Stem (STEM) Operating Expenses (2019 - 2026)
Stem (STEM) posted Operating Expenses of $21.56 million for Q2 2026, down 17.5% from $26.14 million a year earlier and down 14.0% from the prior quarter.
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Analysis
Stem (STEM) Operating Expenses (2019 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Operating Expenses at Stem was $104.45 million, down 58.7% year-over-year; for FY2025, it was $115.64 million, down 86.0% from FY2024.
- Annual Operating Expenses shows a five-year compound annual growth rate of 20.5% (FY2020 to FY2025).
- In prior years, Stem's Operating Expenses was $828.4 million in FY2024 (+352.7%), $182.98 million in FY2023 (+11.4%), $164.21 million in FY2022 (+94.7%) and $84.32 million in FY2021 (+85.4%).
- The Q2 2026 figure stands as the lowest quarterly Operating Expenses since Q1 2021.
- On a year-over-year basis, Operating Expenses has declined in each of the last six quarters, with an average decline of 7.5% over the last eight quarters.
- The strongest year-over-year quarter for Operating Expenses in the past five years was Q1 2022, with growth of 295.2%; the weakest was Q2 2025, with a decline of 95.6%.
- According to Business Quant data, Operating Expenses for the three prior quarters was $25.07 million (Q1 2026), $31.38 million (Q4 2025) and $26.43 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 319.55 Bn | 304.63 Bn | 2.30 Bn | 2.13 Bn |
| 2 | CrowdStrike Holdings | 265.46 Bn | 245.90 Bn | 1.10 Bn | 1.13 Bn |
| 3 | Fortinet | 129.36 Bn | 115.29 Bn | 1.64 Bn | 953.90 Mn |
| 4 | Snowflake | 115.65 Bn | 102.97 Bn | 1.04 Bn | 1.30 Bn |
| 5 | Datadog | 96.45 Bn | 78.09 Bn | 881.34 Mn | 875.89 Mn |
| 6 | Axon Enterprise | 34.50 Bn | 29.03 Bn | 546.45 Mn | 499.67 Mn |
| 7 | Okta | 33.79 Bn | 23.89 Bn | 641.00 Mn | 534.00 Mn |
| 8 | Zscaler | 32.51 Bn | 18.63 Bn | - | - |
| 9 | Baidu | 29.59 Bn | -44.04 Bn | 1.47 Mn | 4.17 Bn |
| 10 | Stem | 43.72 Mn | -123.76 Mn | 13.89 Mn | 21.56 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 21.56 Mn |
| Mar 31, 2026 | 25.07 Mn |
| Dec 31, 2025 | 31.38 Mn |
| Sep 30, 2025 | 26.43 Mn |
| Jun 30, 2025 | 26.14 Mn |
| Mar 31, 2025 | 31.69 Mn |
| Dec 31, 2024 | 44.71 Mn |
| Sep 30, 2024 | 150.65 Mn |
| Jun 30, 2024 | 589.22 Mn |
| Mar 31, 2024 | 43.82 Mn |
| Dec 31, 2023 | 44.61 Mn |
| Sep 30, 2023 | 47.98 Mn |
| Jun 30, 2023 | 46.74 Mn |
| Mar 31, 2023 | 43.65 Mn |
| Dec 31, 2022 | 46.28 Mn |
| Sep 30, 2022 | 41.73 Mn |
| Jun 30, 2022 | 37.61 Mn |
| Mar 31, 2022 | 38.60 Mn |
| Dec 31, 2021 | 28.53 Mn |
| Sep 30, 2021 | 22.27 Mn |
API Access
Stem 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=STEM&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "STEM", "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=STEM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();