Aurora Mobile (JG) Total Liabilities (2017 - 2026)
Aurora Mobile (JG) posted Total Liabilities of $46.47 million for Q2 2026, up 17.4% from $39.57 million a year earlier and up 11.4% from the prior quarter.
Aurora Mobile (JG) Total Liabilities (2017 - 2026) Analysis & Trends
At the end of FY2025, Aurora Mobile's Total Liabilities came in at $45.29 million, up 18.6% from FY2024.
- Annual Total Liabilities shows a five-year compound annual growth rate of -8.7% (FY2020 to FY2025).
- In prior years, Aurora Mobile's Total Liabilities was $38.17 million in FY2024 (+10.0%), $34.71 million in FY2023 (-21.6%), $44.28 million in FY2022 (-25.8%) and $59.65 million in FY2021 (-16.5%).
- The Q2 2026 figure stands as the highest quarterly Total Liabilities since Q1 2022.
- On a year-over-year basis, Total Liabilities has increased in each of the last seven quarters, with growth averaging 13.4% over the last eight quarters.
- The strongest year-over-year quarter for Total Liabilities in the past five years was Q2 2025, with growth of 23.2%; the weakest was Q1 2023, with a decline of 40.2%.
- According to Business Quant data, Total Liabilities for the three prior quarters was $41.71 million (Q1 2026), $44.66 million (Q4 2025) and $40.62 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 319.55 Bn | 304.63 Bn | 2.30 Bn | 20.97 Bn |
| 2 | CrowdStrike Holdings | 265.46 Bn | 245.90 Bn | 1.10 Bn | 6.89 Bn |
| 3 | Fortinet | 129.36 Bn | 115.29 Bn | 1.64 Bn | 9.31 Bn |
| 4 | Snowflake | 115.65 Bn | 102.97 Bn | 1.04 Bn | 6.54 Bn |
| 5 | Datadog | 96.45 Bn | 78.09 Bn | 881.34 Mn | 3.18 Bn |
| 6 | Axon Enterprise | 34.50 Bn | 29.03 Bn | 546.45 Mn | 3.81 Bn |
| 7 | Okta | 33.79 Bn | 23.89 Bn | 641.00 Mn | 2.17 Bn |
| 8 | Zscaler | 32.51 Bn | 18.63 Bn | - | 5.27 Bn |
| 9 | Baidu | 29.59 Bn | -44.04 Bn | 1.47 Mn | 27.69 Bn |
| 10 | Aurora Mobile | 5.16 Bn | 5.09 Bn | 10.20 Mn | 46.47 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 46.47 Mn |
| Mar 31, 2026 | 41.71 Mn |
| Dec 31, 2025 | 44.66 Mn |
| Sep 30, 2025 | 40.62 Mn |
| Jun 30, 2025 | 39.57 Mn |
| Mar 31, 2025 | 38.22 Mn |
| Dec 31, 2024 | 38.79 Mn |
| Sep 30, 2024 | 36.82 Mn |
| Jun 30, 2024 | 32.12 Mn |
| Mar 31, 2024 | 32.17 Mn |
| Dec 31, 2023 | 34.23 Mn |
| Sep 30, 2023 | 36.85 Mn |
| Jun 30, 2023 | 38.00 Mn |
| Mar 31, 2023 | 41.23 Mn |
| Dec 31, 2022 | 42.94 Mn |
| Sep 30, 2022 | 39.90 Mn |
| Jun 30, 2022 | 41.01 Mn |
| Mar 31, 2022 | 68.98 Mn |
| Dec 31, 2021 | 59.45 Mn |
| Sep 30, 2021 | 59.08 Mn |
Aurora Mobile Total 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-liabilities&ticker=JG&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "JG", "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-liabilities&ticker=JG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();