Investment Comparison: Snowflake vs MongoDB
Written by Emily J. Thompson, Senior Investment Analyst
Updated: Jun 04 2026
0mins
Source: NASDAQ.COM
- Revenue Growth Comparison: Snowflake achieved nearly $4.7 billion in revenue for FY 2026, reflecting a 29.2% increase despite a net loss of $1.3 billion, indicating heavy investment in market expansion; in contrast, MongoDB generated $2.5 billion, a 22.8% growth with a significantly reduced net loss of $71.2 million, showing progress towards breakeven.
- Financial Health Status: Snowflake's debt-to-equity ratio stands at 1.4 with a current ratio of 1.3, demonstrating its ability to cover short-term liabilities; conversely, MongoDB has almost no debt with a 0.0 debt-to-equity ratio and a high current ratio of 4.7, indicating excellent liquidity to manage short-term financial pressures.
- Market Competition Pressure: Snowflake faces intense competition from cloud giants like Amazon and Microsoft, with its reliance on Amazon's infrastructure potentially impacting operations; similarly, MongoDB contends with pressure from legacy database providers like IBM and Oracle, and the adoption of its Atlas product is critical, as any failure could significantly impact revenue.
- Investment Value Assessment: While Snowflake excels in its AI Data Cloud platform, its high valuation may deter investors; MongoDB, with a more reasonable valuation and strong growth potential, particularly in the context of ongoing investments in AI infrastructure, presents an optimistic outlook for future investors.
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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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