Palladyne AI (PDYN) Operating Expenses (2020 - 2026)
Palladyne AI (PDYN) recorded Operating Expenses of $19.2 million in Q2 2026, up 110.7% from $9.11 million a year earlier and up 24.2% from the prior quarter.
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Analysis
Palladyne AI (PDYN) Operating Expenses (2020 - 2026) Analysis & Trends
On a TTM basis, Palladyne AI's Operating Expenses came in at $54.55 million as of Jun 30, 2026, up 64.5% year-over-year; for FY2025, it was $37.65 million, up 8.5% from FY2024.
- Annual Operating Expenses has a five-year compound annual growth rate of 4.8% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $34.71 million in FY2024 (-72.7%), $127.1 million in FY2023 (-33.7%), $191.61 million in FY2022 (+122.6%) and $86.07 million in FY2021 (+188.7%).
- The Q2 2026 figure is the highest quarterly Operating Expenses since Q4 2023.
- On a year-over-year basis, Operating Expenses has increased for five consecutive quarters, with growth averaging 10.2% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 395.5% in Q3 2021, against a decline of 80.8% in Q4 2024 at the low end.
- Per Business Quant, the preceding three quarters came in at $15.46 million (Q1 2026), $10.98 million (Q4 2025) and $8.93 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Aurora Innovation | 10.57 Bn | 5.60 Bn | -5.00 Mn | 261.00 Mn |
| 2 | Mobileye Global | 6.38 Bn | 273.40 Mn | 235.00 Mn | 265.00 Mn |
| 3 | Pony AI | 3.01 Bn | 3.08 Bn | 6.35 Mn | -72.09 Mn |
| 4 | WeRide | 1.89 Bn | -1.13 Bn | 12.76 Mn | -73.92 Mn |
| 5 | Securetech Innovations | 781.01 Mn | 780.42 Mn | - | - |
| 6 | Kodiak AI | 440.77 Mn | -67.35 Mn | - | 47.18 Mn |
| 7 | Serve Robotics | 383.18 Mn | 383.18 Mn | -8.78 Mn | 57.29 Mn |
| 8 | Richtech Robotics | 352.29 Mn | 352.29 Mn | 802,000.00 | 14.07 Mn |
| 9 | ECARX Holdings | 350.87 Mn | -10.95 Mn | 44.50 Mn | -50.70 Mn |
| 10 | Palladyne AI | 259.48 Mn | 67.85 Mn | 1.68 Mn | 19.20 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 19.20 Mn |
| Mar 31, 2026 | 15.46 Mn |
| Dec 31, 2025 | 10.98 Mn |
| Sep 30, 2025 | 8.93 Mn |
| Jun 30, 2025 | 9.11 Mn |
| Mar 31, 2025 | 8.64 Mn |
| Dec 31, 2024 | 7.25 Mn |
| Sep 30, 2024 | 8.17 Mn |
| Jun 30, 2024 | 8.50 Mn |
| Mar 31, 2024 | 10.79 Mn |
| Dec 31, 2023 | 37.80 Mn |
| Sep 30, 2023 | 32.58 Mn |
| Jun 30, 2023 | 31.24 Mn |
| Mar 31, 2023 | 25.48 Mn |
| Dec 31, 2022 | 101.30 Mn |
| Sep 30, 2022 | 31.92 Mn |
| Jun 30, 2022 | 32.02 Mn |
| Mar 31, 2022 | 26.37 Mn |
| Dec 31, 2021 | 28.65 Mn |
| Sep 30, 2021 | 41.62 Mn |
API Access
Palladyne 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=PDYN&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "PDYN", "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=PDYN&period=max&api_key=YOUR_API_KEY");
const data = await res.json();