N-able (NABL) Accumulated Expenses (2020 - 2026)
N-able (NABL) posted Accumulated Expenses of $42.74 million for Q2 2026, down 4.8% from $44.91 million a year earlier but up 7.3% from the prior quarter.
Powered by 
Analysis
N-able (NABL) Accumulated Expenses (2020 - 2026) Analysis & Trends
At the end of FY2025, N-able's Accumulated Expenses came in at $55.28 million, up 9.3% from FY2024.
- Annual Accumulated Expenses has increased for five consecutive years, with a five-year compound annual growth rate of 20.3% (FY2020 to FY2025).
- In prior years, N-able's Accumulated Expenses was $50.58 million in FY2024 (+2.5%), $49.37 million in FY2023 (+38.6%), $35.63 million in FY2022 (+15.1%) and $30.94 million in FY2021 (+40.8%).
- Quarterly Accumulated Expenses has run from a low of $24.3 million in Q1 2022 to a high of $55.28 million in Q4 2025 over five years.
- On a year-over-year basis, Accumulated Expenses increased in five of the last eight quarters, with growth averaging 4.1%.
- The strongest year-over-year quarter for Accumulated Expenses in the past five years was Q2 2023, with growth of 48.1%; the weakest was Q1 2026, with a decline of 13.4%.
- According to Business Quant data, Accumulated Expenses for the three prior quarters was $39.83 million (Q1 2026), $55.28 million (Q4 2025) and $50 million (Q3 2025).
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 | N-able | 776.40 Mn | 329.51 Mn | 106.20 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 42.74 Mn |
| Mar 31, 2026 | 39.83 Mn |
| Dec 31, 2025 | 55.28 Mn |
| Sep 30, 2025 | 50.00 Mn |
| Jun 30, 2025 | 44.91 Mn |
| Mar 31, 2025 | 45.98 Mn |
| Dec 31, 2024 | 50.58 Mn |
| Sep 30, 2024 | 46.47 Mn |
| Jun 30, 2024 | 44.95 Mn |
| Mar 31, 2024 | 36.42 Mn |
| Dec 31, 2023 | 49.37 Mn |
| Sep 30, 2023 | 43.91 Mn |
| Jun 30, 2023 | 39.51 Mn |
| Mar 31, 2023 | 32.30 Mn |
| Dec 31, 2022 | 35.63 Mn |
| Sep 30, 2022 | 33.80 Mn |
| Jun 30, 2022 | 26.68 Mn |
| Mar 31, 2022 | 24.30 Mn |
| Dec 31, 2021 | 30.94 Mn |
| Sep 30, 2021 | 30.90 Mn |
API Access
N-able 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=NABL&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "NABL", "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=NABL&period=max&api_key=YOUR_API_KEY");
const data = await res.json();