MongoDB (MDB) Change in Accured Expenses (2017 - 2026)
MongoDB's Change in Accured Expenses came in at $43.09 million for fiscal Q2 2027 (quarter ended Jul 31, 2026), up 157.3% from $16.75 million a year earlier.
Powered by 
Analysis
MongoDB (MDB) Change in Accured Expenses (2017 - 2026) Analysis & Trends
Over the trailing twelve months to Jul 31, 2026, MongoDB reported Change in Accured Expenses of $50.72 million; for FY2026 (ended Jan 31, 2026), it was $27.83 million, up 10.2% from FY2025.
- Change in Accured Expenses carries a five-year compound annual growth rate of -4.4% (FY2021 to FY2026).
- Going back by fiscal year, Change in Accured Expenses was $25.25 million in FY2025 (-36.1%), $39.5 million in FY2024, -$16.19 million in FY2023 and $59.25 million in FY2022 (+70.0%).
- The fiscal Q2 2027 figure represents the highest quarterly Change in Accured Expenses in data going back to fiscal Q4 2017.
- Year-over-year, Change in Accured Expenses increased in two of the last four quarters, with growth averaging 139.5%.
- The fastest year-over-year change in Change in Accured Expenses over five years came in fiscal Q4 2024 (growth of 643.7%), and the weakest in fiscal Q4 2023 (a decline of 89.7%).
- Business Quant data shows MDB's Change in Accured Expenses at -$22.8 million (Q1 2027), $16.91 million (Q4 2026) and $13.53 million (Q3 2026) in the three fiscal quarters before Q2 2027.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 305.41 Bn | 290.49 Bn | 2.30 Bn | 369.00 Mn |
| 2 | CrowdStrike Holdings | 258.16 Bn | 238.60 Bn | 1.10 Bn | 72.38 Mn |
| 3 | Fortinet | 127.25 Bn | 113.18 Bn | 1.64 Bn | 75.10 Mn |
| 4 | Snowflake | 118.44 Bn | 105.76 Bn | 1.04 Bn | 108.85 Mn |
| 5 | Datadog | 96.25 Bn | 77.89 Bn | 881.34 Mn | 6.98 Mn |
| 6 | Axon Enterprise | 34.94 Bn | 29.46 Bn | 546.45 Mn | 256.58 Mn |
| 7 | MongoDB | 33.06 Bn | 23.53 Bn | 569.77 Mn | 43.09 Mn |
| 8 | Okta | 32.62 Bn | 22.72 Bn | 641.00 Mn | -2.00 Mn |
| 9 | Zscaler | 31.48 Bn | 17.60 Bn | - | - |
| 10 | Baidu | 29.71 Bn | -43.91 Bn | 1.47 Mn | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jul 31, 2026 | 43.09 Mn |
| Apr 30, 2026 | -22.80 Mn |
| Jan 31, 2026 | 16.91 Mn |
| Oct 31, 2025 | 13.53 Mn |
| Jul 31, 2025 | 16.75 Mn |
| Apr 30, 2025 | -19.35 Mn |
| Jan 31, 2025 | 2.76 Mn |
| Oct 31, 2024 | -6.72 Mn |
| Jul 31, 2024 | 22.69 Mn |
| Apr 30, 2024 | 6.53 Mn |
| Jan 31, 2024 | 19.19 Mn |
| Oct 31, 2023 | 16.86 Mn |
| Jul 31, 2023 | 16.09 Mn |
| Apr 30, 2023 | -12.63 Mn |
| Jan 31, 2023 | 2.58 Mn |
| Oct 31, 2022 | -18.57 Mn |
| Jul 31, 2022 | 22.82 Mn |
| Apr 30, 2022 | -23.02 Mn |
| Jan 31, 2022 | 25.11 Mn |
| Oct 31, 2021 | 32.67 Mn |
API Access
MongoDB Change in Accured 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=change-in-accured-expenses&ticker=MDB&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-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=change-in-accured-expenses&ticker=MDB&period=max&api_key=YOUR_API_KEY");
const data = await res.json();