Everspin Technologies (MRAM) Operating Expenses (2015 - 2026)
Everspin Technologies (MRAM) recorded Operating Expenses of $14.48 million in Q2 2026, up 65.9% from $8.73 million a year earlier and up 37.1% from the prior quarter.
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Analysis
Everspin Technologies (MRAM) Operating Expenses (2015 - 2026) Analysis & Trends
On a TTM basis, Everspin Technologies' Operating Expenses came in at $42.38 million as of Jun 30, 2026, up 25.2% year-over-year; for FY2025, it came in at $34.75 million, up 4.6% from FY2024.
- Annual Operating Expenses has increased for three straight years, with a five-year compound annual growth rate of 6.3% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $33.22 million in FY2024 (+5.9%), $31.36 million in FY2023 (+13.1%), $27.72 million in FY2022 (-1.1%) and $28.04 million in FY2021 (+9.3%).
- The Q2 2026 figure is the highest quarterly Operating Expenses in data going back to Q3 2015.
- On a year-over-year basis, Operating Expenses has increased for five consecutive quarters, with growth averaging 13.9% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 65.9% in Q2 2026, against a decline of 4.3% in Q3 2022 at the low end.
- Per Business Quant, the preceding three quarters came in at $10.56 million (Q1 2026), $8.59 million (Q4 2025) and $8.75 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nvidia | 5,638.19 Bn | 5,412.36 Bn | 72.14 Bn | 8.41 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 2,452.80 Bn | 2,078.51 Bn | 27.22 Bn | 3.13 Bn |
| 3 | Broadcom | 1,695.44 Bn | 1,621.48 Bn | 20.46 Bn | 4.50 Bn |
| 4 | Micron Technology | 1,213.55 Bn | 1,152.31 Bn | 35.06 Bn | 1.72 Bn |
| 5 | Advanced Micro Devices | 1,034.54 Bn | 991.29 Bn | 6.20 Bn | 4.21 Bn |
| 6 | Asml Holding | 719.69 Bn | 675.53 Bn | 5.90 Bn | - |
| 7 | Intel | 601.78 Bn | 486.52 Bn | 6.51 Bn | 4.71 Bn |
| 8 | Lam Research | 434.81 Bn | 411.60 Bn | 3.48 Bn | 965.25 Mn |
| 9 | Applied Materials | 428.57 Bn | 394.02 Bn | 4.59 Bn | 1.51 Bn |
| 10 | Everspin Technologies | 457.03 Mn | 282.93 Mn | 10.10 Mn | 14.48 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 14.48 Mn |
| Mar 31, 2026 | 10.56 Mn |
| Dec 31, 2025 | 8.59 Mn |
| Sep 30, 2025 | 8.75 Mn |
| Jun 30, 2025 | 8.73 Mn |
| Mar 31, 2025 | 8.69 Mn |
| Dec 31, 2024 | 8.36 Mn |
| Sep 30, 2024 | 8.07 Mn |
| Jun 30, 2024 | 8.04 Mn |
| Mar 31, 2024 | 8.76 Mn |
| Dec 31, 2023 | 8.12 Mn |
| Sep 30, 2023 | 7.94 Mn |
| Jun 30, 2023 | 7.57 Mn |
| Mar 31, 2023 | 7.73 Mn |
| Dec 31, 2022 | 7.52 Mn |
| Sep 30, 2022 | 7.05 Mn |
| Jun 30, 2022 | 6.85 Mn |
| Mar 31, 2022 | 6.30 Mn |
| Dec 31, 2021 | 7.66 Mn |
| Sep 30, 2021 | 7.37 Mn |
API Access
Everspin Technologies 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=MRAM&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "MRAM", "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=MRAM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();