Diebold Nixdorf (DBD) EV to EBITDA (2023 - 2026)
Diebold Nixdorf's EV to EBITDA came in at 7.05 for Q2 2026, up 33.6% from 5.28 a year earlier and up 15.9% from the prior quarter.
Diebold Nixdorf (DBD) EV to EBITDA (2023 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Diebold Nixdorf reported EV to EBITDA of 4.21, up 69.2% year-over-year; for FY2025, it came in at 5.39, up 68.8% from FY2024.
- Going back by year, EV to EBITDA was 3.19 in FY2024 (+21.3%) and 2.63 in FY2023.
- The Q2 2026 figure represents the highest quarterly EV to EBITDA in data going back to Q1 2024.
- Year-over-year, EV to EBITDA has increased for five consecutive quarters, with growth averaging 28.3% over the last six quarters.
- Business Quant data shows DBD's EV to EBITDA at 6.08 (Q1 2026), 5.09 (Q4 2025) and 4.74 (Q3 2025) in the three quarters before Q2 2026.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Bryn | 945.00 Bn | 945.00 Bn | - |
| 2 | Veeva Systems | 45.35 Bn | 17.59 Bn | 695.95 Mn |
| 3 | Samsara | 22.20 Bn | 18.97 Bn | 392.58 Mn |
| 4 | Toast | 17.53 Bn | 10.20 Bn | 516.00 Mn |
| 5 | Ptc | 15.47 Bn | 14.28 Bn | 490.47 Mn |
| 6 | Trimble | 13.41 Bn | 12.48 Bn | 674.90 Mn |
| 7 | Duolingo | 12.56 Bn | 7.73 Bn | 216.74 Mn |
| 8 | Manhattan Associates | 11.77 Bn | 10.77 Bn | 168.33 Mn |
| 9 | Costar | 10.95 Bn | 4.91 Bn | 728.00 Mn |
| 10 | Diebold Nixdorf | 2.07 Bn | 741.27 Mn | 239.60 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 7.05 |
| Mar 31, 2026 | 6.08 |
| Dec 31, 2025 | 5.09 |
| Sep 30, 2025 | 4.74 |
| Jun 30, 2025 | 5.28 |
| Mar 31, 2025 | 3.58 |
| Dec 31, 2024 | 3.57 |
| Sep 30, 2024 | 4.48 |
| Jun 30, 2024 | 3.84 |
| Mar 31, 2024 | 4.44 |
| Sep 30, 2023 | 1.81 |
Diebold Nixdorf EV to EBITDA 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=ev-to-ebitda&ticker=DBD&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "ev-to-ebitda", "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=ev-to-ebitda&ticker=DBD&period=max&api_key=YOUR_API_KEY");
const data = await res.json();