Datadog (DDOG) Other Operating Expenses (2018 - 2026)
Datadog (DDOG) posted Other Operating Expenses of $311.52 million for Q2 2026, up 30.3% from $239.03 million a year earlier and up 11.3% from the prior quarter.
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Analysis
Datadog (DDOG) Other Operating Expenses (2018 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Other Operating Expenses at Datadog was $1.09 billion, up 28.9% year-over-year; for FY2025, it came in at $956.42 million, up 26.4% from FY2024.
- Annual Other Operating Expenses has increased for eight consecutive years, with a five-year compound annual growth rate of 35.0% (FY2020 to FY2025).
- In prior years, Datadog's Other Operating Expenses was $756.61 million in FY2024 (+24.2%), $609.28 million in FY2023 (+23.0%), $495.29 million in FY2022 (+65.4%) and $299.5 million in FY2021 (+40.2%).
- The Q2 2026 figure stands as the highest quarterly Other Operating Expenses in data going back to Q3 2018.
- On a year-over-year basis, Other Operating Expenses has increased in each of the last 24 quarters, with growth averaging 27.0% over the last eight quarters.
- Across the past five years, year-over-year growth in Other Operating Expenses ran from 7.1% in Q4 2023 to 70.8% in Q3 2022.
- According to Business Quant data, Other Operating Expenses for the three prior quarters was $279.82 million (Q1 2026), $264.38 million (Q4 2025) and $238.73 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Palo Alto Networks | 305.41 Bn | 290.49 Bn | 2.30 Bn |
| 2 | CrowdStrike Holdings | 258.16 Bn | 238.60 Bn | 1.10 Bn |
| 3 | Fortinet | 127.25 Bn | 113.18 Bn | 1.64 Bn |
| 4 | Snowflake | 118.44 Bn | 105.76 Bn | 1.04 Bn |
| 5 | Datadog | 96.25 Bn | 77.89 Bn | 881.34 Mn |
| 6 | Axon Enterprise | 34.94 Bn | 29.46 Bn | 546.45 Mn |
| 7 | MongoDB | 33.06 Bn | 23.53 Bn | 569.77 Mn |
| 8 | Okta | 32.62 Bn | 22.72 Bn | 641.00 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 |
|---|---|
| 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 |
| Sep 30, 2021 | 75.83 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();