |
| Spain | 16 | 1% | 19 | 1% | even |
| Japan | 14 | 1% | – | – | outside top 12 |
| Poland | 13 | 1% | – | – | outside top 12 |
| Netherlands | 12 | 1% | 28 | 2% | even |
| Sweden | 12 | 1% | – | – | outside top 12 |
| Brazil | – | – | 30 | 2% | outside top 12 |
| Singapore | – | – | 20 | 1% | outside top 12 |
| Switzerland | – | – | 17 | 1% | outside top 12 |
| Segment | Amazon SageMaker | share | MLflow | share | Leans |
|---|---|---|---|---|---|
| Data & AI | 2,900 | 60% | 4,073 | 75% | MLflow |
| Software engineering | 725 | 15% | 486 | 9% | Amazon SageMaker |
| IT operations & business systems | 297 | 6% | 144 | 3% | Amazon SageMaker |
| Product, program & project management | 119 | 2% | 59 | 1% | even |
| Sales | 78 | 2% | 55 | 1% | even |
| Customer service & success | 36 | 1% | 1 | 0% | even |
| Engineering (non-software) | 32 | 1% | 25 | 0% | even |
| General management & consulting | 29 | 1% | 31 | 1% | even |
| Legal, compliance & risk | 26 | 1% | 12 | 0% | even |
| Unclassified | 13 | 0% | 8 | 0% | even |
| Marketing & communications | 12 | 0% | 10 | 0% | even |
| Business development & strategy | 9 | 0% | 4 | 0% | even |
| Finance & accounting | 9 | 0% | 8 | 0% | even |
| Research and science | 8 | 0% | 22 | 0% | even |
| Education & training | 6 | 0% | – | – | outside top 12 |
| Construction and civil | 5 | 0% | 1 | 0% | even |
| Quality, safety & environment | 3 | 0% | 4 | 0% | even |
| Arts media and entertainment | 2 | 0% | – | – | outside top 12 |
| Human resources | 2 | 0% | 1 | 0% | even |
| Administration & office | 1 | 0% | 2 | 0% | even |
| Design & UX | 1 | 0% | 1 | 0% | even |
| Healthcare clinical | 1 | 0% | 2 | 0% | even |
| Logistics warehouse and transport | 1 | 0% | 5 | 0% | even |
| Hospitality and food service | – | – | 1 | 0% | outside top 12 |
The companies behind each column
Companies using Amazon SageMaker →
Companies using MLflow →
mlops platforms ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
MLflow is named by 1,756 companies in job postings and Amazon SageMaker by 1,384; 637 name both. (Breaknorm, 4 November 2026, https://breaknorm.co/compare/amazon-sagemaker-vs-mlflow)
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.
Amazon SageMaker vs MLflow: questions
Which is more used, Amazon SageMaker or MLflow?
By companies naming it in their own job postings, MLflow (1,756) over Amazon SageMaker (1,384). By website-and-DNS footprint, MLflow (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses Amazon SageMaker?
38% of Amazon SageMaker companies have 2 to 200 employees, against 41% for MLflow. 36% of Amazon SageMaker companies have over 1,000 employees, against 35% for MLflow.
Do companies run both?
637 companies name both in their postings, usually Amazon SageMaker in marketing and MLflow 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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