System1 (SST) Operating Expenses (2020 - 2026)
System1 (SST) recorded Operating Expenses of $38.49 million in Q2 2026, down 59.1% from $94.02 million a year earlier and down 56.4% from the prior quarter.
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Analysis
System1 (SST) Operating Expenses (2020 - 2026) Analysis & Trends
On a TTM basis, System1's Operating Expenses came in at $273.27 million as of Jun 30, 2026, down 28.0% year-over-year; for FY2025, it was $328.17 million, down 24.1% from FY2024.
- Annual Operating Expenses has declined for three straight years, though with a five-year compound annual growth rate of 232.6% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $432.53 million in FY2024 (-11.4%), $487.96 million in FY2023 (-54.4%), $1.07 billion in FY2022 (+67.8%) and $637.56 million in FY2021.
- The Q2 2026 figure is the lowest quarterly Operating Expenses since Q4 2020.
- On a year-over-year basis, Operating Expenses rose in two of the last eight quarters, with an average decline of 22.0%.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 229.5% in Q3 2022, against a decline of 78.8% in Q3 2023 at the low end.
- Per Business Quant, the preceding three quarters came in at $88.27 million (Q1 2026), $69.38 million (Q4 2025) and $77.13 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Alphabet | 4,164.76 Bn | 3,922.28 Bn | 73.85 Bn | 79.03 Bn |
| 2 | Meta Platforms | 1,823.40 Bn | 1,525.92 Bn | 49.47 Bn | 42.03 Bn |
| 3 | Netflix | 288.27 Bn | 248.47 Bn | 6.52 Bn | 1.51 Bn |
| 4 | Alibaba Group Holding | 252.72 Bn | 70.58 Bn | 15.11 Bn | -5.17 Bn |
| 5 | Shopify | 186.52 Bn | 163.71 Bn | 1.71 Bn | 1.22 Bn |
| 6 | Uber Technologies | 139.11 Bn | 111.09 Bn | 6.38 Bn | 12.30 Bn |
| 7 | Booking Holdings | 123.13 Bn | 56.18 Bn | - | 4.85 Bn |
| 8 | PDD Holdings | 111.72 Bn | -140.21 Bn | 9.45 Bn | -5.39 Bn |
| 9 | AppLovin | 103.35 Bn | 93.38 Bn | 1.70 Bn | 429.41 Mn |
| 10 | System1 | 27.60 Mn | -232.22 Mn | 24.27 Mn | 38.49 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 38.49 Mn |
| Mar 31, 2026 | 88.27 Mn |
| Dec 31, 2025 | 69.38 Mn |
| Sep 30, 2025 | 77.13 Mn |
| Jun 30, 2025 | 94.02 Mn |
| Mar 31, 2025 | 87.64 Mn |
| Dec 31, 2024 | 87.50 Mn |
| Sep 30, 2024 | 110.65 Mn |
| Jun 30, 2024 | 123.67 Mn |
| Mar 31, 2024 | 110.71 Mn |
| Dec 31, 2023 | 115.20 Mn |
| Sep 30, 2023 | 108.67 Mn |
| Jun 30, 2023 | 118.74 Mn |
| Mar 31, 2023 | 145.35 Mn |
| Dec 31, 2022 | 194.44 Mn |
| Sep 30, 2022 | 512.09 Mn |
| Jun 30, 2022 | 250.91 Mn |
| Mar 31, 2022 | 205.60 Mn |
| Dec 31, 2021 | 192.27 Mn |
| Sep 30, 2021 | 155.42 Mn |
API Access
System1 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=SST&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "SST", "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=SST&period=max&api_key=YOUR_API_KEY");
const data = await res.json();