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50 Most Valuable Private Companies in the World
Last updated on 23 May 2026
Those are the world's most valuable private companies
As of May 2026 SpaceX, Anthropic and OpenAI are the world's most valuable private companies. ByteDance, Tether, and big AI labs like Anthropic and xAI follow close behind.
It's busy at the top: rankings are shifting constantly. AI companies (model makers) are raising massive rounds at a pace we've never seen before, causing valuations to leapfrog each other every few months.
- Tech still completely rules the world: just 3 companies out of 50 are non-tech (Cargill, Panama Ports Company, and EG Group)
- ... and AI slowly eats it: nearly 1/4 of companies in the ranking are AI-native
- Heavy 🇺🇸 American domination: 60%+ of world's most valuable private companies come from the US
- 🇨🇳 China follows with ByteDance: 20%+ of the list comes from China, including the infamous TikTok owner ByteDance
- 🇪🇺 Very few private giants originating from Europe, but the biggest neobanking success story is European - 🇬🇧 Revolut
Top 50 most valuable private companies as of May 2026
This ranking includes 50 largest non-public companies in the world, as of May 2026. It might include acquired companies that operate in a standalone way. Most valuations are last reported or recently rumoured.
On ZIRP-era valuations: we exclude companies that latest reported valuation would be enough to hit the list (e.g. CloudKitchens), but it is expected that the company is now valued significantly lower.
# | |||||
|---|---|---|---|---|---|
1 | 🇺🇸 | $1,750B | $15.5B | 112.9x | |
2 | 🇺🇸 | $965B | $47.0B | 20.5x | |
3 | 🇺🇸 | $852B | $25.0B | 34.1x | |
4 | 🇨🇳 | $550B | $172.0B | 3.2x | |
5 | 🇸🇻 | $500B | $10.2B | 49.0x | |
6 | 🇺🇸 | $250B | $3.8B | 65.8x | |
7 | 🇺🇸 | $159B | $5.0B | 31.8x | |
8 | 🇺🇸 | $134B | $4.0B | 33.5x | |
9 | 🇺🇸 | $126B | $0.4B | 315.0x | |
10 | 🇨🇳 | $79B | $21.0B | 3.7x | |
11 | 🇬🇧 | $75B | $6.0B | 12.5x | |
12 | 🇦🇺 | $65B | $3.0B | 21.7x | |
13 | 🇨🇳 | $62B | $16.8B | 3.7x | |
14 | 🇺🇸 | $62B | $154.0B | 0.4x | |
15 | 🇺🇸 | $61B | $2.2B | 27.7x | |
16 | 🇨🇳 | $45B | $38.0B | 1.2x | |
17 | 🇺🇸 | $40B | |||
18 | ![](data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 20 20"><rect fill="%23e5e7eb" width="20" height="20" rx="4"/></svg>) Project Prometheus | 🇺🇸 | $38B | ||
19 | 🇨🇳 | $37B | |||
20 | 🇺🇸 | $32B | |||
21 | 🇺🇸 | $32B | $1.0B | 32.0x | |
22 | 🇺🇸 | $32B | $5.8B | 5.4x | |
23 | 🇨🇳 | $31B | $4.8B | 6.5x | |
24 | 🇺🇸 | $31B | $8.1B | 3.8x | |
25 | 🇦🇪 | $30B | $1.4B | 21.4x | |
26 | 🇨🇳 | $30B | |||
27 | 🇺🇸 | $30B | |||
28 | 🇺🇸 | $29B | $2.0B | 14.7x | |
29 | 🇺🇸 | $29B | $0.9B | 33.3x | |
30 | 🇺🇸 | $26B | $0.1B | 356.2x | |
31 | 🇺🇸 | $25B | $1.5B | 16.7x | |
32 | 🇨🇳 | $23B | $4.7B | 4.9x | |
33 | 🇵🇦 | $23B | |||
34 | 🇺🇸 | $22B | $9.7B | 2.3x | |
35 | 🇺🇸 | $22B | $1.5B | 14.7x | |
36 | 🇳🇴 | $21B | $3.5B | 6.0x | |
37 | 🇬🇧 | $20B | $1.6B | 12.5x | |
38 | 🇺🇸 | $20B | $0.1B | 200.0x | |
39 | 🇨🇳 | $20B | $0.2B | 100.0x | |
40 | 🇨🇳 | $20B | $1.7B | 11.6x | |
41 | 🇩🇪 | $18B | |||
42 | 🇸🇪 | $18B | $1.4B | 12.2x | |
43 | 🇺🇸 | $18B | $0.6B | 29.2x | |
44 | 🇺🇸 | $17B | |||
45 | 🇺🇸 | $17B | $0.6B | 29.5x | |
46 | 🇺🇸 | $16B | $6.5B | 2.5x | |
47 | 🇺🇸 | $16B | $0.2B | 105.3x | |
48 | 🇨🇳 | $16B | |||
49 | 🇨🇳 | $15B | $3.8B | 3.9x | |
50 | 🇨🇳 | $15B |
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Data and methodology
Underlying data
Public markets data is powered by FactSet (consensus analyst estimates), and Morningstar (historical data). Data points are calendarized to December where relevant: retrieved data on financial year ends (e.g. FY, FY+1 etc.) are mapped to calendar years (2025A, 2026E etc.) before the appropriate month weights are then applied to prior/future fundamentals.
Private transaction data is multi-sourced, aggregated from harvesting public information, 3rd party APIs, and data engineering. All data is verified and provided with an extensive manual process. If data permits, we apply our own logic to get to the EV. For example, for a large M&A deal with available information on the target's net debt, we might adjust a valuation to fully reflect an accurate EV. In all other cases, we take the reported valuation as the numerator. Financials: we source LTM revenue and LTM EBITDA data from company filings, press releases, or other verified sources. If LTM data is unavailable, we take the 'next best-fit' period (run-rate or calendar year), provided it makes sense in a given case. For example, if a deal closed in November 2025, we might take full-year 2025 revenue as a revenue benchmark.
Any raw figures are harmonised to USD for comparison purposes.
This page is built on data available at Multiples. Sign up for a free trial to see the full dataset.
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