Veeco Instruments (VECO) Operating Expenses (2010 - 2026)
Veeco Instruments (VECO) reported Operating Expenses of $62.98 million for Q2 2026, up 11.8% from $56.36 million a year earlier and up 7.7% from the prior quarter.
Veeco Instruments (VECO) Operating Expenses (2010 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Veeco Instruments' Operating Expenses came in at $240.49 million, up 2.1% year-over-year; for FY2025, it came in at $229.7 million, down 3.1% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 6.0% (FY2020 to FY2025).
- By year, Operating Expenses came in at $237.02 million in FY2024 (+10.2%), $215.12 million in FY2023 (+6.0%), $202.85 million in FY2022 (+9.3%) and $185.56 million in FY2021 (+8.1%).
- The Q2 2026 figure ranks as the highest quarterly Operating Expenses since Q4 2024.
- Year over year, Operating Expenses gained in six of the last eight quarters, with growth averaging 4.9%.
- The high point for year-over-year Operating Expenses in five years was Q4 2024 (growth of 28.7%); the low point was Q4 2025 (a decline of 11.7%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $58.49 million (Q1 2026), $61.88 million (Q4 2025) and $57.15 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nvidia | 5,257.94 Bn | 5,032.10 Bn | 72.14 Bn | 8.41 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 2,182.39 Bn | 1,807.25 Bn | 27.22 Bn | 3.13 Bn |
| 3 | Broadcom | 1,596.71 Bn | 1,522.75 Bn | 20.46 Bn | 4.50 Bn |
| 4 | Micron Technology | 1,020.41 Bn | 926.05 Bn | 47.05 Bn | 2.77 Bn |
| 5 | Advanced Micro Devices | 879.62 Bn | 836.36 Bn | 6.20 Bn | 4.21 Bn |
| 6 | Asml Holding | 640.55 Bn | 596.23 Bn | 5.90 Bn | - |
| 7 | Intel | 470.49 Bn | 339.62 Bn | 6.51 Bn | 4.71 Bn |
| 8 | Lam Research | 342.11 Bn | 318.91 Bn | 3.48 Bn | 965.25 Mn |
| 9 | Applied Materials | 328.25 Bn | 293.69 Bn | 4.59 Bn | 1.51 Bn |
| 10 | Veeco Instruments | 2.66 Bn | 1.09 Bn | 74.83 Mn | 62.98 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 62.98 Mn |
| Mar 31, 2026 | 58.49 Mn |
| Dec 31, 2025 | 61.88 Mn |
| Sep 30, 2025 | 57.15 Mn |
| Jun 30, 2025 | 56.36 Mn |
| Mar 31, 2025 | 54.32 Mn |
| Dec 31, 2024 | 70.11 Mn |
| Sep 30, 2024 | 54.88 Mn |
| Jun 30, 2024 | 58.67 Mn |
| Mar 31, 2024 | 53.37 Mn |
| Dec 31, 2023 | 54.47 Mn |
| Sep 30, 2023 | 54.61 Mn |
| Jun 30, 2023 | 53.82 Mn |
| Mar 31, 2023 | 52.21 Mn |
| Dec 31, 2022 | 49.53 Mn |
| Sep 30, 2022 | 52.39 Mn |
| Jun 30, 2022 | 51.44 Mn |
| Mar 31, 2022 | 49.50 Mn |
| Dec 31, 2021 | 46.40 Mn |
| Sep 30, 2021 | 46.75 Mn |
Veeco Instruments 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=VECO&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "VECO", "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=VECO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();