Cintas (CTAS) Operating Expenses (2009 - 2026)
Cintas (CTAS) recorded Operating Expenses of $792.12 million in fiscal Q4 2026 (quarter ended May 31, 2026), up 8.7% from $728.54 million a year earlier and up 0.5% from the prior quarter.
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Analysis
Cintas (CTAS) Operating Expenses (2009 - 2026) Analysis & Trends
For FY2026 (ended May 31, 2026), Cintas reported Operating Expenses of $3.1 billion, up 10.2% from FY2025.
- Annual Operating Expenses has increased for five straight fiscal years, with a five-year compound annual growth rate of 10.0% (FY2021 to FY2026).
- Across earlier fiscal years, Operating Expenses came in at $2.81 billion in FY2025 (+7.5%), $2.62 billion in FY2024 (+10.4%), $2.37 billion in FY2023 (+15.9%) and $2.04 billion in FY2022 (+6.0%).
- The fiscal Q4 2026 figure is the highest quarterly Operating Expenses in data going back to fiscal Q1 2010.
- On a year-over-year basis, Operating Expenses has increased for 21 consecutive quarters, with growth averaging 8.6% over the last eight quarters.
- The year-over-year growth in Operating Expenses has ranged between 1.6% (fiscal Q3 2022) and 19.7% (fiscal Q3 2023) over the last five years.
- Per Business Quant, the preceding three fiscal quarters came in at $788.55 million (Q3 2026), $756.77 million (Q2 2026) and $748.7 million (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Accenture | 118.39 Bn | 78.80 Bn | 6.13 Bn | 15.54 Bn |
| 2 | Cintas | 79.13 Bn | 78.31 Bn | 1.48 Bn | 792.12 Mn |
| 3 | Iron Mountain | 33.17 Bn | 32.69 Bn | 1.07 Bn | 1.66 Bn |
| 4 | APi | 16.75 Bn | 13.79 Bn | 703.00 Mn | 528.00 Mn |
| 5 | Rollins | 14.67 Bn | 14.22 Bn | 569.95 Mn | 877.22 Mn |
| 6 | Aramark | 14.35 Bn | 12.37 Bn | 430.34 Mn | 4.84 Bn |
| 7 | UL Solutions | 13.11 Bn | 11.89 Bn | 417.00 Mn | 267.00 Mn |
| 8 | Gartner | 11.73 Bn | 5.42 Bn | 1.19 Bn | 1.30 Bn |
| 9 | Rentokil Initial | 10.12 Bn | 3.46 Bn | - | - |
| 10 | Tetra Tech | 8.63 Bn | 7.74 Bn | 243.21 Mn | 5.70 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| May 31, 2026 | 792.12 Mn |
| Feb 28, 2026 | 788.55 Mn |
| Nov 30, 2025 | 756.77 Mn |
| Aug 31, 2025 | 748.70 Mn |
| May 31, 2025 | 728.54 Mn |
| Feb 28, 2025 | 709.49 Mn |
| Nov 30, 2024 | 685.31 Mn |
| Aug 31, 2024 | 691.10 Mn |
| May 31, 2024 | 667.86 Mn |
| Feb 29, 2024 | 667.05 Mn |
| Nov 30, 2023 | 641.87 Mn |
| Aug 31, 2023 | 641.02 Mn |
| May 31, 2023 | 617.98 Mn |
| Feb 28, 2023 | 587.22 Mn |
| Nov 30, 2022 | 577.51 Mn |
| Aug 31, 2022 | 587.99 Mn |
| May 31, 2022 | 541.76 Mn |
| Feb 28, 2022 | 490.55 Mn |
| Nov 30, 2021 | 503.91 Mn |
| Aug 31, 2021 | 508.66 Mn |
API Access
Cintas 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=CTAS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "CTAS", "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=CTAS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();