Aurora Mobile (JG) Total Non-Current Liabilities (2017 - 2026)
Aurora Mobile's Total Non-Current Liabilities was $98.01 million in Q2 2026, up 5.2% from $93.14 million a year earlier.
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Analysis
Aurora Mobile (JG) Total Non-Current Liabilities (2017 - 2026) Analysis & Trends
At the end of FY2025, Total Non-Current Liabilities at Aurora Mobile came in at $96.66 million, up 3.7% from FY2024.
- Total Non-Current Liabilities shows a five-year compound annual growth rate of 153.3% (FY2020 to FY2025).
- In earlier years, Total Non-Current Liabilities was $93.21 million in FY2024 (-25.4%), $125.01 million in FY2023 (-6.5%), $133.73 million in FY2022 (+289.3%) and $34.35 million in FY2021.
- The Q2 2026 figure marks the highest quarterly Total Non-Current Liabilities since Q4 2023.
- Compared with a year earlier, Total Non-Current Liabilities was higher in four of the last seven quarters, with growth averaging 60.5%.
- The best year-over-year quarter for Total Non-Current Liabilities over five years was Q3 2024 (growth of 258.3%); the worst was Q2 2023 (a decline of 99.0%).
- Per Business Quant data, JG's Total Non-Current Liabilities in the three quarters before Q2 2026 was $1.88 million (Q1 2026), $95.31 million (Q4 2025) and $2.05 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Non-Current Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 316.55 Bn | 301.63 Bn | 2.30 Bn | 19.68 Bn |
| 2 | CrowdStrike Holdings | 269.03 Bn | 249.47 Bn | 1.10 Bn | 6.57 Bn |
| 3 | Fortinet | 129.08 Bn | 115.01 Bn | 1.64 Bn | 9.17 Bn |
| 4 | Snowflake | 116.42 Bn | 103.73 Bn | 1.04 Bn | 6.45 Bn |
| 5 | Datadog | 96.40 Bn | 78.04 Bn | 881.34 Mn | 3.16 Bn |
| 6 | Okta | 34.24 Bn | 24.34 Bn | 641.00 Mn | 2.11 Bn |
| 7 | Axon Enterprise | 34.08 Bn | 28.61 Bn | 546.45 Mn | 3.68 Bn |
| 8 | Zscaler | 32.34 Bn | 18.46 Bn | - | 5.20 Bn |
| 9 | Baidu | 29.50 Bn | -44.13 Bn | 1.47 Mn | 12.63 Bn |
| 10 | Aurora Mobile | 4.82 Bn | 4.75 Bn | 10.20 Mn | 98.01 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 98.01 Mn |
| Mar 31, 2026 | 1.88 Mn |
| Dec 31, 2025 | 95.31 Mn |
| Sep 30, 2025 | 2.05 Mn |
| Jun 30, 2025 | 93.14 Mn |
| Mar 31, 2025 | 2.17 Mn |
| Dec 31, 2024 | 94.72 Mn |
| Sep 30, 2024 | 2.87 Mn |
| Jun 30, 2024 | 717,000.00 |
| Mar 31, 2024 | 668,000.00 |
| Dec 31, 2023 | 123.27 Mn |
| Sep 30, 2023 | 800,000.00 |
| Jun 30, 2023 | 1.36 Mn |
| Mar 31, 2023 | 2.13 Mn |
| Dec 31, 2022 | 129.69 Mn |
| Sep 30, 2022 | 1.94 Mn |
| Jun 30, 2022 | 138.46 Mn |
| Mar 31, 2022 | 1.84 Mn |
| Dec 31, 2021 | 135.33 Mn |
| Sep 30, 2021 | 1.17 Mn |
API Access
Aurora Mobile 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=JG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-non-current-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-non-current-liabilities&ticker=JG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();