Datadog (DDOG) Receivables (2018 - 2026)
Datadog's Receivables was $975.55 million in Q2 2026, up 40.1% from $696.47 million a year earlier and up 19.8% from the prior quarter.
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Analysis
Datadog (DDOG) Receivables (2018 - 2026) Analysis & Trends
At the end of FY2025, Receivables at Datadog came in at $868.26 million, up 28.5% from FY2024.
- Receivables has now increased for seven consecutive years, with a five-year compound annual growth rate of 36.5% (FY2020 to FY2025).
- In earlier years, Receivables was $675.92 million in FY2024 (+18.5%), $570.48 million in FY2023 (+24.1%), $459.55 million in FY2022 (+46.8%) and $313.02 million in FY2021 (+70.6%).
- The Q2 2026 figure marks the highest quarterly Receivables in data going back to Q4 2018.
- Compared with a year earlier, Receivables has increased for 20 straight quarters, with growth averaging 23.5% over the last eight quarters.
- Across the past five years, year-over-year growth in Receivables ran from 8.9% in Q1 2025 to 87.9% in Q3 2021.
- Per Business Quant data, DDOG's Receivables in the three quarters before Q2 2026 was $814.53 million (Q1 2026), $868.26 million (Q4 2025) and $637.65 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Receivables (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 305.41 Bn | 290.49 Bn | 2.30 Bn | 3.63 Bn |
| 2 | CrowdStrike Holdings | 258.16 Bn | 238.60 Bn | 1.10 Bn | 1.04 Bn |
| 3 | Fortinet | 127.25 Bn | 113.18 Bn | 1.64 Bn | 1.46 Bn |
| 4 | Snowflake | 118.44 Bn | 105.76 Bn | 1.04 Bn | 718.46 Mn |
| 5 | Datadog | 96.25 Bn | 77.89 Bn | 881.34 Mn | 975.55 Mn |
| 6 | Axon Enterprise | 34.94 Bn | 29.46 Bn | 546.45 Mn | 1.52 Bn |
| 7 | MongoDB | 33.06 Bn | 23.53 Bn | 569.77 Mn | 483.22 Mn |
| 8 | Okta | 32.62 Bn | 22.72 Bn | 641.00 Mn | 469.00 Mn |
| 9 | Zscaler | 31.48 Bn | 17.60 Bn | - | 1.15 Bn |
| 10 | Baidu | 29.71 Bn | -43.91 Bn | 1.47 Mn | 2.09 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 975.55 Mn |
| Mar 31, 2026 | 814.53 Mn |
| Dec 31, 2025 | 868.26 Mn |
| Sep 30, 2025 | 637.65 Mn |
| Jun 30, 2025 | 696.47 Mn |
| Mar 31, 2025 | 573.57 Mn |
| Dec 31, 2024 | 675.92 Mn |
| Sep 30, 2024 | 575.26 Mn |
| Jun 30, 2024 | 611.09 Mn |
| Mar 31, 2024 | 526.56 Mn |
| Dec 31, 2023 | 570.48 Mn |
| Sep 30, 2023 | 459.95 Mn |
| Jun 30, 2023 | 399.80 Mn |
| Mar 31, 2023 | 430.05 Mn |
| Dec 31, 2022 | 459.55 Mn |
| Sep 30, 2022 | 411.93 Mn |
| Jun 30, 2022 | 363.70 Mn |
| Mar 31, 2022 | 324.44 Mn |
| Dec 31, 2021 | 313.02 Mn |
| Sep 30, 2021 | 259.70 Mn |
API Access
Datadog Receivables 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=receivables&ticker=DDOG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "receivables", "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=receivables&ticker=DDOG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();