Buy and Build in Capital Markets Evolves Through Hybrid Technology Strategy

Capital markets firms are operating in one of the most demanding technology environments in recent years. Geopolitical volatility, tariff-driven market disruption, the shift to shorter settlement and extended trading hours, escalating regulatory complexity, the rapid emergence of agentic AI, and the increasing use of AI to identify and exploit software vulnerabilities have compressed the window in which firms can afford to stand still. Technology strategy, once an operational consideration, now sits at the centre of a firm’s capacity to compete. 

Asset managers and sell-side firms alike are realising that the buy-vs-build debate doesn’t have a clear winner, with pros and cons inherent in both approaches. A more agile approach has emerged over recent years in the form of buy and build. This model is rapidly becoming the default technology strategy for firms serious about sustaining competitive advantage in capital markets. 

What Is the Buy-and-Build Model in Capital Markets? 

The buy-and-build model (sometimes called the hybrid model) is a technology strategy in which firms purchase core software infrastructure from specialist vendors while simultaneously developing proprietary application layers in-house to generate differentiated capability and competitive edge. 

Rather than choosing between the rigidity of off-the-shelf vendor solutions and the overhead of building everything from scratch, buy and build allows firms to adopt the best of both approaches. The key principle is to buy common components while building differentiation – leading to a distinct competitive advantage and improved time-to-market. 

Complex but common infrastructure, prevalent across the industry, such as FIX engines, data transformation, market connectivity, and other trading workflow foundations, is well served by specialist vendors that have invested heavily in performance, reliability, scalability and compliance. Building these components in-house consumes engineering resources without generating a meaningful competitive advantage. 

The proprietary layer is different. Trading algorithms, cross-asset risk models, execution optimisation logic, and client-specific margin frameworks are areas where a firm’s unique data, expertise and judgment can generate measurable alpha. This is where in-house development is the better choice. 

Why Buy and Build Is Now the Dominant Strategy 

Industry data confirms what practitioners are experiencing on the ground. A survey of 50 capital markets professionals across the US, APAC, and EMEA found that 44% of firms plan to take a greater hybrid buy-and-build approach in the coming years — significantly outpacing the 26% who plan to rely primarily on either in-house development or off-the-shelf solutions. 

The patchwork problem has become unsustainable. Years of layering independent vendor solutions on top of one another have created technical debt that limits customisation, slows innovation, and compounds operational risk. Firms that relied heavily on off-the-shelf purchasing are confronting the long-term cost of monolithic vendor dependency and vendor lock-in and increasingly concluding that breaking away from it is not optional but necessary. 

Interoperability standards  

Open API platform technology has made it genuinely practical to combine vendor and proprietary components within a single, coherent architecture, avoiding the need for hard-coded, restrictive integration. Standards such as the FIX messaging protocol continue to play a foundational role in enabling communication between systems but require seamless transformation when interfacing with other protocols or proprietary models across the trading ecosystem. The integration complexity that once made hybrid models technically treacherous has been substantially reduced. 

Modular workflow platforms  

The emergence of modular, component-based platforms — built specifically to enable firms to customise off-the-shelf foundations with proprietary enhancements — has removed much of the friction that historically made buy and build difficult to execute. Firms can now accelerate deployment while retaining the ability to layer in differentiated capability without rebuilding from scratch. 

The commodity line keeps moving  

Once proprietary capabilities are now standard vendor offerings. Firms that continue to maintain these in-house consume resources that could be redirected toward genuinely differentiating work. 

AI and the economics of building 

AI-assisted development tools are making it faster and cheaper to build capabilities in-house, nudging build-vs-buy in favour of proprietary development for a wider range of use cases than was previously viable. But that doesn’t negate the value of battle-hardened, robust infrastructure that can provide a solid foundation on which firms can innovate. 

The Architecture Behind Effective Buy and Build 

For a buy-and-build strategy to deliver in practice, the underlying architecture must be designed for interoperability from day one. Three principles are non-negotiable. 

1. Context Awareness Across the Application Estate 

In modern trading architecture, selecting an instrument in one system should automatically and accurately propagate relevant information — market data, open positions, news, risk exposure — to every connected system, regardless of whether they originate from internal systems or third-party vendors. This shared context transforms a collection of discrete systems into a unified, intelligent workflow. 

2. Real-Time State Propagation 

The request-response model is insufficient for front-office workflows where data flows in milliseconds or less. Firms need event-driven messaging infrastructure (underpinned by tools like Apache Kafka or equivalent) that ensures state changes propagate instantly across the entire application estate. This is the data plane backbone that makes a genuinely component-based architecture function at the speed markets demand. 

3. Well-Defined Data Contracts 

Interoperability at scale requires agreed and fixed data schemes. Protobuf and Avro are common choices that allow disparate systems to communicate without bespoke translation layers. Without clearly defined data contracts and API’s, integration complexity compounds rapidly as the number of participating components grows, eventually undermining the flexibility that the buy-and-build model is designed to provide. These fixed APIs and data schemas also enable future component replacement. 

Starting With the User, Not the Technology 

One of the most common architectural mistakes in buy-and-build programmes is starting with the technology rather than the business outcome. A more effective approach is to begin with the use case: define the workflow, identify the systems, data, and processes involved, understand where the current architecture creates friction, and then determine what to buy, what to build, and how the components should interact. 

