Applied Digital (APLD) Long-Term Debt Repayments (2022 - 2026)
Applied Digital's Long-Term Debt Repayments came in at $315.93 million for fiscal Q1 2027 (quarter ended Aug 31, 2026), up 955.5% from $29.93 million a year earlier and up 255.1% from the prior quarter.
Applied Digital (APLD) Long-Term Debt Repayments (2022 - 2026) Analysis & Trends
Over the trailing twelve months to Aug 31, 2026, Applied Digital reported Long-Term Debt Repayments of $807.51 million, up 172.0% year-over-year; for FY2026 (ended May 31, 2026), it came in at $521.51 million, up 78.0% from FY2025.
- Long-Term Debt Repayments carries a four-year compound annual growth rate of 597.0% (FY2022 to FY2026).
- Going back by fiscal year, Long-Term Debt Repayments was $293.05 million in FY2025 (+388.7%), $59.97 million in FY2024, $3.35 million in FY2023 and $221,000 in FY2022.
- The five-year range for quarterly Long-Term Debt Repayments is -$6.71 million (fiscal Q2 2023) to $400.35 million (fiscal Q2 2026).
- Year-over-year, Long-Term Debt Repayments increased in four of the last six quarters, with growth averaging 340.1%.
- The fastest year-over-year change in Long-Term Debt Repayments over five years came in fiscal Q3 2025 (growth of 987.6%), and the weakest in fiscal Q3 2026 (a decline of 98.6%).
- Business Quant data shows APLD's Long-Term Debt Repayments at $88.98 million (Q4 2026), $2.25 million (Q3 2026) and $400.35 million (Q2 2026) in the three fiscal quarters before Q1 2027.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | LT Debt Repayment (Qtr) |
|---|---|---|---|---|---|
| 1 | Microsoft | 3,964.86 Bn | 3,888.01 Bn | 60.48 Bn | - |
| 2 | International Business Machines | 222.38 Bn | 173.19 Bn | 9.91 Bn | 4.21 Bn |
| 3 | Cloudflare | 105.62 Bn | 89.15 Bn | 499.52 Mn | - |
| 4 | Equinix | 96.25 Bn | 84.81 Bn | 1.40 Bn | 756.00 Mn |
| 5 | Nebius | 41.41 Bn | 14.84 Bn | 448.70 Mn | - |
| 6 | CoreWeave | 33.73 Bn | 20.82 Bn | 1.70 Bn | 3.88 Bn |
| 7 | Verisign | 27.82 Bn | 25.04 Bn | 384.60 Mn | - |
| 8 | Nutanix | 18.15 Bn | 9.84 Bn | 651.35 Mn | - |
| 9 | GoDaddy | 13.94 Bn | 9.52 Bn | 828.20 Mn | - |
| 10 | Applied Digital | 5.62 Bn | -2.57 Bn | 96.17 Mn | 330.18 Mn |
Historic Data
| Date | Value |
|---|---|
| Aug 31, 2026 | 330.18 Mn |
| May 31, 2026 | 93.98 Mn |
| Feb 28, 2026 | 26.66 Mn |
| Nov 30, 2025 | 467.99 Mn |
| Aug 31, 2025 | 32.35 Mn |
| May 31, 2025 | 33.59 Mn |
| Feb 28, 2025 | 189.04 Mn |
| Nov 30, 2024 | 163.55 Mn |
| Aug 31, 2024 | 31.94 Mn |
| May 31, 2024 | 99.65 Mn |
| Feb 29, 2024 | 14.46 Mn |
| Nov 30, 2023 | 8.22 Mn |
| Aug 31, 2023 | 4.85 Mn |
| May 31, 2023 | 1.72 Mn |
| Feb 28, 2023 | 857,000.00 |
| Nov 30, 2022 | -6.92 Mn |
| Aug 31, 2022 | 7.70 Mn |
| May 31, 2022 | 219,000.00 |
| Feb 28, 2022 | 2,000.00 |
Applied Digital Long-Term Debt Repayments 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=long-term-debt-repayments&ticker=APLD&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "long-term-debt-repayments", "ticker": "APLD", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=long-term-debt-repayments&ticker=APLD&period=max&api_key=YOUR_API_KEY");
const data = await res.json();