Diebold Nixdorf (DBD) Operating Expenses (2009 - 2026)
Diebold Nixdorf's Operating Expenses was $182.2 million in Q2 2026, up 2.5% from $177.8 million a year earlier and up 1.0% from the prior quarter.
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Analysis
Diebold Nixdorf (DBD) Operating Expenses (2009 - 2026) Analysis & Trends
On a trailing twelve-month basis, Diebold Nixdorf's Operating Expenses was $731.3 million through Jun 30, 2026, unchanged year-over-year; for FY2025, it came in at $719.2 million, down 2.5% from FY2024.
- In earlier years, Operating Expenses was $737.9 million in FY2024 (+183.2%), $260.6 million in FY2023 (-73.1%), $969 million in FY2022 (+6.9%) and $906.3 million in FY2021.
- Quarterly Operating Expenses has moved between $171.3 million (Q3 2025) and $268.7 million (Q1 2022) over five years.
- Compared with a year earlier, Operating Expenses has increased for three straight quarters, with an average decline of 0.5% over the last six quarters.
- The best year-over-year quarter for Operating Expenses over five years was Q4 2022 (growth of 24.3%); the worst was Q3 2022 (a decline of 15.0%).
- Per Business Quant data, DBD's Operating Expenses in the three quarters before Q2 2026 was $180.4 million (Q1 2026), $197.4 million (Q4 2025) and $171.3 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Bryn | 945.00 Bn | 945.00 Bn | - | 178,646.00 |
| 2 | Veeva Systems | 45.35 Bn | 17.59 Bn | 695.95 Mn | 420.93 Mn |
| 3 | Samsara | 22.20 Bn | 18.97 Bn | 392.58 Mn | 387.71 Mn |
| 4 | Toast | 17.53 Bn | 10.20 Bn | 516.00 Mn | 364.00 Mn |
| 5 | Ptc | 15.47 Bn | 14.28 Bn | 490.47 Mn | 323.96 Mn |
| 6 | Trimble | 13.41 Bn | 12.48 Bn | 674.90 Mn | 542.90 Mn |
| 7 | Duolingo | 12.56 Bn | 7.73 Bn | 216.74 Mn | 182.80 Mn |
| 8 | Manhattan Associates | 11.77 Bn | 10.77 Bn | 168.33 Mn | 231.56 Mn |
| 9 | Costar | 10.95 Bn | 4.91 Bn | 728.00 Mn | 652.00 Mn |
| 10 | Diebold Nixdorf | 2.07 Bn | 741.27 Mn | 239.60 Mn | 182.20 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 182.20 Mn |
| Mar 31, 2026 | 180.40 Mn |
| Dec 31, 2025 | 197.40 Mn |
| Sep 30, 2025 | 171.30 Mn |
| Jun 30, 2025 | 177.80 Mn |
| Mar 31, 2025 | 172.80 Mn |
| Dec 31, 2024 | 190.40 Mn |
| Sep 30, 2024 | 190.20 Mn |
| Jun 30, 2024 | 172.50 Mn |
| Mar 31, 2024 | 184.80 Mn |
| Sep 30, 2023 | 92.70 Mn |
| Jun 30, 2023 | 229.10 Mn |
| Mar 31, 2023 | 211.40 Mn |
| Dec 31, 2022 | 259.70 Mn |
| Sep 30, 2022 | 188.30 Mn |
| Jun 30, 2022 | 252.30 Mn |
| Mar 31, 2022 | 268.70 Mn |
| Dec 31, 2021 | 208.90 Mn |
| Sep 30, 2021 | 221.40 Mn |
| Jun 30, 2021 | 239.00 Mn |
API Access
Diebold Nixdorf 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=DBD&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "DBD", "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=DBD&period=max&api_key=YOUR_API_KEY");
const data = await res.json();