Ufp Technologies (UFPT) Operating Expenses (2010 - 2026)
Ufp Technologies (UFPT) reported Operating Expenses of $22.83 million for Q2 2026, up 20.4% from $18.96 million a year earlier and up 8.6% from the prior quarter.
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Analysis
Ufp Technologies (UFPT) Operating Expenses (2010 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Ufp Technologies' Operating Expenses came in at $83.89 million, up 13.8% year-over-year; for FY2025, it was $77.77 million, up 20.1% from FY2024.
- Operating Expenses has increased for five consecutive years, with a five-year compound annual growth rate of 23.1% (FY2020 to FY2025).
- By year, Operating Expenses came in at $64.74 million in FY2024 (+27.2%), $50.89 million in FY2023 (+8.7%), $46.82 million in FY2022 (+56.5%) and $29.91 million in FY2021 (+8.8%).
- The Q2 2026 figure ranks as the highest quarterly Operating Expenses in data going back to Q2 2010.
- Year over year, Operating Expenses has now increased in each of the last 12 quarters, with growth averaging 24.9% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q2 2022 (growth of 70.4%); the low point was Q2 2023 (a decline of 0.2%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $21.02 million (Q1 2026), $20.97 million (Q4 2025) and $19.08 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 249.71 Bn | 228.86 Bn | 4.88 Bn | 9.91 Bn |
| 2 | Abbott Laboratories | 171.00 Bn | 142.08 Bn | 7.27 Bn | 10.90 Bn |
| 3 | Danaher | 155.79 Bn | 139.61 Bn | 3.61 Bn | 2.48 Bn |
| 4 | Intuitive Surgical | 143.91 Bn | 123.46 Bn | 1.96 Bn | 988.50 Mn |
| 5 | Medtronic | 110.42 Bn | 76.29 Bn | 6.34 Bn | 4.06 Bn |
| 6 | Stryker | 105.71 Bn | 91.82 Bn | 4.50 Bn | 2.84 Bn |
| 7 | Boston Scientific | 63.26 Bn | 58.27 Bn | 3.85 Bn | 2.67 Bn |
| 8 | Edwards Lifesciences | 49.80 Bn | 33.91 Bn | 1.35 Bn | 840.10 Mn |
| 9 | Becton Dickinson | 48.78 Bn | 45.93 Bn | 2.32 Bn | 4.32 Bn |
| 10 | Ufp Technologies | 2.30 Bn | 2.23 Bn | 50.92 Mn | 22.83 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 22.83 Mn |
| Mar 31, 2026 | 21.02 Mn |
| Dec 31, 2025 | 20.97 Mn |
| Sep 30, 2025 | 19.08 Mn |
| Jun 30, 2025 | 18.96 Mn |
| Mar 31, 2025 | 18.76 Mn |
| Dec 31, 2024 | 19.46 Mn |
| Sep 30, 2024 | 16.52 Mn |
| Jun 30, 2024 | 14.84 Mn |
| Mar 31, 2024 | 13.91 Mn |
| Dec 31, 2023 | 13.12 Mn |
| Sep 30, 2023 | 12.47 Mn |
| Jun 30, 2023 | 12.30 Mn |
| Mar 31, 2023 | 13.01 Mn |
| Dec 31, 2022 | 11.89 Mn |
| Sep 30, 2022 | 11.83 Mn |
| Jun 30, 2022 | 12.32 Mn |
| Mar 31, 2022 | 10.79 Mn |
| Dec 31, 2021 | 8.41 Mn |
| Sep 30, 2021 | 6.96 Mn |
API Access
Ufp 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=UFPT&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "UFPT", "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=UFPT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();