Datadog (DDOG) Total Non-Current Liabilities (2018 - 2026)
Datadog (DDOG) posted Total Non-Current Liabilities of $3.16 billion for Q2 2026, up 21.2% from $2.61 billion a year earlier and up 7.1% from the prior quarter.
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Analysis
Datadog (DDOG) Total Non-Current Liabilities (2018 - 2026) Analysis & Trends
At the end of FY2025, Datadog's Total Non-Current Liabilities came in at $2.9 billion, down 5.3% from FY2024.
- Annual Total Non-Current Liabilities shows a five-year compound annual growth rate of 25.6% (FY2020 to FY2025).
- In prior years, Datadog's Total Non-Current Liabilities was $3.06 billion in FY2024 (+60.7%), $1.9 billion in FY2023 (+19.9%), $1.59 billion in FY2022 (+19.4%) and $1.33 billion in FY2021 (+43.2%).
- The Q2 2026 figure stands as the highest quarterly Total Non-Current Liabilities in data going back to Q4 2018.
- On a year-over-year basis, Total Non-Current Liabilities increased in six of the last eight quarters, with growth averaging 26.0%.
- The strongest year-over-year quarter for Total Non-Current Liabilities in the past five years was Q4 2024, with growth of 60.7%; the weakest was Q4 2025, with a decline of 5.3%.
- According to Business Quant data, Total Non-Current Liabilities for the three prior quarters was $2.95 billion (Q1 2026), $2.9 billion (Q4 2025) and $2.59 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Non-Current Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 256.01 Bn | 241.08 Bn | 2.30 Bn | 19.68 Bn |
| 2 | CrowdStrike Holdings | 231.46 Bn | 211.86 Bn | 1.10 Bn | 6.57 Bn |
| 3 | Fortinet | 123.83 Bn | 109.76 Bn | 1.64 Bn | 9.17 Bn |
| 4 | Snowflake | 107.27 Bn | 94.59 Bn | 1.04 Bn | 6.45 Bn |
| 5 | Datadog | 88.05 Bn | 69.69 Bn | 881.34 Mn | 3.16 Bn |
| 6 | Zscaler | 33.23 Bn | 19.35 Bn | - | 5.20 Bn |
| 7 | Okta | 32.56 Bn | 22.66 Bn | 641.00 Mn | 2.11 Bn |
| 8 | Axon Enterprise | 31.41 Bn | 25.93 Bn | 546.45 Mn | 3.62 Bn |
| 9 | MongoDB | 27.26 Bn | 17.73 Bn | 569.77 Mn | 770.18 Mn |
| 10 | F5 | 25.06 Bn | 19.47 Bn | 711.51 Mn | 1.20 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 3.16 Bn |
| Mar 31, 2026 | 2.95 Bn |
| Dec 31, 2025 | 2.90 Bn |
| Sep 30, 2025 | 2.59 Bn |
| Jun 30, 2025 | 2.61 Bn |
| Mar 31, 2025 | 3.08 Bn |
| Dec 31, 2024 | 3.06 Bn |
| Sep 30, 2024 | 2.00 Bn |
| Jun 30, 2024 | 2.00 Bn |
| Mar 31, 2024 | 1.93 Bn |
| Dec 31, 2023 | 1.90 Bn |
| Sep 30, 2023 | 1.73 Bn |
| Jun 30, 2023 | 1.66 Bn |
| Mar 31, 2023 | 1.62 Bn |
| Dec 31, 2022 | 1.59 Bn |
| Sep 30, 2022 | 1.50 Bn |
| Jun 30, 2022 | 1.43 Bn |
| Mar 31, 2022 | 1.40 Bn |
| Dec 31, 2021 | 1.33 Bn |
| Sep 30, 2021 | 1.23 Bn |
API Access
Datadog Total Non-Current Liabilities 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=total-non-current-liabilities&ticker=DDOG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-non-current-liabilities", "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=total-non-current-liabilities&ticker=DDOG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();