Arlo Technologies (ARLO) Accumulated Expenses (2017 - 2026)
Arlo Technologies' Accumulated Expenses came in at $92.78 million for Q2 2026, up 3.1% from $90 million a year earlier and up 3.9% from the prior quarter.
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Analysis
Arlo Technologies (ARLO) Accumulated Expenses (2017 - 2026) Analysis & Trends
At the end of FY2025, Arlo Technologies' Accumulated Expenses was $92.37 million, up 7.7% from FY2024.
- Accumulated Expenses carries a five-year compound annual growth rate of -5.4% (FY2020 to FY2025).
- Going back by year, Accumulated Expenses was $85.73 million in FY2024 (-2.8%), $88.21 million in FY2023 (-10.8%), $98.86 million in FY2022 (+1.5%) and $97.39 million in FY2021 (-20.0%).
- The five-year range for quarterly Accumulated Expenses is $78.93 million (Q3 2024) to $98.86 million (Q4 2022).
- Year-over-year, Accumulated Expenses has increased for six consecutive quarters, with growth averaging 5.5% over the last eight quarters.
- The fastest year-over-year change in Accumulated Expenses over five years came in Q3 2025 (growth of 19.1%), and the weakest in Q4 2021 (a decline of 20.0%).
- Business Quant data shows ARLO's Accumulated Expenses at $89.33 million (Q1 2026), $92.37 million (Q4 2025) and $94 million (Q3 2025) in the three quarters before Q2 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Palo Alto Networks | 323.81 Bn | 308.88 Bn | 2.30 Bn |
| 2 | CrowdStrike Holdings | 271.09 Bn | 251.53 Bn | 1.10 Bn |
| 3 | Fortinet | 131.14 Bn | 117.07 Bn | 1.64 Bn |
| 4 | Snowflake | 119.71 Bn | 107.03 Bn | 1.04 Bn |
| 5 | Datadog | 98.30 Bn | 79.94 Bn | 881.34 Mn |
| 6 | Okta | 34.95 Bn | 25.05 Bn | 641.00 Mn |
| 7 | Axon Enterprise | 34.32 Bn | 28.85 Bn | 546.45 Mn |
| 8 | Zscaler | 32.52 Bn | 18.64 Bn | - |
| 9 | Baidu | 29.56 Bn | -44.07 Bn | 1.47 Mn |
| 10 | Arlo Technologies | 1.33 Bn | 847.27 Mn | 75.22 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 28, 2026 | 92.78 Mn |
| Mar 29, 2026 | 89.33 Mn |
| Dec 31, 2025 | 92.37 Mn |
| Sep 28, 2025 | 94.00 Mn |
| Jun 29, 2025 | 90.00 Mn |
| Mar 30, 2025 | 81.37 Mn |
| Dec 31, 2024 | 85.73 Mn |
| Sep 29, 2024 | 78.93 Mn |
| Jun 30, 2024 | 80.02 Mn |
| Mar 31, 2024 | 79.10 Mn |
| Dec 31, 2023 | 88.21 Mn |
| Oct 1, 2023 | 86.00 Mn |
| Jul 2, 2023 | 88.22 Mn |
| Apr 2, 2023 | 88.22 Mn |
| Dec 31, 2022 | 98.86 Mn |
| Oct 2, 2022 | 92.12 Mn |
| Jul 3, 2022 | 82.23 Mn |
| Apr 3, 2022 | 91.73 Mn |
| Dec 31, 2021 | 97.39 Mn |
| Oct 3, 2021 | 93.44 Mn |
API Access
Arlo Technologies Accumulated 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=accumulated-expenses&ticker=ARLO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-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=accumulated-expenses&ticker=ARLO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();