Datavault AI (DVLT) Operating Expenses (2017 - 2026)
Datavault AI's Operating Expenses came in at $29.33 million for Q2 2026, up 134.8% from $12.49 million a year earlier but down 5.6% from the prior quarter.
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Analysis
Datavault AI (DVLT) Operating Expenses (2017 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Datavault AI reported Operating Expenses of $101.28 million, up 197.7% year-over-year; for FY2025, it came in at $62.88 million, up 192.3% from FY2024.
- Operating Expenses carries a five-year compound annual growth rate of 39.9% (FY2020 to FY2025).
- Going back by year, Operating Expenses was $21.51 million in FY2024 (+19.5%), $18 million in FY2023 (-2.4%), $18.44 million in FY2022 (+37.9%) and $13.38 million in FY2021 (+14.2%).
- The five-year range for quarterly Operating Expenses is $3.42 million (Q3 2021) to $31.06 million (Q1 2026).
- Year-over-year, Operating Expenses has increased for nine consecutive quarters, with growth averaging 145.5% over the last eight quarters.
- The fastest year-over-year change in Operating Expenses over five years came in Q4 2025 (growth of 297.2%), and the weakest in Q4 2023 (a decline of 19.2%).
- Business Quant data shows DVLT's Operating Expenses at $31.06 million (Q1 2026), $26.03 million (Q4 2025) and $14.85 million (Q3 2025) in the three quarters before Q2 2026.
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 | Datavault AI | 136.71 Mn | 136.71 Mn | 2.88 Mn | 29.33 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 29.33 Mn |
| Mar 31, 2026 | 31.06 Mn |
| Dec 31, 2025 | 26.03 Mn |
| Sep 30, 2025 | 14.85 Mn |
| Jun 30, 2025 | 12.49 Mn |
| Mar 31, 2025 | 9.50 Mn |
| Dec 31, 2024 | 6.55 Mn |
| Sep 30, 2024 | 5.47 Mn |
| Jun 30, 2024 | 5.42 Mn |
| Mar 31, 2024 | 4.08 Mn |
| Dec 31, 2023 | 4.29 Mn |
| Sep 30, 2023 | 4.67 Mn |
| Jun 30, 2023 | 4.49 Mn |
| Mar 31, 2023 | 4.55 Mn |
| Dec 31, 2022 | 5.31 Mn |
| Sep 30, 2022 | 4.88 Mn |
| Jun 30, 2022 | 4.29 Mn |
| Mar 31, 2022 | 3.96 Mn |
| Dec 31, 2021 | 3.67 Mn |
| Sep 30, 2021 | 3.42 Mn |
API Access
Datavault 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=DVLT&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "DVLT", "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=DVLT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();