Appsoft Technologies (ASFT) Operating Expenses (2015 - 2026)
Appsoft Technologies' Operating Expenses came in at $16,937 for Q2 2026, down 44.3% from $30,396 a year earlier and down 17.2% from the prior quarter.
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Analysis
Appsoft Technologies (ASFT) Operating Expenses (2015 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Appsoft Technologies reported Operating Expenses of $75,039, down 14.8% year-over-year; for FY2025, it was $93,643, up 53.9% from FY2024.
- Operating Expenses has increased in each of the last three years, with a five-year compound annual growth rate of 14.9% (FY2020 to FY2025).
- Going back by year, Operating Expenses was $60,847 in FY2024 (+4.3%), $58,342 in FY2023 (+85.6%), $31,439 in FY2022 (-38.2%) and $50,854 in FY2021 (+8.6%).
- The Q2 2026 figure represents the lowest quarterly Operating Expenses since Q3 2024.
- Year-over-year, Operating Expenses has declined for three consecutive quarters, with growth averaging 89.8% over the last eight quarters.
- The fastest year-over-year change in Operating Expenses over five years came in Q4 2024 (growth of 372.3%), and the weakest in Q3 2022 (a decline of 64.3%).
- Business Quant data shows ASFT's Operating Expenses at $20,449 (Q1 2026), $18,823 (Q4 2025) and $18,830 (Q3 2025) in the three quarters before Q2 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Alphabet | 4,164.76 Bn | 3,922.28 Bn | 73.85 Bn | 79.03 Bn |
| 2 | Meta Platforms | 1,823.40 Bn | 1,525.92 Bn | 49.47 Bn | 42.03 Bn |
| 3 | Netflix | 288.27 Bn | 248.47 Bn | 6.52 Bn | 1.51 Bn |
| 4 | Alibaba Group Holding | 252.72 Bn | 70.58 Bn | 15.11 Bn | -5.17 Bn |
| 5 | Shopify | 186.52 Bn | 163.71 Bn | 1.71 Bn | 1.22 Bn |
| 6 | Uber Technologies | 139.11 Bn | 111.09 Bn | 6.38 Bn | 12.30 Bn |
| 7 | Booking Holdings | 123.13 Bn | 56.18 Bn | - | 4.85 Bn |
| 8 | PDD Holdings | 111.72 Bn | -140.21 Bn | 9.45 Bn | -5.39 Bn |
| 9 | AppLovin | 103.35 Bn | 93.38 Bn | 1.70 Bn | 429.41 Mn |
| 10 | Appsoft Technologies | 1.12 Mn | 1.12 Mn | - | 16,937.00 |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 16,937.00 |
| Mar 31, 2026 | 20,449.00 |
| Dec 31, 2025 | 18,823.00 |
| Sep 30, 2025 | 18,830.00 |
| Jun 30, 2025 | 30,396.00 |
| Mar 31, 2025 | 25,594.00 |
| Dec 31, 2024 | 21,075.00 |
| Sep 30, 2024 | 11,042.00 |
| Jun 30, 2024 | 13,739.00 |
| Mar 31, 2024 | 14,986.00 |
| Dec 31, 2023 | 4,462.00 |
| Sep 30, 2023 | 4,275.00 |
| Jun 30, 2023 | 22,532.00 |
| Mar 31, 2023 | 27,068.00 |
| Dec 31, 2022 | 7,615.00 |
| Sep 30, 2022 | 6,513.00 |
| Jun 30, 2022 | 9,303.00 |
| Mar 31, 2022 | 8,008.00 |
| Dec 31, 2021 | 8,831.00 |
| Sep 30, 2021 | 18,228.00 |
API Access
Appsoft Technologies Operating Expenses 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=operating-expenses&ticker=ASFT&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "ASFT", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=ASFT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();