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- The Unicorns of Latin America β June 2026
The Unicorns of Latin America β June 2026
These are the tech unicorns of Latin America
There are now over 50 tech companies in π Latin America valuaed above $1B. The majority of them operate in fintech and payments.
The majority of LatAm's unicorns come from π§π· Brazil, but the most valuable private company in the region is π¨π΄ Colombian - Rappi, valued at over $5B and rumored to go public multiple times already.
Most unicorns in LatAm were minted during the 2020-2022 ZIRP-era, and their $1B+ 'paper' valuations are already a few years old. Liquidity remains to be seen, as very few IPOs and acquisitions have happened to date.
# | ||||||
|---|---|---|---|---|---|---|
1 | π¦π· | $83.5B | $34.5B | 2.4x | π Public | |
2 | π§π· | $60.6B | $18.8B | 3.2x | π Public | |
3 | π§π· | $36.9B | $8.0B | 4.6x | π Public | |
4 | π§π· | $8.0B | $3.7B | 2.2x | π Public | |
5 | π¨π΄ | $5.3B | $1.4B | 3.8x | π Private | |
6 | π§π· | $5.1B | π Private | |||
7 | π§π· | $5.0B | $1.7B | 2.9x | π Private | |
8 | πΊπΎ | $3.6B | $1.3B | 2.8x | π Public | |
9 | π§π· | $3.1B | π Private | |||
10 | π²π½ | $3.1B | $0.6B | 5.2x | π Private | |
11 | π§π· | $3.1B | $1.3B | 2.4x | π Public | |
12 | π§π· | $3.0B | π Private | |||
13 | π¦π· | $2.8B | π Private | |||
14 | π§π· | $2.6B | $2.8B | 0.9x | π Public | |
15 | π§π· | $2.6B | π Private | |||
16 | π§π· | $2.5B | $3.9B | 0.6x | π Public | |
17 | π§π· | $2.4B | π Private | |||
18 | π§π· | $2.3B | $1.8B | 1.3x | π Public | |
19 | π§π· | $2.2B | π Private | |||
20 | π²π½ | $2.2B | π Private | |||
21 | π²π½ | $2.2B | π Private | |||
22 | π§π· | $2.1B | π Private | |||
23 | π§π· | $2.1B | π Private | |||
24 | π§π· | $2.0B | π Private | |||
25 | π²π½ | $2.0B | π Private | |||
26 | π§π· | $1.8B | π Private | |||
27 | π§π· | $1.7B | $1.5B | 1.2x | π Public | |
28 | π§π· | $1.6B | π Private | |||
29 | π§π· | $1.5B | π Private | |||
30 | π§π· | $1.5B | π Private | |||
31 | π¨π± | $1.5B | π Private | |||
32 | πͺπ¨ | $1.5B | π Private | |||
33 | π¦π· | $1.4B | π Private | |||
34 | π§π· | $1.3B | $0.7B | 1.8x | π Public | |
35 | π²π½ | $1.3B | π Private | |||
36 | π§π· | $1.3B | $2.3B | 0.6x | π Public | |
37 | π¦π· | $1.3B | $2.5B | 0.5x | π Public | |
38 | π²π½ | $1.3B | π Private | |||
39 | π²π½ | $1.2B | π Private | |||
40 | π²π½ | $1.2B | π Private | |||
41 | π¨π± | $1.2B | $3.4B | 0.3x | π Public | |
42 | π§π· | $1.1B | π Public | |||
43 | π²π½ | $1.1B | π Private | |||
44 | π§π· | $1.0B | π Private | |||
45 | π§π· | $1.0B | π Private | |||
46 | π§π· | $1.0B | π Private | |||
47 | π§π· | $1.0B | π Private | |||
48 | π§π· | $1.0B | π Private | |||
49 | π§π· | $1.0B | π Private | |||
50 | π¨π± | $1.0B | π Private |
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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.
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