Nano Dimension (NNDM) Operating Expenses (2016 - 2026)
Nano Dimension's Operating Expenses came in at $25.46 million for Q2 2026, down 45.8% from $47.01 million a year earlier and down 4.1% from the prior quarter.
Nano Dimension (NNDM) Operating Expenses (2016 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Nano Dimension reported Operating Expenses of $120.72 million, up 1.5% year-over-year; for FY2025, it came in at $97.4 million, down 14.6% from FY2024.
- Operating Expenses has declined in each of the last three years, though with a five-year compound annual growth rate of 26.4% (FY2020 to FY2025).
- Going back by year, Operating Expenses was $114 million in FY2024 (-25.9%), $153.75 million in FY2023 (-17.6%), $186.55 million in FY2022 (+204.2%) and $61.33 million in FY2021 (+103.3%).
- The Q2 2026 figure represents the lowest quarterly Operating Expenses since Q1 2025.
- Year-over-year, Operating Expenses increased in four of the last eight quarters, with growth averaging 14.0%.
- The fastest year-over-year change in Operating Expenses over five years came in Q1 2022 (growth of 243.9%), and the weakest in Q2 2026 (a decline of 45.8%).
- Business Quant data shows NNDM's Operating Expenses at $26.54 million (Q1 2026), $27.45 million (Q4 2025) and $41.27 million (Q3 2025) in the three quarters before Q2 2026.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Caterpillar | 268.42 Bn | 239.73 Bn | 7.35 Bn | 16.47 Bn |
| 2 | Amphenol | 200.18 Bn | 194.88 Bn | 4.02 Bn | 995.40 Mn |
| 3 | Deere | 182.33 Bn | 150.42 Bn | 4.66 Bn | 10.73 Bn |
| 4 | Eaton | 139.45 Bn | 136.39 Bn | 3.11 Bn | 1.52 Bn |
| 5 | Parker-Hannifin | 120.96 Bn | 118.84 Bn | 2.26 Bn | 1.07 Bn |
| 6 | 3M | 97.01 Bn | 78.45 Bn | 2.78 Bn | 5.03 Bn |
| 7 | Emerson Electric | 93.92 Bn | 86.65 Bn | 2.66 Bn | 1.34 Bn |
| 8 | Illinois Tool Works | 80.83 Bn | 77.51 Bn | 1.91 Bn | - |
| 9 | Vertiv Holdings | 75.75 Bn | 64.60 Bn | 1.64 Bn | 557.10 Mn |
| 10 | Nano Dimension | 283.75 Mn | -942.43 Mn | 14.44 Mn | 44.35 Mn |
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 44.35 Mn |
| Jun 30, 2026 | 25.46 Mn |
| Mar 31, 2026 | 26.54 Mn |
| Dec 31, 2025 | 27.45 Mn |
| Sep 30, 2025 | 41.27 Mn |
| Jun 30, 2025 | 47.01 Mn |
| Mar 31, 2025 | 12.79 Mn |
| Dec 31, 2024 | 31.49 Mn |
| Sep 30, 2024 | 27.67 Mn |
| Jun 30, 2024 | 28.09 Mn |
| Mar 31, 2024 | 20.80 Mn |
| Dec 31, 2023 | 29.62 Mn |
| Sep 30, 2023 | 35.22 Mn |
| Jun 30, 2023 | 30.25 Mn |
| Mar 31, 2023 | 30.28 Mn |
| Dec 31, 2022 | 30.08 Mn |
| Sep 30, 2022 | 25.95 Mn |
| Jun 30, 2022 | 25.57 Mn |
| Mar 31, 2022 | 24.61 Mn |
| Dec 31, 2021 | 21.57 Mn |
Nano Dimension 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=NNDM&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "NNDM", "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=NNDM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();