Shoals Technologies (SHLS) Operating Expenses (2020 - 2026)
Shoals Technologies (SHLS) recorded Operating Expenses of $30.8 million in Q2 2026, up 22.2% from $25.2 million a year earlier but down 7.5% from the prior quarter.
Shoals Technologies (SHLS) Operating Expenses (2020 - 2026) Analysis & Trends
On a TTM basis, Shoals Technologies' Operating Expenses came in at $125.19 million as of Jun 30, 2026, up 33.8% year-over-year; for FY2025, it was $110.12 million, up 21.2% from FY2024.
- Annual Operating Expenses has increased for six straight years, with a five-year compound annual growth rate of 30.3% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $90.85 million in FY2024 (+1.8%), $89.27 million in FY2023 (+37.4%), $64.98 million in FY2022 (+40.0%) and $46.41 million in FY2021 (+58.6%).
- Quarterly Operating Expenses has ranged from $12.21 million in Q3 2021 to $33.29 million in Q1 2026 over the past five years.
- On a year-over-year basis, Operating Expenses has increased for five consecutive quarters, with growth averaging 17.1% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 118.6% in Q3 2021, against a decline of 15.7% in Q3 2024 at the low end.
- Per Business Quant, the preceding three quarters came in at $33.29 million (Q1 2026), $29.51 million (Q4 2025) and $31.58 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | GE Vernova | 252.99 Bn | 214.06 Bn | 2.36 Bn | 1.71 Bn |
| 2 | Bloom Energy | 81.25 Bn | 73.07 Bn | 355.57 Mn | 173.34 Mn |
| 3 | First Solar | 18.82 Bn | 9.78 Bn | 605.00 Mn | 154.62 Mn |
| 4 | BWX Technologies | 12.55 Bn | 10.85 Bn | 202.31 Mn | 811.92 Mn |
| 5 | Generac Holdings | 12.21 Bn | 11.04 Bn | 521.81 Mn | 311.38 Mn |
| 6 | Nextpower | 11.82 Bn | 7.71 Bn | 335.85 Mn | 144.95 Mn |
| 7 | EnerSys | 6.67 Bn | 4.86 Bn | 313.36 Mn | 151.24 Mn |
| 8 | Enphase Energy | 4.36 Bn | -500.13 Mn | 175.01 Mn | 123.50 Mn |
| 9 | Mirion Technologies | 3.45 Bn | 1.34 Bn | 133.10 Mn | 115.20 Mn |
| 10 | Shoals Technologies | 1.36 Bn | 1.33 Bn | 49.53 Mn | 30.80 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 30.80 Mn |
| Mar 31, 2026 | 33.29 Mn |
| Dec 31, 2025 | 29.51 Mn |
| Sep 30, 2025 | 31.58 Mn |
| Jun 30, 2025 | 25.20 Mn |
| Mar 31, 2025 | 23.83 Mn |
| Dec 31, 2024 | 23.70 Mn |
| Sep 30, 2024 | 20.85 Mn |
| Jun 30, 2024 | 21.42 Mn |
| Mar 31, 2024 | 24.88 Mn |
| Dec 31, 2023 | 23.51 Mn |
| Sep 30, 2023 | 24.72 Mn |
| Jun 30, 2023 | 18.88 Mn |
| Mar 31, 2023 | 22.16 Mn |
| Dec 31, 2022 | 17.01 Mn |
| Sep 30, 2022 | 16.08 Mn |
| Jun 30, 2022 | 15.61 Mn |
| Mar 31, 2022 | 16.29 Mn |
| Dec 31, 2021 | 13.24 Mn |
| Sep 30, 2021 | 12.21 Mn |
Shoals 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=SHLS&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "SHLS", "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=SHLS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();