Paid (PAYD) Operating Expenses (2010 - 2026)
Paid's Operating Expenses was $1.25 million in Q2 2026, down 22.4% from $1.61 million a year earlier but up 5.9% from the prior quarter.
Powered by 
Analysis
Paid (PAYD) Operating Expenses (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Paid's Operating Expenses was $4.94 million through Jun 30, 2026, down 3.4% year-over-year; for FY2025, it was $5.3 million, up 16.1% from FY2024.
- Operating Expenses has now increased for three consecutive years, with a five-year compound annual growth rate of 0.2% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $4.56 million in FY2024 (+4.4%), $4.37 million in FY2023 (+20.5%), $3.63 million in FY2022 (-8.0%) and $3.94 million in FY2021 (-24.8%).
- Quarterly Operating Expenses has moved between $858,046 (Q1 2022) and $1.61 million (Q2 2025) over five years.
- Compared with a year earlier, Operating Expenses was higher in six of the last eight quarters, with growth averaging 8.2%.
- The best year-over-year quarter for Operating Expenses over five years was Q1 2023 (growth of 48.3%); the worst was Q4 2021 (a decline of 64.9%).
- Per Business Quant data, PAYD's Operating Expenses in the three quarters before Q2 2026 was $1.18 million (Q1 2026), $1.26 million (Q4 2025) and $1.25 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Alphabet | 4,164.76 Bn | 3,922.28 Bn | 73.85 Bn | 79.03 Bn |
| 2 | Meta Platforms | 1,823.40 Bn | 1,525.92 Bn | 49.47 Bn | 42.03 Bn |
| 3 | Netflix | 288.27 Bn | 248.47 Bn | 6.52 Bn | 1.51 Bn |
| 4 | Alibaba Group Holding | 252.72 Bn | 70.58 Bn | 15.11 Bn | -5.17 Bn |
| 5 | Shopify | 186.52 Bn | 163.71 Bn | 1.71 Bn | 1.22 Bn |
| 6 | Uber Technologies | 139.11 Bn | 111.09 Bn | 6.38 Bn | 12.30 Bn |
| 7 | Booking Holdings | 123.13 Bn | 56.18 Bn | - | 4.85 Bn |
| 8 | PDD Holdings | 111.72 Bn | -140.21 Bn | 9.45 Bn | -5.39 Bn |
| 9 | AppLovin | 103.35 Bn | 93.38 Bn | 1.70 Bn | 429.41 Mn |
| 10 | Paid | 20.86 Mn | 16.57 Mn | 1.22 Mn | 1.25 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 1.25 Mn |
| Mar 31, 2026 | 1.18 Mn |
| Dec 31, 2025 | 1.26 Mn |
| Sep 30, 2025 | 1.25 Mn |
| Jun 30, 2025 | 1.61 Mn |
| Mar 31, 2025 | 1.18 Mn |
| Dec 31, 2024 | 1.09 Mn |
| Sep 30, 2024 | 1.23 Mn |
| Jun 30, 2024 | 1.18 Mn |
| Mar 31, 2024 | 1.06 Mn |
| Dec 31, 2023 | 1.08 Mn |
| Sep 30, 2023 | 1.01 Mn |
| Jun 30, 2023 | 1.01 Mn |
| Mar 31, 2023 | 1.27 Mn |
| Dec 31, 2022 | 963,310.00 |
| Sep 30, 2022 | 945,944.00 |
| Jun 30, 2022 | 862,688.00 |
| Mar 31, 2022 | 858,046.00 |
| Dec 31, 2021 | 1.00 Mn |
| Sep 30, 2021 | 921,497.00 |
API Access
Paid 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=PAYD&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "PAYD", "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=PAYD&period=max&api_key=YOUR_API_KEY");
const data = await res.json();