MongoDB (MDB) Accumulated Expenses (2017 - 2026)
MongoDB (MDB) reported Accumulated Expenses of $129.81 million for fiscal Q2 2027 (quarter ended Jul 31, 2026), up 25.2% from $103.66 million a year earlier and up 17.5% from the prior quarter.
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Analysis
MongoDB (MDB) Accumulated Expenses (2017 - 2026) Analysis & Trends
At the end of FY2026 (ended Jan 31, 2026), MongoDB posted Accumulated Expenses of $109.8 million, up 25.3% from FY2025.
- Accumulated Expenses has increased for four consecutive fiscal years, with a five-year compound annual growth rate of 14.2% (FY2021 to FY2026).
- By fiscal year, Accumulated Expenses came in at $87.66 million in FY2025 (+17.1%), $74.83 million in FY2024 (+42.1%), $52.67 million in FY2023 (+7.8%) and $48.85 million in FY2022 (-13.5%).
- The fiscal Q2 2027 figure ranks as the highest quarterly Accumulated Expenses in data going back to fiscal Q4 2017.
- Year over year, Accumulated Expenses has now increased in each of the last 12 quarters, with growth averaging 18.4% over the last eight quarters.
- The high point for year-over-year Accumulated Expenses in five years was fiscal Q2 2023 (growth of 110.8%); the low point was fiscal Q2 2024 (a decline of 25.5%).
- Per Business Quant data, the three fiscal quarters before Q2 2027 came in at $110.52 million (Q1 2027), $109.8 million (Q4 2026) and $101.44 million (Q3 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Palo Alto Networks | 323.81 Bn | 308.88 Bn | 2.30 Bn |
| 2 | CrowdStrike Holdings | 271.09 Bn | 251.53 Bn | 1.10 Bn |
| 3 | Fortinet | 131.14 Bn | 117.07 Bn | 1.64 Bn |
| 4 | Snowflake | 119.71 Bn | 107.03 Bn | 1.04 Bn |
| 5 | Datadog | 98.30 Bn | 79.94 Bn | 881.34 Mn |
| 6 | Okta | 34.95 Bn | 25.05 Bn | 641.00 Mn |
| 7 | Axon Enterprise | 34.32 Bn | 28.85 Bn | 546.45 Mn |
| 8 | Zscaler | 32.52 Bn | 18.64 Bn | - |
| 9 | Baidu | 29.56 Bn | -44.07 Bn | 1.47 Mn |
| 10 | MongoDB | 28.08 Bn | 18.55 Bn | 569.77 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jul 31, 2026 | 129.81 Mn |
| Apr 30, 2026 | 110.52 Mn |
| Jan 31, 2026 | 109.80 Mn |
| Oct 31, 2025 | 101.44 Mn |
| Jul 31, 2025 | 103.66 Mn |
| Apr 30, 2025 | 89.06 Mn |
| Jan 31, 2025 | 87.66 Mn |
| Oct 31, 2024 | 86.80 Mn |
| Jul 31, 2024 | 100.80 Mn |
| Apr 30, 2024 | 84.11 Mn |
| Jan 31, 2024 | 74.83 Mn |
| Oct 31, 2023 | 66.72 Mn |
| Jul 31, 2023 | 55.03 Mn |
| Apr 30, 2023 | 50.47 Mn |
| Jan 31, 2023 | 52.67 Mn |
| Oct 31, 2022 | 52.83 Mn |
| Jul 31, 2022 | 73.92 Mn |
| Apr 30, 2022 | 49.22 Mn |
| Jan 31, 2022 | 48.85 Mn |
| Oct 31, 2021 | 46.47 Mn |
API Access
MongoDB Accumulated Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=MDB&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "MDB", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=MDB&period=max&api_key=YOUR_API_KEY");
const data = await res.json();