Gitlab (GTLB) Operating Expenses (2020 - 2026)
Gitlab's Operating Expenses was $297.55 million in fiscal Q2 2027 (quarter ended Jul 31, 2026), up 31.8% from $225.81 million a year earlier and up 22.7% from the prior quarter.
Gitlab (GTLB) Operating Expenses (2020 - 2026) Analysis & Trends
On a trailing twelve-month basis, Gitlab's Operating Expenses was $995.04 million through Jul 31, 2026, up 15.6% year-over-year; for FY2026 (ended Jan 31, 2026), it came in at $904.96 million, up 10.8% from FY2025.
- Operating Expenses has now increased for six consecutive fiscal years, with a five-year compound annual growth rate of 21.1% (FY2021 to FY2026).
- In earlier fiscal years, Operating Expenses was $816.82 million in FY2025 (+15.4%), $707.64 million in FY2024 (+21.2%), $584.07 million in FY2023 (+66.1%) and $351.63 million in FY2022 (+1.2%).
- The fiscal Q2 2027 figure marks the highest quarterly Operating Expenses in data going back to fiscal Q3 2021.
- Compared with a year earlier, Operating Expenses has increased for 18 straight quarters, with growth averaging 14.1% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was fiscal Q2 2023 (growth of 89.8%); the worst was fiscal Q4 2022 (a decline of 35.2%).
- Per Business Quant data, GTLB's Operating Expenses in the three fiscal quarters before Q2 2027 was $242.42 million (Q1 2027), $230.58 million (Q4 2026) and $224.49 million (Q3 2026).
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 | Gitlab | 7.76 Bn | 2.73 Bn | 240.62 Mn | 297.55 Mn |
Historic Data
| Date | Value |
|---|---|
| Jul 31, 2026 | 297.55 Mn |
| Apr 30, 2026 | 242.42 Mn |
| Jan 31, 2026 | 230.58 Mn |
| Oct 31, 2025 | 224.49 Mn |
| Jul 31, 2025 | 225.81 Mn |
| Apr 30, 2025 | 224.08 Mn |
| Jan 31, 2025 | 207.90 Mn |
| Oct 31, 2024 | 202.65 Mn |
| Jul 31, 2024 | 202.22 Mn |
| Apr 30, 2024 | 204.05 Mn |
| Jan 31, 2024 | 182.67 Mn |
| Oct 31, 2023 | 174.85 Mn |
| Jul 31, 2023 | 178.94 Mn |
| Apr 30, 2023 | 171.17 Mn |
| Jan 31, 2023 | 154.94 Mn |
| Oct 31, 2022 | 155.38 Mn |
| Jul 31, 2022 | 153.31 Mn |
| Apr 30, 2022 | 120.43 Mn |
| Jan 31, 2022 | 109.18 Mn |
| Oct 31, 2021 | 92.15 Mn |
Gitlab 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=GTLB&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "GTLB", "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=GTLB&period=max&api_key=YOUR_API_KEY");
const data = await res.json();