Nerdy (NRDY) Operating Expenses (2020 - 2026)
Nerdy (NRDY) posted Operating Expenses of $22.89 million for Q2 2026, down 13.9% from $26.57 million a year earlier and down 4.3% from the prior quarter.
Nerdy (NRDY) Operating Expenses (2020 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Operating Expenses at Nerdy was $97.34 million, down 16.6% year-over-year; for FY2025, it was $105.52 million, down 16.8% from FY2024.
- Annual Operating Expenses shows a five-year compound annual growth rate of 19.5% (FY2020 to FY2025).
- In prior years, Nerdy's Operating Expenses was $126.88 million in FY2024 (+1.0%), $125.57 million in FY2023 (-3.1%), $129.56 million in FY2022 (+6.2%) and $121.97 million in FY2021 (+182.1%).
- The Q2 2026 figure stands as the lowest quarterly Operating Expenses since Q2 2021.
- On a year-over-year basis, Operating Expenses has declined in each of the last eight quarters, with an average decline of 13.7% over the last eight quarters.
- The strongest year-over-year quarter for Operating Expenses in the past five years was Q3 2021, with growth of 510.1%; the weakest was Q3 2022, with a decline of 44.2%.
- According to Business Quant data, Operating Expenses for the three prior quarters was $23.92 million (Q1 2026), $24.72 million (Q4 2025) and $25.82 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Bryn | 945.00 Bn | 945.00 Bn | - | 178,646.00 |
| 2 | Veeva Systems | 45.09 Bn | 17.34 Bn | 695.95 Mn | 420.93 Mn |
| 3 | Samsara | 22.22 Bn | 18.99 Bn | 392.58 Mn | 387.71 Mn |
| 4 | Toast | 17.58 Bn | 10.25 Bn | 516.00 Mn | 364.00 Mn |
| 5 | Ptc | 15.23 Bn | 14.05 Bn | 490.47 Mn | 323.96 Mn |
| 6 | Trimble | 13.37 Bn | 12.44 Bn | 674.90 Mn | 542.90 Mn |
| 7 | Duolingo | 13.34 Bn | 8.51 Bn | 216.74 Mn | 182.80 Mn |
| 8 | Manhattan Associates | 11.67 Bn | 10.66 Bn | 168.33 Mn | 231.56 Mn |
| 9 | Costar | 10.92 Bn | 4.88 Bn | 728.00 Mn | 652.00 Mn |
| 10 | Nerdy | 108.96 Mn | -46.10 Mn | 27.98 Mn | 22.89 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 22.89 Mn |
| Mar 31, 2026 | 23.92 Mn |
| Dec 31, 2025 | 24.72 Mn |
| Sep 30, 2025 | 25.82 Mn |
| Jun 30, 2025 | 26.57 Mn |
| Mar 31, 2025 | 28.41 Mn |
| Dec 31, 2024 | 29.86 Mn |
| Sep 30, 2024 | 31.86 Mn |
| Jun 30, 2024 | 33.18 Mn |
| Mar 31, 2024 | 31.98 Mn |
| Dec 31, 2023 | 30.65 Mn |
| Sep 30, 2023 | 35.51 Mn |
| Jun 30, 2023 | 29.71 Mn |
| Mar 31, 2023 | 29.70 Mn |
| Dec 31, 2022 | 32.89 Mn |
| Sep 30, 2022 | 33.41 Mn |
| Jun 30, 2022 | 32.75 Mn |
| Mar 31, 2022 | 30.51 Mn |
| Dec 31, 2021 | 34.29 Mn |
| Sep 30, 2021 | 59.90 Mn |
Nerdy 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=NRDY&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "NRDY", "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=NRDY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();