The revenue of Mongodb with headquarters in the United States amounted to 2 billion U.S. dollars in 2024. The reported fiscal year ends on January 31.Compared to the earliest depicted value from 2020 this is a total increase by approximately 1.4 billion U.S. dollars. The trend from 2020 to 2024 shows, furthermore, that this increase happened continuously.

MongoDB operating income for the twelve months ending April 30, 2025 was $-0.171B, a 34.91% decline year-over-year. MongoDB annual operating income for 2025 was $-0.216B, a 7.56% decline from 2024. MongoDB annual operating income for 2024 was $-0.234B, a 32.58% decline from 2023. MongoDB annual operating income for 2023 was $-0.347B, a 19.8% increase from 2022.

MongoDB reported $158.04M in Cost of Sales for its fiscal quarter ending in April of 2025. Data for MongoDB | MDB - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last July in 2025.

MongoDB reported $549M in Sales Revenues for its fiscal quarter ending in April of 2025. Data for MongoDB | MDB - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

MongoDB income taxes for the twelve months ending April 30, 2025 were $-0.001B, a 106.12% decline year-over-year. MongoDB annual income taxes for 2025 were $-0.003B, a 119.31% decline from 2024. MongoDB annual income taxes for 2024 were $0.013B, a 7.74% increase from 2023. MongoDB annual income taxes for 2023 were $0.012B, a 205.36% increase from 2022.

MongoDB ebitda margin for the quarter ending April 30, 2025 was -6.75%. MongoDB average ebitda margin for 2024 was -12.68%, a 33.23% decline from 2023. MongoDB average ebitda margin for 2023 was -18.99%, a 33.51% decline from 2022. MongoDB average ebitda margin for 2022 was -28.56%, a 7.17% decline from 2021. Ebitda margin can be defined as earnings before interest, taxes, depreciation and amortization as a portion of total revenue.

MongoDB pre-tax income for the twelve months ending April 30, 2025 was $-0.087B, a 54.2% decline year-over-year. MongoDB annual pre-tax income for 2025 was $-0.132B, a 19.52% decline from 2024. MongoDB annual pre-tax income for 2024 was $-0.164B, a 50.93% decline from 2023. MongoDB annual pre-tax income for 2023 was $-0.333B, a 10.03% increase from 2022.

MongoDB reported $-37626000 in Net Income for its fiscal quarter ending in April of 2025. Data for MongoDB | MDB - Net Income including historical, tables and charts were last updated by Trading Economics this last July in 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

MongoDb (MDB) Stock: Navigating The Data Landscape

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

MongoDB gross margin for the quarter ending April 30, 2025 was 72.91%. MongoDB average gross margin for 2024 was 74.27%, a 0.83% decline from 2023. MongoDB average gross margin for 2023 was 73.66%, a 3.64% increase from 2022. MongoDB average gross margin for 2022 was 71.07%, a 1.76% increase from 2021. Gross margin can be defined as a company's total sales revenue minus its cost of goods sold, divided by the total sales revenue, expressed as a percentage.

MongoDB reported $1 in EPS Earnings Per Share for its fiscal quarter ending in April of 2025. Data for MongoDB | MDB - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last July in 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is MongoDB Stock a Buy?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

MongoDB reported $406.55M in Gross Profit on Sales for its fiscal quarter ending in April of 2025. Data for MongoDB | MDB - Gross Profit On Sales including historical, tables and charts were last updated by Trading Economics this last July in 2025.

MongoDB stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

MongoDB's (MDB) Ascent: Will Momentum Continue?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

MongoDB reported $24.45M in Interest Income for its fiscal quarter ending in January of 2025. Data for MongoDB | MDB - Interest Income including historical, tables and charts were last updated by Trading Economics this last June in 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Mongo's Growth Trajectory Solid: Analysts Bullish on (MDB)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

MongoDB reported $-18562000 in Operating Profit for its fiscal quarter ending in January of 2025. Data for MongoDB | MDB - Operating Profit including historical, tables and charts were last updated by Trading Economics this last July in 2025.

MongoDB is anticipated to experience continued growth in revenue and market share due to the increasing adoption of its database by enterprises. However, there are risks associated with this growth, such as competition from other database providers, potential security breaches, and economic downturns that could impact customer spending.

MongoDB reported $566.96M in Operating Expenses for its fiscal quarter ending in January of 2025. Data for MongoDB | MDB - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last July in 2025.