Datavault AI (DVLT) Research & Development (2017 - 2026)
Datavault AI's Research & Development came in at $7.24 million for Q2 2026, up 71.3% from $4.22 million a year earlier and up 26.3% from the prior quarter.
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Analysis
Datavault AI (DVLT) Research & Development (2017 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Datavault AI reported Research & Development of $22.88 million, up 109.9% year-over-year; for FY2025, it came in at $16.5 million, up 111.0% from FY2024.
- Research & Development has increased in each of the last five years, with a five-year compound annual growth rate of 29.3% (FY2020 to FY2025).
- Going back by year, Research & Development was $7.82 million in FY2024 (+4.9%), $7.46 million in FY2023 (+4.4%), $7.14 million in FY2022 (+36.4%) and $5.24 million in FY2021 (+14.5%).
- The Q2 2026 figure represents the highest quarterly Research & Development in data going back to Q2 2017.
- Year-over-year, Research & Development has increased for eight consecutive quarters, with growth averaging 85.7% over the last eight quarters.
- The fastest year-over-year change in Research & Development over five years came in Q1 2026 (growth of 142.7%), and the weakest in Q1 2024 (a decline of 9.4%).
- Business Quant data shows DVLT's Research & Development at $5.73 million (Q1 2026), $4.94 million (Q4 2025) and $4.98 million (Q3 2025) in the three quarters before Q2 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | R&D (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 260.12 Bn | 245.19 Bn | 2.30 Bn | 779.00 Mn |
| 2 | CrowdStrike Holdings | 232.49 Bn | 212.89 Bn | 1.10 Bn | 444.20 Mn |
| 3 | Fortinet | 125.70 Bn | 111.63 Bn | 1.64 Bn | 225.00 Mn |
| 4 | Snowflake | 110.19 Bn | 97.50 Bn | 1.04 Bn | 567.48 Mn |
| 5 | Datadog | 92.71 Bn | 74.35 Bn | 881.34 Mn | 477.97 Mn |
| 6 | Zscaler | 34.07 Bn | 20.19 Bn | - | - |
| 7 | Okta | 33.06 Bn | 23.16 Bn | 641.00 Mn | 163.00 Mn |
| 8 | Axon Enterprise | 30.90 Bn | 25.43 Bn | 546.45 Mn | 208.69 Mn |
| 9 | MongoDB | 28.04 Bn | 18.51 Bn | 569.77 Mn | 213.87 Mn |
| 10 | Datavault AI | 96.77 Mn | 89.46 Mn | 2.88 Mn | 7.24 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 7.24 Mn |
| Mar 31, 2026 | 5.73 Mn |
| Dec 31, 2025 | 4.94 Mn |
| Sep 30, 2025 | 4.98 Mn |
| Jun 30, 2025 | 4.22 Mn |
| Mar 31, 2025 | 2.36 Mn |
| Dec 31, 2024 | 2.09 Mn |
| Sep 30, 2024 | 2.23 Mn |
| Jun 30, 2024 | 1.79 Mn |
| Mar 31, 2024 | 1.72 Mn |
| Dec 31, 2023 | 1.79 Mn |
| Sep 30, 2023 | 1.84 Mn |
| Jun 30, 2023 | 1.93 Mn |
| Mar 31, 2023 | 1.89 Mn |
| Dec 31, 2022 | 1.79 Mn |
| Sep 30, 2022 | 1.94 Mn |
| Jun 30, 2022 | 1.88 Mn |
| Mar 31, 2022 | 1.54 Mn |
| Dec 31, 2021 | 1.44 Mn |
| Sep 30, 2021 | 1.32 Mn |
API Access
Datavault AI Research & Development 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=research-and-development&ticker=DVLT&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "research-and-development", "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=research-and-development&ticker=DVLT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();