Soundhound Ai (SOUN) Operating Expenses (2021 - 2026)
Soundhound Ai (SOUN) reported Operating Expenses of $105.2 million for Q2 2026, down 12.9% from $120.73 million a year earlier but up 57.3% from the prior quarter.
Soundhound Ai (SOUN) Operating Expenses (2021 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Soundhound Ai's Operating Expenses came in at $342.49 million, down 8.0% year-over-year; for FY2025, it came in at $192.19 million, down 54.9% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 1118.3% (FY2020 to FY2025).
- By year, Operating Expenses came in at $426.05 million in FY2024 (+272.2%), $114.48 million in FY2023 (-16.3%), $136.8 million in FY2022 (+58.1%) and $86.52 million in FY2021.
- Five-year quarterly Operating Expenses spans a low of -$98.97 million in Q1 2025 and a high of $291.62 million in Q4 2024.
- Year over year, Operating Expenses gained in four of the last six quarters, with growth averaging 216.5%.
- The high point for year-over-year Operating Expenses in five years was Q4 2024 (growth of 887.2%); the low point was Q4 2025 (a decline of 95.7%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $66.87 million (Q1 2026), $12.49 million (Q4 2025) and $157.94 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 316.55 Bn | 301.63 Bn | 2.30 Bn | 2.13 Bn |
| 2 | CrowdStrike Holdings | 269.03 Bn | 249.47 Bn | 1.10 Bn | 1.13 Bn |
| 3 | Fortinet | 129.08 Bn | 115.01 Bn | 1.64 Bn | 953.90 Mn |
| 4 | Snowflake | 116.42 Bn | 103.73 Bn | 1.04 Bn | 1.30 Bn |
| 5 | Datadog | 96.40 Bn | 78.04 Bn | 881.34 Mn | 875.89 Mn |
| 6 | Okta | 34.24 Bn | 24.34 Bn | 641.00 Mn | 534.00 Mn |
| 7 | Axon Enterprise | 34.08 Bn | 28.61 Bn | 546.45 Mn | 499.67 Mn |
| 8 | Zscaler | 32.34 Bn | 18.46 Bn | - | - |
| 9 | Baidu | 29.50 Bn | -44.13 Bn | 1.47 Mn | 4.17 Bn |
| 10 | Soundhound Ai | 2.36 Bn | 1.42 Bn | 27.93 Mn | 105.20 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 105.20 Mn |
| Mar 31, 2026 | 66.87 Mn |
| Dec 31, 2025 | 12.49 Mn |
| Sep 30, 2025 | 157.94 Mn |
| Jun 30, 2025 | 120.73 Mn |
| Mar 31, 2025 | -98.97 Mn |
| Dec 31, 2024 | 291.62 Mn |
| Sep 30, 2024 | 58.86 Mn |
| Jun 30, 2024 | 35.45 Mn |
| Mar 31, 2024 | 40.12 Mn |
| Dec 31, 2023 | 29.54 Mn |
| Sep 30, 2023 | 27.80 Mn |
| Jun 30, 2023 | 25.23 Mn |
| Mar 31, 2023 | 31.91 Mn |
| Dec 31, 2022 | 38.45 Mn |
| Sep 30, 2022 | 38.26 Mn |
| Jun 30, 2022 | 35.08 Mn |
| Mar 31, 2022 | 25.01 Mn |
| Dec 31, 2021 | 24.19 Mn |
| Sep 30, 2021 | 21.20 Mn |
Soundhound Ai 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=SOUN&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "SOUN", "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=SOUN&period=max&api_key=YOUR_API_KEY");
const data = await res.json();