The True Cost of Real-Time Market Data Infrastructure:
Quantifying the Build vs. Buy Debate for Capital Markets Firms

Our findings, based on real-world data from a Tier 1 global bank and Exegy’s more than 20 years of market data expertise, reveal that building in-house software-based market data infrastructure is significantly more expensive, time-consuming, and operationally burdensome than vendor solutions.

Key Findings

» In-House Software Development Is Expensive

• The first in-house software feed handler costs $1.8 million, while full North American equities and commodities coverage (20 markets) exceeds $4.7 million.
• In-house builds are ~8x more expensive than Exegy’s for full North American equities coverage.

» Software Maintenance Is a Recurring Cost Drain

• Annual in-house maintenance costs exceed $3.5 million, while Exegy’s fully managed solution reduces these recurring expenses by ~2.6x.
• In 2023 alone, Exegy managed 8,356 exchange notifications and 481 exchange-directed changes (EDCs), with 35% requiring code updates that demand extensive development and QA efforts.
• Managing EDCs in-house adds hidden operational risks due to regulatory and exchange-driven protocol updates.

» Vendor Solutions Offer Time Savings & Resource Optimization

• Exegy delivers production-ready solutions in 4-6 months, compared to 3.5 years for in-house teams—a 7x speed advantage.
• In-house development diverts skilled engineers and resources from strategic projects, increasing overhead and opportunity costs.
• Partnering with Exegy reduces internal workloads, allowing firms to focus on core business innovations.

Strategic Takeaways

Building market data feed handlers in-house may seem like a way to retain control, but the true cost in time, resources, and ongoing maintenance is substantial. 

Vendor solutions provide:

• Cost Savings: Lower total cost of ownership (TCO) by reducing development and maintenance expenses
• Faster Time to Market: Immediate access to fully managed market data solutions
• Reduced Operational Overhead: Vendors absorbing the burden of ongoing exchange-driven updates and system monitoring

For firms evaluating field-programmable gate array (FPGA)-based alternatives, our companion white paper details the costs and complexities of hardware-accelerated market data processing.

The ability to process real-time market data efficiently and reliably is a mission-critical capability for capital markets firms, including investment banks, hedge funds, and proprietary trading firms. 

As trading volumes grow and latency requirements become increasingly stringent, firms must decide whether to build their own market data processing infrastructure or buy from specialized vendors. This decision carries significant implications for cost, operational efficiency, and business agility.

Historically, many firms have built custom software-based market data processing systems to meet their trading and risk management needs. However, the true cost of building and maintaining these systems in-house is often underestimated. Engineering and operational expenses, ongoing compliance with Exchange-Directed Changes (EDCs), and the cost of scaling to new markets all contribute to significant long-term financial and resource burdens.

This paper provides a quantitative cost analysis of in-house software-based market data infrastructure, based on real-world benchmarks from a Tier 1 global bank and Exegy’s 20+ years of experience in market data technology. By breaking down the development, scaling, and maintenance costs of in-house software feed handlers, this paper offers critical insights into the long-term viability of in-house solutions versus vendor-managed alternatives.

Our Analysis Draws On

• Tier 1 Global Bank Insights: Real-world data from a Tier 1 global bank’s experience with building in-house software feed handlers
• Exegy’s Expertise: More than 20 years of specialized knowledge in developing market data processing solutions using both software and FPGA technology
• Comparative Cost Data: In-depth analysis of salary, overhead, and productivity metrics, providing a comprehensive view of total cost of ownership
• Operational Insights: Advanced data on managing EDCs, reflecting real-world resource demands and performance outcomes

For firms evaluating hardware-accelerated FPGA-based solutions, a companion white paper details the cost and complexity of building FPGA-based market data infrastructure, offering a side-by-side comparison of software and hardware approaches.

Industry Challenges Driving Market Data Infrastructure Decisions

Before diving into the challenges of building in-house, it is important to understand the broader industry dynamics compelling firms to reassess their market data infrastructure. Several key factors are placing unprecedented demands on existing systems:

• Rising Market Data Volumes: The rapid growth in trading activity and data generation is outpacing infrastructure upgrades, increasing the risk of outages and system failures.
• Constrained Budgets & Data Center Space: Firms face budgetary pressures and limited physical space for data center expansion, making it difficult to scale traditional infrastructures cost-effectively.
• Heightened Competition & Tightening Spreads: Increased market competition is driving tighter spreads, reducing fill rates, and forcing firms to optimize latency and data processing capabilities to maintain an edge.

Quantitative Industry Trends

• The volume of derivative contracts has increased 5x since 2020.
• Options contracts doubled compared to the previous year.
• Derivatives contracts surpassed 150 billion in 2024.
• The average daily trade volume of U.S. equities has risen 57% since 2019, from 6 billion to 11 billion shares.
• The upcoming tick size reduction is expected to significantly increase data volumes.

As firms look for strategic solutions to the above industry macro trends, those opting to develop in-house will face a range of operational, financial, and strategic hurdles. 

These challenges are amplified by the growing complexity of market dynamics, the rapid pace of technological change, and the need to remain competitive in a data-driven landscape. Understanding these obstacles is critical for firms to evaluate whether to build, buy, or use a hybrid approach for their market data infrastructure.

Operational Complexity

Managing Exchange-Directed Changes (EDCs) is an ongoing, resource-intensive process. In 2023 alone, Exegy processed 8,356 inbound exchange notifications, with 481 EDCs requiring analysis. Approximately 35% of these changes demanded code updates, testing, and deployment—consuming an average of 140 hours per EDC. This workload strains engineering teams, often diverting focus from strategic projects to routine maintenance.

Business Impact: Frequent EDCs introduce operational risk, increasing the potential for system outages or latency issues that can disrupt trading operations.

Inefficient Resource Allocation

In-house development consumes significant engineering time and expertise. Maintaining real-time market data infrastructure requires highly specialized talent, including low-latency software developers, FPGA engineers and architects, and QA specialists. This resource allocation pulls valuable personnel away from revenue-generating activities, such as algorithm development or trading strategy optimization.

Business Impact: Firms face higher personnel costs, increased turnover risk due to burnout, and reduced capacity for innovation in core business areas.

Increased Risk Exposure

Building and maintaining proprietary infrastructure exposes firms to operational and systemic risks. In-house systems are more vulnerable to outages if not continuously optimized, especially as market data volumes grow. Budget constraints and limited data center space further exacerbate these risks, making it difficult to scale infrastructure effectively.

Business Impact: Downtime during peak trading periods can result in significant financial losses, regulatory scrutiny, and reputational damage.

Scalability Challenges

As firms expand their market coverage, scaling in-house infrastructure becomes increasingly complex. Each new data feed or market requires added development, testing, and maintenance efforts. Unlike vendor solutions that offer plug-and-play scalability, in-house systems face bottlenecks that delay time to market and limit growth potential.

Business Impact: Delayed market access reduces competitive advantage, especially in high-frequency trading environments where any delays in getting your strategy into production can have a significant impact on the business.