Factset Research Systems (FDS) Cost of Revenue (2009 - 2026)
Factset Research Systems (FDS) reported Cost of Revenue of $312.19 million for fiscal Q3 2026 (quarter ended May 31, 2026), up 11.2% from $280.73 million a year earlier and up 5.2% from the prior quarter.
Factset Research Systems (FDS) Cost of Revenue (2009 - 2026) Analysis & Trends
Over the twelve months ended May 31, 2026, Factset Research Systems' Cost of Revenue came in at $1.19 billion, up 11.1% year-over-year; for FY2025 (ended Aug 31, 2025), it was $1.1 billion, up 8.5% from FY2024.
- Cost of Revenue has increased for 15 consecutive fiscal years, with a five-year compound annual growth rate of 9.6% (FY2020 to FY2025).
- By fiscal year, Cost of Revenue came in at $1.01 billion in FY2024 (+4.0%), $973.23 million in FY2023 (+11.7%), $871.11 million in FY2022 (+10.8%) and $786.4 million in FY2021 (+13.1%).
- The fiscal Q3 2026 figure ranks as the highest quarterly Cost of Revenue in data going back to fiscal Q1 2010.
- Year over year, Cost of Revenue has now increased in each of the last seven quarters, with growth averaging 8.1% over the last eight quarters.
- The high point for year-over-year Cost of Revenue in five years was fiscal Q4 2022 (growth of 22.5%); the low point was fiscal Q4 2024 (a decline of 2.1%).
- Per Business Quant data, the three fiscal quarters before Q3 2026 came in at $296.74 million (Q2 2026), $287.92 million (Q1 2026) and $288.67 million (Q4 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Cost of Rev (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 113.90 Bn | 109.50 Bn | 2.98 Bn | 1.17 Bn |
| 2 | Moodys | 76.48 Bn | 68.91 Bn | 1.67 Bn | 518.00 Mn |
| 3 | Msci | 38.99 Bn | 37.33 Bn | 717.10 Mn | 149.90 Mn |
| 4 | Verisk Analytics | 21.33 Bn | 15.96 Bn | 572.90 Mn | 233.40 Mn |
| 5 | Equifax | 16.47 Bn | 15.87 Bn | 926.40 Mn | 773.70 Mn |
| 6 | TransUnion | 12.16 Bn | 9.14 Bn | - | - |
| 7 | Factset Research Systems | 9.52 Bn | 8.28 Bn | 310.73 Mn | 312.19 Mn |
| 8 | Morningstar | 6.89 Bn | 4.79 Bn | 423.90 Mn | 239.30 Mn |
| 9 | Mastercard | 480.84 Bn | 440.75 Bn | - | - |
| 10 | Cme | 94.54 Bn | 94.54 Bn | - | - |
Historic Data
| Date | Value |
|---|---|
| May 31, 2026 | 312.19 Mn |
| Feb 28, 2026 | 296.74 Mn |
| Nov 30, 2025 | 287.92 Mn |
| Aug 31, 2025 | 288.67 Mn |
| May 31, 2025 | 280.73 Mn |
| Feb 28, 2025 | 269.60 Mn |
| Nov 30, 2024 | 258.78 Mn |
| Aug 31, 2024 | 258.20 Mn |
| May 31, 2024 | 246.99 Mn |
| Feb 29, 2024 | 255.14 Mn |
| Nov 30, 2023 | 251.62 Mn |
| Aug 31, 2023 | 263.69 Mn |
| May 31, 2023 | 241.69 Mn |
| Feb 28, 2023 | 240.81 Mn |
| Nov 30, 2022 | 227.04 Mn |
| Aug 31, 2022 | 241.94 Mn |
| May 31, 2022 | 222.62 Mn |
| Feb 28, 2022 | 199.41 Mn |
| Nov 30, 2021 | 207.13 Mn |
| Aug 31, 2021 | 197.53 Mn |
Factset Research Systems Cost of Revenue 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=cost-of-revenue&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "cost-of-revenue", "ticker": "FDS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=cost-of-revenue&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();