Investment Comparison Between Snowflake and MongoDB
Written by Emily J. Thompson, Senior Investment Analyst
Updated: Jun 03 2026
0mins
Source: Fool
- Snowflake Financial Performance: In FY 2026, Snowflake reported nearly $4.7 billion in revenue, reflecting a growth rate of approximately 29.2%, although it incurred a net loss of about $1.3 billion, indicating a focus on market expansion and R&D despite short-term profitability challenges.
- MongoDB Growth Potential: MongoDB generated around $2.5 billion in revenue for FY 2026, marking a 22.8% increase, with net losses significantly reduced to $71.2 million, suggesting progress towards break-even and a strong financial position with almost no debt supporting future growth.
- Risk Analysis: Snowflake faces intense competition from cloud giants like Amazon and Microsoft, with a heavy reliance on Amazon's infrastructure posing operational risks; meanwhile, MongoDB must navigate pressures from legacy database providers and the adoption risks of its Atlas product.
- Valuation Comparison: Snowflake's forward P/E ratio stands at 128.5x, significantly higher than MongoDB's 60.1x, indicating that MongoDB offers a more attractive investment opportunity in terms of sales and future earnings estimates, reflecting a relatively conservative valuation strategy.
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Analyst Views on SNOW
Wall Street analysts forecast SNOW stock price to rise
33 Analyst Rating
30 Buy
3 Hold
0 Sell
Strong Buy
Current: 234.520
Low
237.00
Averages
278.19
High
312.00
Current: 234.520
Low
237.00
Averages
278.19
High
312.00
About SNOW
Snowflake Inc. is an artificial intelligence (AI) data cloud company. The Company provides a platform which powers the AI data cloud, enabling customers to consolidate data into a single source of truth to drive insights, apply AI to solve business problems, build data applications, and share data and data products. Its cloud-native architecture includes three independently scalable but logically integrated layers across storage, compute, and cloud services. The storage layer ingests massive amounts and varieties of structured, semi-structured, and unstructured data. The compute layer provides dedicated resources to enable users to simultaneously access common data sets for many use cases with minimal latency. The cloud services layer enables users to securely use AI within applications, tools, and processes. Its platform supports a wide range of product categories for customers’ business objectives, including analytics, data engineering, AI, applications and collaboration.
About the author
Emily J. Thompson
Emily J. Thompson, a Chartered Financial Analyst (CFA) with 12 years in investment research, graduated with honors from the Wharton School. Specializing in industrial and technology stocks, she provides in-depth analysis for Intellectia’s earnings and market brief reports.
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