Xpel (XPEL) Operating Expenses (2018 - 2026)
Xpel's Operating Expenses was $39.93 million in Q2 2026, up 16.7% from $34.22 million a year earlier and up 4.5% from the prior quarter.
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Analysis
Xpel (XPEL) Operating Expenses (2018 - 2026) Analysis & Trends
On a trailing twelve-month basis, Xpel's Operating Expenses was $149.52 million through Jun 30, 2026, up 16.9% year-over-year; for FY2025, it was $138.37 million, up 17.1% from FY2024.
- Operating Expenses has now increased for seven consecutive years, with a five-year compound annual growth rate of 35.2% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $118.21 million in FY2024 (+23.9%), $95.44 million in FY2023 (+29.7%), $73.58 million in FY2022 (+40.0%) and $52.56 million in FY2021 (+71.5%).
- The Q2 2026 figure marks the highest quarterly Operating Expenses in data going back to Q2 2018.
- Compared with a year earlier, Operating Expenses has increased for 24 straight quarters, with growth averaging 17.8% over the last eight quarters.
- Over the past five years, the year-over-year growth in Operating Expenses ranged from 13.9% (Q4 2025) to 87.3% (Q4 2021).
- Per Business Quant data, XPEL's Operating Expenses in the three quarters before Q2 2026 was $38.22 million (Q1 2026), $35.7 million (Q4 2025) and $35.67 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Tesla | 1,148.52 Bn | 983.08 Bn | 4.75 Bn | 4.35 Bn |
| 2 | Toyota Motor | 242.17 Bn | -164.57 Bn | 20.95 Bn | 78.18 Bn |
| 3 | Ferrari | 145.86 Bn | 138.30 Bn | 1.18 Bn | 471.42 Mn |
| 4 | Honda Motor | 143.38 Bn | 10.76 Bn | 8.46 Bn | -34.70 Bn |
| 5 | General Motors | 67.56 Bn | -37.30 Bn | 7.33 Bn | 46.57 Bn |
| 6 | Ford Motor | 47.24 Bn | -95.23 Bn | 6.08 Bn | 47.66 Bn |
| 7 | Rivian Automotive | 20.35 Bn | -2.94 Bn | 179.00 Mn | 1.02 Bn |
| 8 | Magna International | 17.85 Bn | 15.41 Bn | 1.61 Bn | 273.20 Mn |
| 9 | Stellantis | 12.63 Bn | -153.36 Bn | 5.55 Bn | 4.85 Bn |
| 10 | Xpel | 1.22 Bn | 1.03 Bn | 63.14 Mn | 39.93 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 39.93 Mn |
| Mar 31, 2026 | 38.22 Mn |
| Dec 31, 2025 | 35.70 Mn |
| Sep 30, 2025 | 35.67 Mn |
| Jun 30, 2025 | 34.22 Mn |
| Mar 31, 2025 | 32.78 Mn |
| Dec 31, 2024 | 31.36 Mn |
| Sep 30, 2024 | 29.53 Mn |
| Jun 30, 2024 | 28.68 Mn |
| Mar 31, 2024 | 28.65 Mn |
| Dec 31, 2023 | 26.71 Mn |
| Sep 30, 2023 | 23.90 Mn |
| Jun 30, 2023 | 23.80 Mn |
| Mar 31, 2023 | 21.03 Mn |
| Dec 31, 2022 | 20.20 Mn |
| Sep 30, 2022 | 18.46 Mn |
| Jun 30, 2022 | 17.23 Mn |
| Mar 31, 2022 | 17.68 Mn |
| Dec 31, 2021 | 16.16 Mn |
| Sep 30, 2021 | 14.09 Mn |
API Access
Xpel 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=XPEL&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "XPEL", "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=XPEL&period=max&api_key=YOUR_API_KEY");
const data = await res.json();