JFrog (FROG) Operating Expenses (2019 - 2026)
JFrog's Operating Expenses came in at $140.87 million for Q2 2026, up 14.5% from $122.99 million a year earlier and up 5.7% from the prior quarter.
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Analysis
JFrog (FROG) Operating Expenses (2019 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, JFrog reported Operating Expenses of $536.23 million, up 15.9% year-over-year; for FY2025, it came in at $500.24 million, up 18.7% from FY2024.
- Operating Expenses has increased in each of the last seven years, with a five-year compound annual growth rate of 29.6% (FY2020 to FY2025).
- Going back by year, Operating Expenses was $421.29 million in FY2024 (+20.9%), $348.39 million in FY2023 (+13.3%), $307.59 million in FY2022 (+31.9%) and $233.23 million in FY2021 (+70.8%).
- The Q2 2026 figure represents the highest quarterly Operating Expenses in data going back to Q3 2019.
- Year-over-year, Operating Expenses has increased for 24 consecutive quarters, with growth averaging 19.6% over the last eight quarters.
- Across the past five years, year-over-year growth in Operating Expenses ran from 9.3% in Q3 2023 to 70.6% in Q3 2021.
- Business Quant data shows FROG's Operating Expenses at $133.31 million (Q1 2026), $134.46 million (Q4 2025) and $127.6 million (Q3 2025) in the three quarters before Q2 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 319.55 Bn | 304.63 Bn | 2.30 Bn | 2.13 Bn |
| 2 | CrowdStrike Holdings | 265.46 Bn | 245.90 Bn | 1.10 Bn | 1.13 Bn |
| 3 | Fortinet | 129.36 Bn | 115.29 Bn | 1.64 Bn | 953.90 Mn |
| 4 | Snowflake | 115.65 Bn | 102.97 Bn | 1.04 Bn | 1.30 Bn |
| 5 | Datadog | 96.45 Bn | 78.09 Bn | 881.34 Mn | 875.89 Mn |
| 6 | Axon Enterprise | 34.50 Bn | 29.03 Bn | 546.45 Mn | 499.67 Mn |
| 7 | Okta | 33.79 Bn | 23.89 Bn | 641.00 Mn | 534.00 Mn |
| 8 | Zscaler | 32.51 Bn | 18.63 Bn | - | - |
| 9 | Baidu | 29.59 Bn | -44.04 Bn | 1.47 Mn | 4.17 Bn |
| 10 | JFrog | 10.93 Bn | 8.01 Bn | 127.62 Mn | 140.87 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 140.87 Mn |
| Mar 31, 2026 | 133.31 Mn |
| Dec 31, 2025 | 134.46 Mn |
| Sep 30, 2025 | 127.60 Mn |
| Jun 30, 2025 | 122.99 Mn |
| Mar 31, 2025 | 115.20 Mn |
| Dec 31, 2024 | 112.98 Mn |
| Sep 30, 2024 | 111.69 Mn |
| Jun 30, 2024 | 100.28 Mn |
| Mar 31, 2024 | 96.34 Mn |
| Dec 31, 2023 | 92.22 Mn |
| Sep 30, 2023 | 86.94 Mn |
| Jun 30, 2023 | 84.63 Mn |
| Mar 31, 2023 | 84.61 Mn |
| Dec 31, 2022 | 84.12 Mn |
| Sep 30, 2022 | 79.53 Mn |
| Jun 30, 2022 | 74.97 Mn |
| Mar 31, 2022 | 68.97 Mn |
| Dec 31, 2021 | 68.98 Mn |
| Sep 30, 2021 | 63.16 Mn |
API Access
JFrog 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=FROG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-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=operating-expenses&ticker=FROG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();