Rekor Systems (REKR) Operating Expenses (2016 - 2026)
Rekor Systems' Operating Expenses came in at $6.89 million for Q2 2026, down 50.3% from $13.85 million a year earlier and down 51.5% from the prior quarter.
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Analysis
Rekor Systems (REKR) Operating Expenses (2016 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Rekor Systems reported Operating Expenses of $48.62 million, down 31.8% year-over-year; for FY2025, it came in at $55.96 million, down 27.3% from FY2024.
- Operating Expenses carries a five-year compound annual growth rate of 26.1% (FY2020 to FY2025).
- Going back by year, Operating Expenses was $77.01 million in FY2024 (+27.2%), $60.55 million in FY2023 (-36.1%), $94.81 million in FY2022 (+144.0%) and $38.86 million in FY2021 (+121.2%).
- The Q2 2026 figure represents the lowest quarterly Operating Expenses since Q4 2020.
- Year-over-year, Operating Expenses has declined for six consecutive quarters, with an average decline of 8.5% over the last eight quarters.
- The fastest year-over-year change in Operating Expenses over five years came in Q3 2022 (growth of 367.5%), and the weakest in Q3 2023 (a decline of 71.3%).
- Business Quant data shows REKR's Operating Expenses at $14.2 million (Q1 2026), $14.56 million (Q4 2025) and $12.97 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 | 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 | Rekor Systems | 52.30 Mn | 10.64 Mn | 7.11 Mn | 6.89 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 6.89 Mn |
| Mar 31, 2026 | 14.20 Mn |
| Dec 31, 2025 | 14.56 Mn |
| Sep 30, 2025 | 12.97 Mn |
| Jun 30, 2025 | 13.85 Mn |
| Mar 31, 2025 | 14.58 Mn |
| Dec 31, 2024 | 25.38 Mn |
| Sep 30, 2024 | 17.50 Mn |
| Jun 30, 2024 | 16.73 Mn |
| Mar 31, 2024 | 17.41 Mn |
| Dec 31, 2023 | 15.23 Mn |
| Sep 30, 2023 | 14.60 Mn |
| Jun 30, 2023 | 14.71 Mn |
| Mar 31, 2023 | 16.00 Mn |
| Dec 31, 2022 | 13.11 Mn |
| Sep 30, 2022 | 50.81 Mn |
| Jun 30, 2022 | 17.13 Mn |
| Mar 31, 2022 | 14.12 Mn |
| Dec 31, 2021 | 12.81 Mn |
| Sep 30, 2021 | 10.87 Mn |
API Access
Rekor Systems 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=REKR&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "REKR", "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=REKR&period=max&api_key=YOUR_API_KEY");
const data = await res.json();