Datavault AI (DVLT) Accumulated Expenses (2017 - 2026)
Datavault AI's Accumulated Expenses was $11.55 million in Q2 2026, up 208.6% from $3.74 million a year earlier but down 4.2% from the prior quarter.
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Analysis
Datavault AI (DVLT) Accumulated Expenses (2017 - 2026) Analysis & Trends
At the end of FY2025, Accumulated Expenses at Datavault AI came in at $11 million, up 724.7% from FY2024.
- Accumulated Expenses shows a five-year compound annual growth rate of 50.3% (FY2020 to FY2025).
- In earlier years, Accumulated Expenses was $1.33 million in FY2024 (+1.3%), $1.32 million in FY2023 (-19.3%), $1.63 million in FY2022 (+15.3%) and $1.42 million in FY2021 (-1.2%).
- Quarterly Accumulated Expenses has moved between $972,000 (Q2 2023) and $12.05 million (Q1 2026) over five years.
- Compared with a year earlier, Accumulated Expenses has increased for ten straight quarters, with growth averaging 274.4% over the last eight quarters.
- The best year-over-year quarter for Accumulated Expenses over five years was Q1 2026 (growth of 783.7%); the worst was Q2 2022 (a decline of 43.0%).
- Per Business Quant data, DVLT's Accumulated Expenses in the three quarters before Q2 2026 was $12.05 million (Q1 2026), $11 million (Q4 2025) and $5.18 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Palo Alto Networks | 323.81 Bn | 308.88 Bn | 2.30 Bn |
| 2 | CrowdStrike Holdings | 271.09 Bn | 251.53 Bn | 1.10 Bn |
| 3 | Fortinet | 131.14 Bn | 117.07 Bn | 1.64 Bn |
| 4 | Snowflake | 119.71 Bn | 107.03 Bn | 1.04 Bn |
| 5 | Datadog | 98.30 Bn | 79.94 Bn | 881.34 Mn |
| 6 | Okta | 34.95 Bn | 25.05 Bn | 641.00 Mn |
| 7 | Axon Enterprise | 34.32 Bn | 28.85 Bn | 546.45 Mn |
| 8 | Zscaler | 32.52 Bn | 18.64 Bn | - |
| 9 | Baidu | 29.56 Bn | -44.07 Bn | 1.47 Mn |
| 10 | Datavault AI | 136.71 Mn | 136.71 Mn | 2.88 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 11.55 Mn |
| Mar 31, 2026 | 12.05 Mn |
| Dec 31, 2025 | 11.00 Mn |
| Sep 30, 2025 | 5.18 Mn |
| Jun 30, 2025 | 3.74 Mn |
| Mar 31, 2025 | 1.36 Mn |
| Dec 31, 2024 | 1.33 Mn |
| Sep 30, 2024 | 1.43 Mn |
| Jun 30, 2024 | 1.38 Mn |
| Mar 31, 2024 | 1.09 Mn |
| Dec 31, 2023 | 1.32 Mn |
| Sep 30, 2023 | 1.21 Mn |
| Jun 30, 2023 | 972,000.00 |
| Mar 31, 2023 | 1.06 Mn |
| Dec 31, 2022 | 1.63 Mn |
| Sep 30, 2022 | 1.24 Mn |
| Jun 30, 2022 | 1.52 Mn |
| Mar 31, 2022 | 1.73 Mn |
| Dec 31, 2021 | 1.42 Mn |
| Sep 30, 2021 | 2.04 Mn |
API Access
Datavault AI Accumulated 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=accumulated-expenses&ticker=DVLT&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-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=accumulated-expenses&ticker=DVLT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();