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- Swedish Tech Unicorns β June 2026
Swedish Tech Unicorns β June 2026
These are the tech unicorns of Sweden
As of June 2026, there are now nearly 30 tech companies valued at over $1B that originated from πΈπͺ Sweden in the last 10 years.
- Spotify is the largest Swedish tech company, followed by Hexagon and Klarna
- Most Swedish unicorns were acquired or went public, just a few remained independent
- Lovable is Sweden's most valuable private unicorn (still independently owned)
- Sweden is strong across consumer tech - Spotify (music), Klarna (BNPL), Embracer (gaming), Modern Times Group (gaming) and many more
- Great online marketplaces come from Sweden - Hemnet, Etraveli Group
Unicorns of Sweden - ranking
This ranking includes tech companies in Sweden valued above $1 billion. It includes private, public and acquired companies. 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, but it is expected that the company is now valued significantly lower.
# | |||||
|---|---|---|---|---|---|
1 | $84.2B | $20.5B | 4.1x | π Public | |
2 | $23.9B | $6.5B | 3.7x | π Public | |
3 | $7.2B | $3.1B | 2.3x | π Public | |
4 | $6.7B | $3.5B | 1.9x | π Public | |
5 | $6.6B | $0.4B | 16.5x | π Private | |
6 | $5.6B | $0.1B | 55.5x | π Private | |
7 | $5.5B | $2.1B | 2.6x | π€ Acquired | |
8 | $4.6B | π€ Acquired | |||
9 | $4.2B | $1.3B | 3.4x | π Public | |
10 | $3.3B | $3.0B | 1.1x | π Public | |
11 | $3.1B | $2.4B | 1.3x | π€ Acquired | |
12 | $2.6B | $0.2B | 10.7x | π€ Acquired | |
13 | $2.5B | $0.2B | 11.4x | π€ Acquired | |
14 | $2.2B | $0.3B | 7.7x | π€ Acquired | |
15 | $2.2B | $0.2B | 13.2x | π€ Acquired | |
16 | $2.0B | $0.2B | 10.0x | π Private | |
17 | $2.0B | $0.3B | 7.4x | π Private | |
18 | $2.0B | π€ Acquired | |||
19 | $1.8B | $0.1B | 36.0x | π Private | |
20 | $1.8B | $0.0B | π Private | ||
21 | $1.8B | $0.2B | 7.5x | π€ Acquired | |
22 | $1.8B | $1.3B | 1.4x | π Public | |
23 | $1.6B | $0.4B | 3.9x | π Public | |
24 | $1.5B | $0.4B | 3.6x | π€ Acquired | |
25 | $1.4B | $0.2B | 6.9x | π Private | |
26 | $1.4B | $0.0B | π Private | ||
27 | $1.4B | $0.2B | 5.6x | π Private | |
28 | $1.3B | $0.1B | 10.3x | π Public | |
29 | $1.3B | $0.1B | 12.5x | π Public | |
30 | $1.2B | $0.2B | 4.9x | π Public | |
31 | $1.1B | π Private | |||
32 | $1.1B | $0.0B | 110.0x | π€ Acquired | |
33 | $1.0B | $1.7B | 0.6x | π Public | |
34 | $0.8B | $0.9B | 0.9x | π Public | |
35 | $0.7B | $0.2B | 4.0x | π Public | |
36 | $0.7B | $0.1B | 11.0x | π Public | |
37 | $0.7B | $0.5B | 1.5x | π Public | |
38 | $0.6B | $0.2B | 3.0x | π Public | |
39 | $0.6B | $1.2B | 0.5x | π Public | |
40 | $0.3B | $0.2B | 1.5x | π Public | |
41 | π Private |
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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