Wm Technology (MAPS) Operating Expenses (2019 - 2026)
Wm Technology (MAPS) reported Operating Expenses of $40.09 million for Q2 2026, down 6.7% from $42.98 million a year earlier and down 7.5% from the prior quarter.
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Analysis
Wm Technology (MAPS) Operating Expenses (2019 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Wm Technology's Operating Expenses came in at $172.55 million, up 1.9% year-over-year; for FY2025, it was $173.94 million, up 2.5% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 7.6% (FY2020 to FY2025).
- By year, Operating Expenses came in at $169.75 million in FY2024 (-17.8%), $206.45 million in FY2023 (-27.6%), $285.15 million in FY2022 (+41.6%) and $201.32 million in FY2021 (+66.9%).
- The Q2 2026 figure ranks as the lowest quarterly Operating Expenses since Q1 2021.
- Year over year, Operating Expenses gained in three of the last eight quarters, with an average decline of 5.1%.
- The high point for year-over-year Operating Expenses in five years was Q1 2022 (growth of 115.9%); the low point was Q2 2023 (a decline of 37.4%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $43.36 million (Q1 2026), $48.88 million (Q4 2025) and $40.22 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 | Wm Technology | 57.49 Mn | -98.37 Mn | 39.92 Mn | 40.09 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 40.09 Mn |
| Mar 31, 2026 | 43.36 Mn |
| Dec 31, 2025 | 48.88 Mn |
| Sep 30, 2025 | 40.22 Mn |
| Jun 30, 2025 | 42.98 Mn |
| Mar 31, 2025 | 41.97 Mn |
| Dec 31, 2024 | 43.10 Mn |
| Sep 30, 2024 | 41.35 Mn |
| Jun 30, 2024 | 44.67 Mn |
| Mar 31, 2024 | 40.63 Mn |
| Dec 31, 2023 | 59.01 Mn |
| Sep 30, 2023 | 52.24 Mn |
| Jun 30, 2023 | 44.64 Mn |
| Mar 31, 2023 | 50.56 Mn |
| Dec 31, 2022 | 71.98 Mn |
| Sep 30, 2022 | 70.15 Mn |
| Jun 30, 2022 | 71.31 Mn |
| Mar 31, 2022 | 71.71 Mn |
| Dec 31, 2021 | 59.08 Mn |
| Sep 30, 2021 | 46.82 Mn |
API Access
Wm Technology 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=MAPS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "MAPS", "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=MAPS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();