Innodata (INOD) Operating Expenses (2010 - 2026)
Innodata (INOD) recorded Operating Expenses of $74.59 million in Q2 2026, up 52.5% from $48.91 million a year earlier and up 2.5% from the prior quarter.
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Analysis
Innodata (INOD) Operating Expenses (2010 - 2026) Analysis & Trends
On a TTM basis, Innodata's Operating Expenses came in at $258.73 million as of Jun 30, 2026, up 38.0% year-over-year; for FY2025, it was $210.24 million, up 44.0% from FY2024.
- Annual Operating Expenses has a five-year compound annual growth rate of 29.8% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $145.98 million in FY2024 (+68.5%), $86.64 million in FY2023 (-3.2%), $89.48 million in FY2022 (+25.5%) and $71.3 million in FY2021 (+25.0%).
- The Q2 2026 figure is the highest quarterly Operating Expenses in data going back to Q2 2010.
- On a year-over-year basis, Operating Expenses has increased for 11 consecutive quarters, with growth averaging 61.0% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 99.1% in Q1 2025, against a decline of 12.8% in Q2 2023 at the low end.
- Per Business Quant, the preceding three quarters came in at $72.75 million (Q1 2026), $61.02 million (Q4 2025) and $50.37 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Infosys | 52.40 Bn | 38.90 Bn | 1.60 Bn | 528.00 Mn |
| 2 | Cognizant Technology Solutions | 32.46 Bn | 25.62 Bn | 1.83 Bn | 812.00 Mn |
| 3 | Wipro | 22.47 Bn | 17.61 Bn | 745.00 Mn | -154.00 Mn |
| 4 | Cdw | 20.15 Bn | 18.14 Bn | 1.32 Bn | 891.20 Mn |
| 5 | Td Synnex | 19.94 Bn | 14.10 Bn | 1.43 Bn | 784.64 Mn |
| 6 | Cgi | 17.74 Bn | 15.53 Bn | - | - |
| 7 | Arrow Electronics | 11.21 Bn | 10.16 Bn | 1.13 Bn | 747.88 Mn |
| 8 | Avnet | 7.65 Bn | 6.83 Bn | 865.03 Mn | 634.01 Mn |
| 9 | EPAM Systems | 7.01 Bn | 2.65 Bn | 429.57 Mn | 245.25 Mn |
| 10 | Innodata | 2.02 Bn | 1.50 Bn | 42.46 Mn | 74.59 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 74.59 Mn |
| Mar 31, 2026 | 72.75 Mn |
| Dec 31, 2025 | 61.02 Mn |
| Sep 30, 2025 | 50.37 Mn |
| Jun 30, 2025 | 48.91 Mn |
| Mar 31, 2025 | 49.95 Mn |
| Dec 31, 2024 | 47.83 Mn |
| Sep 30, 2024 | 40.78 Mn |
| Jun 30, 2024 | 32.28 Mn |
| Mar 31, 2024 | 25.09 Mn |
| Dec 31, 2023 | 24.21 Mn |
| Sep 30, 2023 | 21.41 Mn |
| Jun 30, 2023 | 20.28 Mn |
| Mar 31, 2023 | 20.73 Mn |
| Dec 31, 2022 | 21.10 Mn |
| Sep 30, 2022 | 21.51 Mn |
| Jun 30, 2022 | 23.27 Mn |
| Mar 31, 2022 | 23.61 Mn |
| Dec 31, 2021 | 20.31 Mn |
| Sep 30, 2021 | 17.97 Mn |
API Access
Innodata 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=INOD&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "INOD", "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=INOD&period=max&api_key=YOUR_API_KEY");
const data = await res.json();