Climb Global Solutions, Inc (CLMB) Fair Value & Analysis
Technology · US · Market cap $422M
Fair value as of: Jun 24, 2026
Analysis
Climb Global Solutions, Inc (CLMB) currently trades at $22.03, while our model-based Fair Value estimate is $22.47 — implying the stock looks roughly 2.0% undervalued today. We read business quality at 95/100 (high quality), in the Technology sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.
About the company
Climb Global Solutions, Inc. operates as a value-added information technology (IT) distribution and solutions company in the United States, Canada, Europe, and the United Kingdom. The company operates through two segments, Distribution and Solutions. It distributes technical software to corporate and value-added resellers, consultants, and systems integrators under the name Climb Channel Solutions; and provides cloud solutions and resells software, hardware, and services under the name Grey Matter. The company also resells computer software and hardware developed by others, as well as provides technical services to end user customers. In addition, it offers a line of products from various software vendors; and tools for virtualization/cloud computing, security, networking, storage and infrastructure management, application lifecycle management, and other technically sophisticated domains, as well as computer hardware. The company markets its products through its own web sites, local…
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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.