Datavault AI (DVLT) Total Liabilities (2017 - 2026)
Datavault AI (DVLT) posted Total Liabilities of $30.75 million for Q2 2026, down 34.0% from $46.62 million a year earlier but up 2.2% from the prior quarter.
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Analysis
Datavault AI (DVLT) Total Liabilities (2017 - 2026) Analysis & Trends
At the end of FY2025, Datavault AI's Total Liabilities came in at $36.73 million, up 146.5% from FY2024.
- Annual Total Liabilities shows a five-year compound annual growth rate of 55.9% (FY2020 to FY2025).
- In prior years, Datavault AI's Total Liabilities was $14.9 million in FY2024 (+49.3%), $9.98 million in FY2023 (-25.8%), $13.45 million in FY2022 (+346.2%) and $3.01 million in FY2021 (-24.3%).
- Quarterly Total Liabilities has run from a low of $2.82 million in Q2 2022 to a high of $46.62 million in Q2 2025 over five years.
- On a year-over-year basis, Total Liabilities increased in five of the last seven quarters, with growth averaging 179.2%.
- The strongest year-over-year quarter for Total Liabilities in the past five years was Q3 2025, with growth of 951.9%; the weakest was Q2 2026, with a decline of 34.0%.
- According to Business Quant data, Total Liabilities for the three prior quarters was $30.09 million (Q1 2026), $36.73 million (Q4 2025) and $39.15 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 319.55 Bn | 304.63 Bn | 2.30 Bn | 20.97 Bn |
| 2 | CrowdStrike Holdings | 265.46 Bn | 245.90 Bn | 1.10 Bn | 6.89 Bn |
| 3 | Fortinet | 129.36 Bn | 115.29 Bn | 1.64 Bn | 9.31 Bn |
| 4 | Snowflake | 115.65 Bn | 102.97 Bn | 1.04 Bn | 6.54 Bn |
| 5 | Datadog | 96.45 Bn | 78.09 Bn | 881.34 Mn | 3.18 Bn |
| 6 | Axon Enterprise | 34.50 Bn | 29.03 Bn | 546.45 Mn | 3.81 Bn |
| 7 | Okta | 33.79 Bn | 23.89 Bn | 641.00 Mn | 2.17 Bn |
| 8 | Zscaler | 32.51 Bn | 18.63 Bn | - | 5.27 Bn |
| 9 | Baidu | 29.59 Bn | -44.04 Bn | 1.47 Mn | 27.69 Bn |
| 10 | Datavault AI | 136.71 Mn | 136.71 Mn | 2.88 Mn | 30.75 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 30.75 Mn |
| Mar 31, 2026 | 30.09 Mn |
| Dec 31, 2025 | 36.73 Mn |
| Sep 30, 2025 | 39.15 Mn |
| Jun 30, 2025 | 46.62 Mn |
| Mar 31, 2025 | 13.99 Mn |
| Dec 31, 2024 | 14.90 Mn |
| Sep 30, 2024 | 3.72 Mn |
| Jun 30, 2024 | 4.20 Mn |
| Mar 31, 2024 | 9.60 Mn |
| Dec 31, 2023 | 9.98 Mn |
| Sep 30, 2023 | 4.65 Mn |
| Jun 30, 2023 | 3.07 Mn |
| Mar 31, 2023 | 4.81 Mn |
| Dec 31, 2022 | 13.45 Mn |
| Sep 30, 2022 | 6.22 Mn |
| Jun 30, 2022 | 2.82 Mn |
| Mar 31, 2022 | 3.99 Mn |
| Dec 31, 2021 | 3.01 Mn |
| Sep 30, 2021 | 2.92 Mn |
API Access
Datavault AI Total Liabilities 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=total-liabilities&ticker=DVLT&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "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=total-liabilities&ticker=DVLT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();