This white paper quantifies the true cost of building and maintaining FPGA-based market data feed handlers. Drawing from real-world benchmarks at a Tier 1 global investment bank and vendor cost models from Exegy, we provide a comprehensive breakdown of the total cost of ownership (TCO), development effort, and long-term support requirements. Our analysis is intended to support informed decision-making in the ongoing build versus buy debate.

Key Findings

» Cost to build

• Developing the first in-house FPGA feed handler costs $5.35M, while building 18 handlers for full North American market coverage exceeds $9.7M.
• In-house FPGA builds are more than 5x more expensive ($9.7M) than Exegy’s ($1.8M).

» Time to market

• Exegy’s FPGA solutions go live in 6-9 months, while in-house development takes more than 3.5 years—a 6x time-to-market improvement.

» Maintenance costs

• 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 quality assurance (QA) efforts.
• Ongoing operational costs exceed $4.59M annually, driven by EDC response, optimization, and monitoring overhead.

» Vendor advantage

• At about $100K per market, Exegy's fully managed feed handlers offer a predictable, scalable cost structure—saving firms millions as they grow their global footprint.

This paper presents detailed cost models, timelines, engineering team structures, and operational risks to help firms objectively assess the strategic implications of FPGA infrastructure development.

Ultralow-latency market data processing is critical for firms engaged in high-frequency trading, market-making, and latency-sensitive algorithmic strategies. FPGA-based solutions are widely recognized as the gold standard for speed and determinism in market data processing, but they come with significant costs, complexity, and risk when built in-house.

Unlike software-based solutions, FPGA market data processing requires specialized engineering talent, customized hardware architectures, and extensive development cycles. Many firms underestimate the cost and effort required to design, build, and maintain FPGA-based feed handlers, leading to budget overruns, extended timelines, and increased operational risk.

This paper provides a quantitative analysis of in-house FPGA-based market data feed handlers, detailing the engineering effort, hardware costs, and ongoing maintenance burden compared to vendor solutions. By leveraging real-world benchmarks from a Tier 1 global bank and Exegy’s experience in FPGA-based market data processing, this report delivers actionable insights into the cost-benefit trade-offs of building versus buying FPGA technology.

This report is a direct companion to a white paper that analyzes the cost and complexity of building and scaling real-time feed handlers entirely in software. While that paper focuses on flexibility and rapid deployment, this analysis focuses on hardware-based performance at scale, giving decision makers a full-spectrum view of the build versus buy tradeoff.

Market Dynamics Driving Adoption of FPGA Infrastructure

The pressure on capital markets firms to reduce latency, handle exploding data volumes, manage increased volatility, and stay ahead of exchange-driven change has never been more intense. While many firms have historically relied on software-based solutions, the demand for determinism, ultralow latency, and throughput at scale has led to increased adoption of FPGA-based market data infrastructure.

This trend isn’t merely technological—it’s strategic. The shift toward hardware acceleration is being driven by multiple converging market forces:

Soaring Market Data Volumes

Trading activity continues to grow at an unprecedented rate, with derivative contract volumes increasing 5x since 2020 and U.S. equities daily trade volumes rising 57% since 2019. Regulatory changes, such as tick size reduction, and the increasing use of multi-leg order strategies are amplifying bandwidth and message rate pressures, pushing existing infrastructure to its limits.

Infrastructure as Strategic Edge

With tighter spreads and shrinking alpha, the infrastructure behind data delivery and execution becomes a core differentiator. FPGA systems enable highly predictable, low-jitter latency performance that drives smarter execution logic.

Constraints on Traditional Scaling

With limited data center space, rising power costs, and talent shortages in low-latency engineering, firms are struggling to scale legacy or software-only systems. FPGA-based architecture allows firms to offload compute, reduce system complexity, and optimize for both cost and space.

Purpose and Scope of This Report

This paper responds directly to these challenges by quantifying the real-world cost of building and maintaining FPGA-based market data feed handlers in-house. Specifically, we examine:

• The engineering effort and time to market for building a single feed handler and scaling to full North American market coverage
• The ongoing cost of support, latency optimization, and EDC compliance
• A side-by-side TCO comparison between in-house and vendor-managed FPGA solutions

This FPGA-focused paper complements our software analysis by showing how the economics change when performance and capacity constraints require hardware-based processing. For firms evaluating whether to engineer internally or partner with a vendor, this analysis quantifies not just capital expenditure but also the time, team structure, and ongoing maintenance burden involved in staying competitive at the hardware layer. 

Together, the two reports provide a decision-making framework for firms balancing latency, flexibility, and scalability.

While the performance benefits of FPGA are well understood, the operational realities of in-house FPGA development are frequently underestimated. Unlike software systems, FPGA-based architectures introduce a fundamentally different development model with significantly more rigid workflows, fewer reusable components, and higher personnel demands.

Dual Development Workflow: Software & Hardware Complexity

Building FPGA systems requires both a complete software implementation and a hardware translation phase using hardware description languages (HDLs), such as Verilog or VHDL. Development is inherently sequential, with software logic needing to stabilize before hardware can be synthesized and optimized. This results in longer project timelines, greater coordination overhead, and a higher chance of rework.

Specialized Engineering Talent & High Salary Costs

FPGA development requires niche roles:

• Hardware engineers (HWEs) for FPGA logic
• Hardware architects (HWAs) for design optimization and validation
• Low-latency software engineers (SWEs) for support and integration

Recruiting and retaining these specialists is difficult and expensive, especially in capital markets hubs.

Recruiting and retaining this talent is not only costly, but it also creates a key person dependency risk that can impact timelines, maintenance, and product continuity.

Scaling Challenges: Low Reusability Across Markets

While some hardware blocks can be reused, each market protocol typically requires custom parsing logic and hardware-level adaptation, reducing scale economies. Most market-specific feed handlers require:

• Custom hardware parsing logic
• Protocol-specific optimizations
• Manual hardware validation per exchange

Unlike software-based systems, where code modularity accelerates expansion, FPGA feed handlers don’t scale linearly. Each new exchange adds nontrivial effort and cost, making broad market coverage a significant burden.

Toolchain and Testing Constraints

FPGA development is inherently constrained by proprietary toolchains, which have steep learning curves. These toolchains include:

• Long synthesis and place-and-route cycles
• Vendor-specific firmware flows
• Incompatibilities across platforms and chipsets

FPGA debugging is also more complex, requiring hardware simulation tools, synthesis, and in-hardware verification. Standard testing methodologies, such as CI/CD, are harder to apply, slowing QA processes and increasing deployment risk.

Maintenance Complexity and Lifecycle Risks

Unlike software, FPGA updates for EDCs require full resynthesis, bitstream regeneration, and regression testing under hardware conditions. Maintenance cycles are slower, riskier, and more resource-intensive.

Firms that underestimate this burden may face:

• Delayed compliance with exchange-mandated changes
• Latency drift from unoptimized logic
• Hardware lifecycle management risks (e.g., migration issues)

FPGA hardware has a longer lifecycle but lower flexibility, making fast adaptation more difficult without vendor support.

These challenges compound over time, making it clear that FPGA development isn’t just an engineering problem—it's also an organizational and strategic one. 

FPGA is not simply “faster software”—it’s an entirely different engineering discipline. The decision to build in-house must factor in not just capital cost but also development sequencing, engineering risk, and long-term support liabilities.