Arlo Technologies (ARLO) Operating Expenses (2017 - 2026)
Arlo Technologies' Operating Expenses was $72.76 million in Q2 2026, up 29.6% from $56.14 million a year earlier and up 11.7% from the prior quarter.
Powered by 
Analysis
Arlo Technologies (ARLO) Operating Expenses (2017 - 2026) Analysis & Trends
On a trailing twelve-month basis, Arlo Technologies' Operating Expenses was $254.32 million through Jun 28, 2026, up 18.2% year-over-year; for FY2025, it was $226.77 million, up 2.0% from FY2024.
- Operating Expenses shows a five-year compound annual growth rate of 7.2% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $222.4 million in FY2024 (+15.6%), $192.47 million in FY2023 (-0.2%), $192.91 million in FY2022 (+14.7%) and $168.17 million in FY2021 (+4.9%).
- The Q2 2026 figure marks the highest quarterly Operating Expenses in data going back to Q2 2017.
- Compared with a year earlier, Operating Expenses has increased for four straight quarters, with growth averaging 10.6% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was Q4 2022 (growth of 39.7%); the worst was Q2 2022 (a decline of 13.3%).
- Per Business Quant data, ARLO's Operating Expenses in the three quarters before Q2 2026 was $65.11 million (Q1 2026), $60.82 million (Q4 2025) and $55.63 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 316.55 Bn | 301.63 Bn | 2.30 Bn | 2.13 Bn |
| 2 | CrowdStrike Holdings | 269.03 Bn | 249.47 Bn | 1.10 Bn | 1.13 Bn |
| 3 | Fortinet | 129.08 Bn | 115.01 Bn | 1.64 Bn | 953.90 Mn |
| 4 | Snowflake | 116.42 Bn | 103.73 Bn | 1.04 Bn | 1.30 Bn |
| 5 | Datadog | 96.40 Bn | 78.04 Bn | 881.34 Mn | 875.89 Mn |
| 6 | Okta | 34.24 Bn | 24.34 Bn | 641.00 Mn | 534.00 Mn |
| 7 | Axon Enterprise | 34.08 Bn | 28.61 Bn | 546.45 Mn | 499.67 Mn |
| 8 | Zscaler | 32.34 Bn | 18.46 Bn | - | - |
| 9 | Baidu | 29.50 Bn | -44.13 Bn | 1.47 Mn | 4.17 Bn |
| 10 | Arlo Technologies | 1.34 Bn | 850.50 Mn | 75.22 Mn | 72.76 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 28, 2026 | 72.76 Mn |
| Mar 29, 2026 | 65.11 Mn |
| Dec 31, 2025 | 60.82 Mn |
| Sep 28, 2025 | 55.63 Mn |
| Jun 29, 2025 | 56.14 Mn |
| Mar 30, 2025 | 54.18 Mn |
| Dec 31, 2024 | 50.88 Mn |
| Sep 29, 2024 | 53.87 Mn |
| Jun 30, 2024 | 59.66 Mn |
| Mar 31, 2024 | 57.99 Mn |
| Dec 31, 2023 | 47.81 Mn |
| Oct 1, 2023 | 45.42 Mn |
| Jul 2, 2023 | 49.89 Mn |
| Apr 2, 2023 | 49.36 Mn |
| Dec 31, 2022 | 54.40 Mn |
| Oct 2, 2022 | 51.19 Mn |
| Jul 3, 2022 | 45.08 Mn |
| Apr 3, 2022 | 42.25 Mn |
| Dec 31, 2021 | 38.95 Mn |
| Oct 3, 2021 | 39.96 Mn |
API Access
Arlo Technologies 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=ARLO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "ARLO", "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=ARLO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();