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Proto Labs, Inc (PRLB) Fair Value & Analysis

Industrials · US · Market cap $1.9B

Price$79.81
Fair Value$18.78
Upside-76.5%
Quality95/100
Evidence: High Range $14.48 – $23.43

Fair value as of: Jun 24, 2026

Analysis

Proto Labs, Inc (PRLB) currently trades at $79.81, while our model-based Fair Value estimate is $18.78 — implying the stock looks roughly 76.5% overvalued today. We read business quality at 95/100 (high quality), in the Industrials 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

Proto Labs, Inc., together with its subsidiaries, operates as a digital manufacturer of custom parts in the United States and Europe. It offers manufacturing services, such as molding, computer numerical control machining, 3D printing, and sheet metal to developers, engineers, and supply chain teams. The company was incorporated in 1999 and is headquartered in Maple Plain, Minnesota.

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Frequently asked questions

Is Proto Labs, Inc (PRLB) undervalued?
As of Jun 24, 2026, our model estimates a fair value of $18.78 versus a price of $79.81 — about −76% (overvalued). Model-based estimate, not financial advice.
What is the fair value of PRLB?
Our 21-model fair value for Proto Labs, Inc is $18.78 (as of Jun 24, 2026), built from audited fundamentals. The current price is $79.81.
What is the quality score of PRLB?
Proto Labs, Inc 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.