Datadog (DDOG) Operating Expenses (2018 - 2026)
Datadog (DDOG) posted Operating Expenses of $875.89 million for Q2 2026, up 25.8% from $696.28 million a year earlier and up 10.9% from the prior quarter.
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Analysis
Datadog (DDOG) Operating Expenses (2018 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Operating Expenses at Datadog was $3.14 billion, up 29.2% year-over-year; for FY2025, it came in at $2.78 billion, up 31.7% from FY2024.
- Annual Operating Expenses has increased for eight consecutive years, with a five-year compound annual growth rate of 41.7% (FY2020 to FY2025).
- In prior years, Datadog's Operating Expenses was $2.11 billion in FY2024 (+20.7%), $1.75 billion in FY2023 (+26.3%), $1.39 billion in FY2022 (+70.5%) and $813.7 million in FY2021 (+67.1%).
- The Q2 2026 figure stands as the highest quarterly Operating Expenses in data going back to Q3 2018.
- On a year-over-year basis, Operating Expenses has increased in each of the last 24 quarters, with growth averaging 28.4% over the last eight quarters.
- The year-over-year growth in Operating Expenses has ranged between 12.2% (Q4 2023) and 76.5% (Q3 2022) over the last five years.
- According to Business Quant data, Operating Expenses for the three prior quarters was $789.87 million (Q1 2026), $756.94 million (Q4 2025) and $715 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 305.41 Bn | 290.49 Bn | 2.30 Bn | 2.13 Bn |
| 2 | CrowdStrike Holdings | 258.16 Bn | 238.60 Bn | 1.10 Bn | 1.13 Bn |
| 3 | Fortinet | 127.25 Bn | 113.18 Bn | 1.64 Bn | 953.90 Mn |
| 4 | Snowflake | 118.44 Bn | 105.76 Bn | 1.04 Bn | 1.30 Bn |
| 5 | Datadog | 96.25 Bn | 77.89 Bn | 881.34 Mn | 875.89 Mn |
| 6 | Axon Enterprise | 34.94 Bn | 29.46 Bn | 546.45 Mn | 499.67 Mn |
| 7 | MongoDB | 33.06 Bn | 23.53 Bn | 569.77 Mn | 541.37 Mn |
| 8 | Okta | 32.62 Bn | 22.72 Bn | 641.00 Mn | 534.00 Mn |
| 9 | Zscaler | 31.48 Bn | 17.60 Bn | - | - |
| 10 | Baidu | 29.71 Bn | -43.91 Bn | 1.47 Mn | 4.17 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 875.89 Mn |
| Mar 31, 2026 | 789.87 Mn |
| Dec 31, 2025 | 756.94 Mn |
| Sep 30, 2025 | 715.00 Mn |
| Jun 30, 2025 | 696.28 Mn |
| Mar 31, 2025 | 616.35 Mn |
| Dec 31, 2024 | 584.16 Mn |
| Sep 30, 2024 | 531.98 Mn |
| Jun 30, 2024 | 509.16 Mn |
| Mar 31, 2024 | 489.16 Mn |
| Dec 31, 2023 | 457.08 Mn |
| Sep 30, 2023 | 448.45 Mn |
| Jun 30, 2023 | 429.62 Mn |
| Mar 31, 2023 | 416.77 Mn |
| Dec 31, 2022 | 407.27 Mn |
| Sep 30, 2022 | 374.28 Mn |
| Jun 30, 2022 | 327.35 Mn |
| Mar 31, 2022 | 278.15 Mn |
| Dec 31, 2021 | 250.59 Mn |
| Sep 30, 2021 | 212.05 Mn |
API Access
Datadog 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=operating-expenses&ticker=DDOG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "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=operating-expenses&ticker=DDOG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();