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Cloud Technologies S.A (CLD) Fair Value & Analysis

Technology · PL · Market cap 314M PLN

Price105.00 PLN
Fair Value37.83 PLN
Upside-64.0%
Quality95/100
Evidence: High Range 27.29 PLN – 49.17 PLN

Fair value as of: Jun 24, 2026

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Analysis

Cloud Technologies S.A (CLD) currently trades at 105.00 PLN, while our model-based Fair Value estimate is 37.83 PLN — implying the stock looks roughly 64.0% overvalued today. We read business quality at 95/100 (high quality), in the Technology sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: high).

About the company

Cloud Technologies S.A. engages in the big data marketing and data monetization businesses. It operates through Data Monetization and Other segments. The company offers a data management platform (DMC) technology under the OnAudience.com brand that is used for collecting and processing data about Internet users worldwide. It also provides data-based solutions, including audience data used in online advertising and for AI training; and proprietary technology for data integration and management. The company was incorporated in 2011 and is headquartered in Warsaw, Poland.

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Frequently asked questions

Is Cloud Technologies S.A (CLD) undervalued?
As of Jun 24, 2026, our model estimates a fair value of 37.83 PLN versus a price of 105.00 PLN — about −64% (overvalued). Model-based estimate, not financial advice.
What is the fair value of CLD?
Our 21-model fair value for Cloud Technologies S.A is 37.83 PLN (as of Jun 24, 2026), built from audited fundamentals. The current price is 105.00 PLN.
What is the quality score of CLD?
Cloud Technologies S.A has a Quality Score of 95/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

How we calculate Fair Value

Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.

Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.