Tetra Technologies (TTI) Operating Expenses (2009 - 2026)
Tetra Technologies (TTI) reported Operating Expenses of $139.94 million for Q2 2026, up 11.4% from $125.63 million a year earlier and up 18.6% from the prior quarter.
Tetra Technologies (TTI) Operating Expenses (2009 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Tetra Technologies' Operating Expenses came in at $493.09 million, up 9.4% year-over-year; for FY2025, it was $474.98 million, up 3.4% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 8.9% (FY2020 to FY2025).
- By year, Operating Expenses came in at $459.26 million in FY2024 (-2.8%), $472.62 million in FY2023 (+9.4%), $432.1 million in FY2022 (+31.3%) and $329.04 million in FY2021 (+6.1%).
- The Q2 2026 figure ranks as the highest quarterly Operating Expenses in data going back to Q2 2009.
- Year over year, Operating Expenses has now increased in each of the last four quarters, with growth averaging 1.2% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q4 2021 (growth of 49.2%); the low point was Q4 2024 (a decline of 15.9%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $118.03 million (Q1 2026), $118.25 million (Q4 2025) and $116.87 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Slb | 76.39 Bn | 73.50 Bn | 1.40 Bn | 324.00 Mn |
| 2 | Baker Hughes | 56.70 Bn | 19.98 Bn | 1.58 Bn | 723.00 Mn |
| 3 | TechnipFMC | 27.74 Bn | 23.88 Bn | 684.70 Mn | 2.28 Bn |
| 4 | Halliburton | 27.03 Bn | 18.79 Bn | 804.00 Mn | 4.94 Bn |
| 5 | Nov | 6.97 Bn | 1.76 Bn | 521.00 Mn | 1.04 Bn |
| 6 | Noble | 6.81 Bn | 4.74 Bn | 239.27 Mn | 689.21 Mn |
| 7 | Weatherford International | 5.95 Bn | 1.88 Bn | 333.00 Mn | 998.00 Mn |
| 8 | Transocean | 5.90 Bn | 3.61 Bn | 358.00 Mn | 812.00 Mn |
| 9 | Valaris | 5.52 Bn | 3.13 Bn | 123.70 Mn | 498.70 Mn |
| 10 | Tetra Technologies | 875.11 Mn | 533.60 Mn | 45.71 Mn | 139.94 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 139.94 Mn |
| Mar 31, 2026 | 118.03 Mn |
| Dec 31, 2025 | 118.25 Mn |
| Sep 30, 2025 | 116.87 Mn |
| Jun 30, 2025 | 125.63 Mn |
| Mar 31, 2025 | 114.23 Mn |
| Dec 31, 2024 | 103.37 Mn |
| Sep 30, 2024 | 107.34 Mn |
| Jun 30, 2024 | 128.68 Mn |
| Mar 31, 2024 | 119.87 Mn |
| Dec 31, 2023 | 122.88 Mn |
| Sep 30, 2023 | 113.54 Mn |
| Jun 30, 2023 | 126.31 Mn |
| Mar 31, 2023 | 109.89 Mn |
| Dec 31, 2022 | 116.34 Mn |
| Sep 30, 2022 | 105.54 Mn |
| Jun 30, 2022 | 112.61 Mn |
| Mar 31, 2022 | 97.62 Mn |
| Dec 31, 2021 | 93.96 Mn |
| Sep 30, 2021 | 79.73 Mn |
Tetra Technologies 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=TTI&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "TTI", "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=TTI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();