Science Applications International (SAIC) EV to EBITDA (2013 - 2026)
Science Applications International's EV to EBITDA was 6.40 in fiscal Q2 2027 (quarter ended Jul 31, 2026), down 11.2% from 7.21 a year earlier but up 16.7% from the prior quarter.
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Analysis
Science Applications International (SAIC) EV to EBITDA (2013 - 2026) Analysis & Trends
On a trailing twelve-month basis, Science Applications International's EV to EBITDA was 5.95 through Jul 31, 2026, down 14.9% year-over-year; for FY2026 (ended Jan 30, 2026), it was 6.41, down 12.3% from FY2025.
- EV to EBITDA has now declined for three consecutive fiscal years.
- In earlier fiscal years, EV to EBITDA was 7.31 in FY2025 (-2.4%), 7.49 in FY2024 (-10.7%), 8.39 in FY2023 (+18.0%) and 7.11 in FY2022 (-25.2%).
- Quarterly EV to EBITDA has moved between 5.49 (fiscal Q1 2027) and 10.94 (fiscal Q3 2025) over five years.
- Compared with a year earlier, EV to EBITDA has declined for five straight quarters, with an average decline of 4.8% over the last eight quarters.
- The best year-over-year quarter for EV to EBITDA over five years was fiscal Q3 2025 (growth of 85.3%); the worst was fiscal Q3 2026 (a decline of 43.1%).
- Per Business Quant data, SAIC's EV to EBITDA in the three fiscal quarters before Q2 2027 was 5.49 (Q1 2027), 6.41 (Q4 2026) and 6.22 (Q3 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Infosys | 50.81 Bn | 42.20 Bn | - |
| 2 | Cognizant Technology Solutions | 28.52 Bn | 22.49 Bn | 1.91 Bn |
| 3 | Cdw | 20.95 Bn | 17.85 Bn | 1.39 Bn |
| 4 | Td Synnex | 18.62 Bn | 12.78 Bn | 1.43 Bn |
| 5 | Arrow Electronics | 10.10 Bn | 9.05 Bn | 1.13 Bn |
| 6 | EPAM Systems | 7.92 Bn | 3.84 Bn | 445.94 Mn |
| 7 | Avnet | 7.12 Bn | 6.30 Bn | 865.03 Mn |
| 8 | ExlService Holdings | 6.52 Bn | 5.32 Bn | 241.14 Mn |
| 9 | Science Applications International | 6.35 Bn | 5.89 Bn | 239.00 Mn |
| 10 | Ingram Micro Holding | 6.29 Bn | 1.70 Bn | 996.93 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jul 31, 2026 | 6.40 |
| May 1, 2026 | 5.49 |
| Jan 30, 2026 | 6.41 |
| Oct 31, 2025 | 6.22 |
| Aug 1, 2025 | 7.21 |
| May 2, 2025 | 7.92 |
| Jan 31, 2025 | 7.31 |
| Nov 1, 2024 | 10.89 |
| Aug 2, 2024 | 9.70 |
| May 3, 2024 | 7.73 |
| Feb 2, 2024 | 7.43 |
| Nov 3, 2023 | 5.87 |
| Aug 4, 2023 | 6.59 |
| May 5, 2023 | 7.27 |
| Feb 3, 2023 | 8.28 |
| Oct 28, 2022 | 9.18 |
| Jul 29, 2022 | 8.38 |
| Apr 29, 2022 | 7.35 |
| Jan 28, 2022 | 7.03 |
| Oct 29, 2021 | 7.57 |
API Access
Science Applications International 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=SAIC&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "ev-to-ebitda", "ticker": "SAIC", "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=SAIC&period=max&api_key=YOUR_API_KEY");
const data = await res.json();