JFrog (FROG) Change in Accured Expenses (2019 - 2026)
JFrog (FROG) posted Change in Accured Expenses of $18.03 million for Q2 2026, up 69.1% from $10.66 million a year earlier.
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Analysis
JFrog (FROG) Change in Accured Expenses (2019 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Change in Accured Expenses at JFrog was $27.5 million, up 34.3% year-over-year; for FY2025, it came in at $20.78 million, up 50.1% from FY2024.
- Annual Change in Accured Expenses has increased for three consecutive years, with a five-year compound annual growth rate of 30.4% (FY2020 to FY2025).
- In prior years, JFrog's Change in Accured Expenses was $13.84 million in FY2024 (+29.6%), $10.68 million in FY2023 (+376.4%), $2.24 million in FY2022 (-82.9%) and $13.09 million in FY2021 (+137.6%).
- The Q2 2026 figure stands as the highest quarterly Change in Accured Expenses in data going back to Q4 2019.
- The strongest year-over-year quarter for Change in Accured Expenses in the past five years was Q4 2021, with growth of 413.6%; the weakest was Q1 2023, with a decline of 73.1%.
- According to Business Quant data, Change in Accured Expenses for the three prior quarters was -$1.77 million (Q1 2026), $16.47 million (Q4 2025) and -$5.23 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 322.94 Bn | 308.02 Bn | 2.30 Bn | 369.00 Mn |
| 2 | CrowdStrike Holdings | 272.46 Bn | 252.90 Bn | 1.10 Bn | 72.38 Mn |
| 3 | Fortinet | 131.11 Bn | 117.04 Bn | 1.64 Bn | 75.10 Mn |
| 4 | Snowflake | 120.49 Bn | 107.81 Bn | 1.04 Bn | 108.85 Mn |
| 5 | Datadog | 99.24 Bn | 80.88 Bn | 881.34 Mn | 6.98 Mn |
| 6 | Okta | 35.54 Bn | 25.63 Bn | 641.00 Mn | -2.00 Mn |
| 7 | Axon Enterprise | 34.30 Bn | 28.82 Bn | 546.45 Mn | 256.58 Mn |
| 8 | Zscaler | 32.41 Bn | 18.53 Bn | - | - |
| 9 | Baidu | 29.12 Bn | -44.51 Bn | 1.47 Mn | - |
| 10 | JFrog | 12.01 Bn | 9.09 Bn | 127.62 Mn | 18.03 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 18.03 Mn |
| Mar 31, 2026 | -1.77 Mn |
| Dec 31, 2025 | 16.47 Mn |
| Sep 30, 2025 | -5.23 Mn |
| Jun 30, 2025 | 10.66 Mn |
| Mar 31, 2025 | -1.13 Mn |
| Dec 31, 2024 | 5.88 Mn |
| Sep 30, 2024 | 5.07 Mn |
| Jun 30, 2024 | 6.11 Mn |
| Mar 31, 2024 | -3.21 Mn |
| Dec 31, 2023 | 5.75 Mn |
| Sep 30, 2023 | 1.91 Mn |
| Jun 30, 2023 | 2.62 Mn |
| Mar 31, 2023 | 410,000.00 |
| Dec 31, 2022 | -947,000.00 |
| Sep 30, 2022 | -2.27 Mn |
| Jun 30, 2022 | 3.93 Mn |
| Mar 31, 2022 | 1.52 Mn |
| Dec 31, 2021 | 7.43 Mn |
| Sep 30, 2021 | 957,000.00 |
API Access
JFrog Change in Accured 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=change-in-accured-expenses&ticker=FROG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-expenses", "ticker": "FROG", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=change-in-accured-expenses&ticker=FROG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();