Datadog (DDOG) Other Operating Expenses (2018 - 2026)
Datadog (DDOG) reported Other Operating Expenses of $557.7 million for Q3 2026, up 20.1% from $464.25 million a year earlier and up 1.5% from the prior quarter.
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Analysis
Datadog (DDOG) Other Operating Expenses (2018 - 2026) Analysis & Trends
Over the twelve months ended Sep 30, 2026, Datadog's Other Operating Expenses came in at $2.11 billion, up 23.8% year-over-year; for FY2025, it came in at $1.82 billion, up 30.4% from FY2024.
- Other Operating Expenses has increased for eight consecutive years, with a five-year compound annual growth rate of 44.0% (FY2020 to FY2025).
- By year, Other Operating Expenses came in at $1.39 billion in FY2024 (+20.0%), $1.16 billion in FY2023 (+29.0%), $900.9 million in FY2022 (+85.7%) and $485.02 million in FY2021 (+64.9%).
- The Q3 2026 figure ranks as the highest quarterly Other Operating Expenses in data going back to Q3 2018.
- Year over year, Other Operating Expenses has now increased in each of the last 25 quarters, with growth averaging 26.4% over the last eight quarters.
- Over the past five years, the year-over-year growth in Other Operating Expenses ranged from 13.7% (Q4 2023) to 95.2% (Q2 2022).
- Per Business Quant data, the three quarters before Q3 2026 came in at $549.37 million (Q2 2026), $505.89 million (Q1 2026) and $495.41 million (Q4 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Palo Alto Networks | 235.00 Bn | 220.07 Bn | 2.30 Bn |
| 2 | CrowdStrike Holdings | 221.18 Bn | 201.58 Bn | 1.10 Bn |
| 3 | Snowflake | 96.49 Bn | 83.81 Bn | 1.04 Bn |
| 4 | Fortinet | 87.26 Bn | 71.51 Bn | 1.64 Bn |
| 5 | Datadog | 64.27 Bn | 44.82 Bn | 959.92 Mn |
| 6 | Zscaler | 31.67 Bn | 17.82 Bn | 689.22 Mn |
| 7 | Okta | 29.42 Bn | 19.51 Bn | 641.00 Mn |
| 8 | Axon Enterprise | 25.30 Bn | 19.82 Bn | 546.45 Mn |
| 9 | MongoDB | 25.20 Bn | 15.67 Bn | 569.77 Mn |
| 10 | Roku | 23.89 Bn | 13.92 Bn | 648.52 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 303.30 Mn |
| Jun 30, 2026 | 311.52 Mn |
| Mar 31, 2026 | 279.82 Mn |
| Dec 31, 2025 | 264.38 Mn |
| Sep 30, 2025 | 238.73 Mn |
| Jun 30, 2025 | 239.03 Mn |
| Mar 31, 2025 | 214.29 Mn |
| Dec 31, 2024 | 207.95 Mn |
| Sep 30, 2024 | 187.77 Mn |
| Jun 30, 2024 | 187.01 Mn |
| Mar 31, 2024 | 173.88 Mn |
| Dec 31, 2023 | 159.98 Mn |
| Sep 30, 2023 | 156.87 Mn |
| Jun 30, 2023 | 147.46 Mn |
| Mar 31, 2023 | 144.97 Mn |
| Dec 31, 2022 | 149.36 Mn |
| Sep 30, 2022 | 129.49 Mn |
| Jun 30, 2022 | 115.27 Mn |
| Mar 31, 2022 | 101.17 Mn |
| Dec 31, 2021 | 88.91 Mn |
API Access
Datadog Other Operating 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=other-operating-expenses&ticker=DDOG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "other-operating-expenses", "ticker": "DDOG", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=other-operating-expenses&ticker=DDOG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();