Datadog (DDOG) Non-Current Assets (2018 - 2026)
Datadog (DDOG) recorded Non-Current Assets of $1.54 billion in Q2 2026, up 30.7% from $1.18 billion a year earlier and up 16.0% from the prior quarter.
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Analysis
Datadog (DDOG) Non-Current Assets (2018 - 2026) Analysis & Trends
At the end of FY2025, Datadog reported Non-Current Assets of $1.26 billion, up 44.3% from FY2024.
- Annual Non-Current Assets has increased for seven straight years, with a five-year compound annual growth rate of 48.9% (FY2020 to FY2025).
- Across earlier years, Non-Current Assets came in at $874.22 million in FY2024 (+15.3%), $757.94 million in FY2023 (+14.7%), $660.62 million in FY2022 (+29.6%) and $509.85 million in FY2021 (+196.1%).
- The Q2 2026 figure is the highest quarterly Non-Current Assets in data going back to Q4 2018.
- On a year-over-year basis, Non-Current Assets has increased for 20 consecutive quarters, with growth averaging 31.5% over the last eight quarters.
- The year-over-year growth in Non-Current Assets has ranged between 14.7% (Q4 2023) and 196.1% (Q4 2021) over the last five years.
- Per Business Quant, the preceding three quarters came in at $1.33 billion (Q1 2026), $1.26 billion (Q4 2025) and $1.21 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Non-Current Assets (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 319.55 Bn | 304.63 Bn | 2.30 Bn | 39.82 Bn |
| 2 | CrowdStrike Holdings | 265.46 Bn | 245.90 Bn | 1.10 Bn | 5.07 Bn |
| 3 | Fortinet | 129.36 Bn | 115.29 Bn | 1.64 Bn | 4.65 Bn |
| 4 | Snowflake | 115.65 Bn | 102.97 Bn | 1.04 Bn | 5.20 Bn |
| 5 | Datadog | 96.45 Bn | 78.09 Bn | 881.34 Mn | 1.54 Bn |
| 6 | Axon Enterprise | 34.50 Bn | 29.03 Bn | 546.45 Mn | 4.48 Bn |
| 7 | Okta | 33.79 Bn | 23.89 Bn | 641.00 Mn | 6.05 Bn |
| 8 | Zscaler | 32.51 Bn | 18.63 Bn | - | 2.84 Bn |
| 9 | Baidu | 29.59 Bn | -44.04 Bn | 1.47 Mn | 38.70 Bn |
| 10 | MongoDB | 26.96 Bn | 17.43 Bn | 569.77 Mn | 651.70 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 1.54 Bn |
| Mar 31, 2026 | 1.33 Bn |
| Dec 31, 2025 | 1.26 Bn |
| Sep 30, 2025 | 1.21 Bn |
| Jun 30, 2025 | 1.18 Bn |
| Mar 31, 2025 | 934.04 Mn |
| Dec 31, 2024 | 874.22 Mn |
| Sep 30, 2024 | 842.04 Mn |
| Jun 30, 2024 | 820.30 Mn |
| Mar 31, 2024 | 807.80 Mn |
| Dec 31, 2023 | 757.94 Mn |
| Sep 30, 2023 | 723.82 Mn |
| Jun 30, 2023 | 712.10 Mn |
| Mar 31, 2023 | 676.75 Mn |
| Dec 31, 2022 | 660.62 Mn |
| Sep 30, 2022 | 621.03 Mn |
| Jun 30, 2022 | 584.90 Mn |
| Mar 31, 2022 | 525.34 Mn |
| Dec 31, 2021 | 509.85 Mn |
| Sep 30, 2021 | 457.84 Mn |
API Access
Datadog Non-Current Assets 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=non-current-assets&ticker=DDOG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "non-current-assets", "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=non-current-assets&ticker=DDOG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();