Great Expectations VS Monte Carlo
As of 30 October 2026, Monte Carlo is named by 113 companies and Great Expectations by 215; Great Expectations skews to companies under 200 people (40% vs 30%), Monte Carlo to companies over 1,000 (39% vs 33%).
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: Great Expectations's script and mail records show on hundreds of thousands of small sites, Monte Carlo rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- Great Expectations: 2–200 employees
- 40%
- Monte Carlo: 2–200 employees
- 30%
- Great Expectations: 1,000+ employees
- 33%
- Monte Carlo: 1,000+ employees
- 39%
- Postings per company, 90 days
- 2.2 · 2.7
- 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 | Great Expectations | share | Monte Carlo | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 3 | 1% | – | – | outside top 12 |
| 2-10 employees | 11 | 5% | 8 | 7% | even |
| 11-50 employees | 29 | 13% | 9 | 8% | Great Expectations |
| 51-200 employees | 46 | 21% | 17 | 15% | Great Expectations |
| 201-500 employees | 27 | 13% | 17 | 15% | Monte Carlo |
| 501-1000 employees | 29 | 13% | 18 | 16% | Monte Carlo |
| 1001-5000 employees | 38 | 18% | 21 | 19% | even |
| 5001-10000 employees | 10 | 5% | 3 | 3% | even |
| 10,001+ employees | 22 | 10% | 20 | 18% | Monte Carlo |
| Segment | Great Expectations | share | Monte Carlo | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 104 | 48% | 34 | 30% | Great Expectations |
| Business & finance services | 44 | 20% | 30 | 27% | Monte Carlo |
| Manufacturing & industry | 14 | 7% | 18 | 16% | Monte Carlo |
| Utilities & infrastructure | 12 | 6% | 2 | 2% | Great Expectations |
| Commerce & trade | 11 | 5% | 9 | 8% | Monte Carlo |
| Public, community & associations | 6 | 3% | 5 | 4% | even |
| Consumer services & leisure | 5 | 2% | 2 | 2% | even |
| Media & content | 5 | 2% | 5 | 4% | Monte Carlo |
| Health care | 4 | 2% | 5 | 4% | Monte Carlo |
| Hospitality & travel | 3 | 1% | 1 | 1% | even |
| Property & construction | 3 | 1% | 1 | 1% | even |
| Automotive | 2 | 1% | – | – | outside top 12 |
| Transport, logistics & rental | – | – | 1 | 1% | outside top 12 |
| Segment | Great Expectations | share | Monte Carlo | share | Leans |
|---|---|---|---|---|---|
| United States | 150 | 70% | 87 | 77% | Monte Carlo |
| United Kingdom | 16 | 7% | 6 | 5% | Great Expectations |
| Canada | 6 | 3% | 1 | 1% | even |
| France | 6 | 3% | 4 | 4% | even |
| Belgium | 4 | 2% | 2 | 2% | even |
| Germany | 4 | 2% | 2 | 2% | even |
| India | 4 | 2% | – | – | outside top 12 |
| Australia | 3 | 1% | 3 | 3% | even |
| Netherlands | 3 | 1% | 1 | 1% | even |
| Switzerland | 2 | 1% | – | – | outside top 12 |
| Sweden | 2 | 1% | – | – | outside top 12 |
| Singapore | 2 | 1% | – | – | outside top 12 |
| Austria | – | – | 2 | 2% | outside top 12 |
| MK | – | – | 1 | 1% | outside top 12 |
| Mexico | – | – | 1 | 1% | outside top 12 |
| Norway | – | – | 1 | 1% | outside top 12 |
| Segment | Great Expectations | share | Monte Carlo | share | Leans |
|---|---|---|---|---|---|
| Data & AI | 341 | 61% | 252 | 70% | Monte Carlo |
| Software engineering | 49 | 9% | 8 | 2% | Great Expectations |
| IT operations & business systems | 21 | 4% | 7 | 2% | even |
| Engineering (non-software) | 19 | 3% | 31 | 9% | Monte Carlo |
| Quality, safety & environment | 17 | 3% | 2 | 1% | Great Expectations |
| Legal, compliance & risk | 7 | 1% | 2 | 1% | even |
| Product, program & project management | 7 | 1% | 13 | 4% | Monte Carlo |
| Skilled trades and field service | 7 | 1% | – | – | outside top 12 |
| Sales | 5 | 1% | – | – | outside top 12 |
| Customer service & success | 2 | 0% | – | – | outside top 12 |
| Hospitality and food service | 2 | 0% | – | – | outside top 12 |
| Real estate and property | 2 | 0% | – | – | outside top 12 |
| Finance & accounting | – | – | 5 | 1% | outside top 12 |
| Research and science | – | – | 4 | 1% | outside top 12 |
| Construction and civil | – | – | 3 | 1% | outside top 12 |
| General management & consulting | – | – | 2 | 1% | outside top 12 |
The companies behind each column
Companies using Great Expectations →
Companies using Monte Carlo →
data governance quality mdm ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
Monte Carlo is named by 113 companies in job postings and Great Expectations by 215; 76 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/great-expectations-vs-monte-carlo)
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.
Great Expectations vs Monte Carlo: questions
Which is more used, Great Expectations or Monte Carlo?
By companies naming it in their own job postings, Great Expectations (215) over Monte Carlo (113). By website-and-DNS footprint, Monte Carlo (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses Great Expectations?
40% of Great Expectations companies have 2 to 200 employees, against 30% for Monte Carlo. 33% of Great Expectations companies have over 1,000 employees, against 39% for Monte Carlo.
Do companies run both?
76 companies name both in their postings, usually Great Expectations in marketing and Monte Carlo 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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