Low-code platforms, interoperability solutions, and modern cloud infrastructure have made this use-case-driven approach more accessible than ever, enabling firms to connect existing capabilities, automate workflows, and evolve their technology stack without the engineering overhead that once accompanied significant architectural change. 

This approach also guards against the temptation to atomise everything into the smallest possible components — an instinct that is technically understandable but often counterproductive. Instead, identifying the largest reusable business capabilities and integrating them into end-to-end workflows typically delivers a more practical and sustainable modernisation path. 

How Buy and Build Is Reshaping Vendor Relationships 

The rise of buy and build is transforming what firms expect from their technology vendors and what vendors need to deliver to remain relevant. 

According to a recent survey by Adaptive and Markets Media, capital markets firms are moving away from reliance on a single monolithic supplier and toward a more focused ecosystem of specialist vendors with deep domain expertise. Only 8% of respondents expect to consolidate around a single vendor. The majority are building selective, high-trust partner ecosystems rather than either concentrating supplier risk in one relationship or fragmenting it across dozens. 

Firms are no longer willing to accommodate vendors whose product roadmaps, integration postures, or commercial terms obstruct their ability to build agile, interoperable architectures. The expectation is genuine collaboration. Vendors must invest in understanding a firm’s business objectives and evolve their products accordingly, or they risk being replaced by more flexible alternatives. 

In the buy-and-build model, vendors are not one-off suppliers of packaged software. They are long-term strategic partners, expected to co-evolve their roadmaps alongside the firm and engage in genuine collaboration rather than transactional sales. Firms that have gone through major technology transformations consistently report that the quality of the vendor relationship was as important as the quality of the product. 

Managing Vendor Lock-In 

Vendor lock-in is a genuine risk in any capital markets technology programme, but it is frequently overstated as a reason to avoid deep vendor partnerships. Some degree of lock-in is inherent to any serious technology commitment, whether to a vendor platform, a proprietary architecture, or a key technical team. The more productive question is not how to eliminate lock-in but how to ensure the relationship is one in which difficult conversations (e.g., changing requirements, contract renegotiation, or strategic pivots) can be had constructively. 

Where firms can meaningfully reduce lock-in risk, open standards are the most effective tool. Adopting widely supported interoperability standards as a prerequisite in vendor evaluation processes creates architectural flexibility and accelerates vendor onboarding without requiring bespoke abstraction layers that consume time and deliver limited long-term value. 

The Cultural Shift Buy and Build Requires 

Technology strategy and organisational culture are inseparable. Adopting a buy-and-build model is more than a technical decision and requires a meaningful shift in how firms think about resource allocation, opportunity cost, and the relationship between technology teams and the business. 

The concept of opportunity cost is of critical importance to capital market firms. Every engineering hour spent maintaining a commodity system that could be purchased from a specialist vendor is an engineering hour not spent on the proprietary capabilities that generate competitive advantage. Firms that have successfully embedded a buy-and-build mindset tend to be those where technology leadership has clearly articulated this trade-off and built processes that enforce it — preventing the gravitational pull of legacy systems and established vendor relationships from consuming resources that should be directed toward differentiation. 

Equally important is change management. Redesigning an operating model or technology platform requires buy-in from across the organisation — from trading desks and risk teams through to legal, compliance, and senior leadership. The firms that navigate this most successfully tend to have dedicated change and transformation functions that understand both the technical dependencies and the business implications of each decision and can communicate those clearly to every stakeholder group involved. 

Buy Side vs Sell Side: Different Priorities, Same Direction 

While buy and build is a cross-industry trend, the motivations and emphasis differ meaningfully between buy-side and sell-side firms. 

Sell-side firms show a stronger inclination toward in-house development. More than half expect an increase in proprietary build activity, with only 8% anticipating greater reliance on off-the-shelf purchasing. This reflects the sell-side’s historically strong emphasis on technology control as a competitive differentiator — particularly in execution quality, risk management, and the ability to offer differentiated services to institutional clients. 

Buy-side firms are more comfortable with vendor-sourced components, with hybrid approaches leading the way at 58%. The buy-side’s competitive edge is more often expressed through investment process and portfolio construction than through technology infrastructure, making the case for building commodity systems in-house structurally weaker. 

In both cases, the underlying logic is to build where you genuinely add differentiated value, buy where the market has commoditised the problem, and be rigorous and honest about which is which. 

AI and the Evolving Buy-and-Build Equation 

Artificial intelligence is reshaping the buy-and-build debate in two distinct ways, and capital markets firms need to think carefully about both. 

AI as a Development Accelerator 

As a development tool, AI is a genuine productivity multiplier for buy-and-build programmes. It accelerates prototyping, assists with integration work across vendor and proprietary components, enables faster experimentation with new strategies, and reduces the cost of building capabilities that previously required significant engineering investment. Protocols like MCP (Model Context Protocol) are also emerging as integration accelerators, allowing AI systems to query and connect disparate applications — from monitoring infrastructure to data warehouses to proprietary systems — through natural language interfaces. 

This shift in development economics has a direct impact on the buy-versus-build calculus. As building becomes cheaper and faster, the range of use cases where in-house development is the superior choice widens. Firms with strong engineering cultures are already taking advantage of this. 

AI in Production Trading Systems 

As a component within production trading systems, AI demands considerably more caution. Its non-deterministic outputs make it unsuitable as an authoritative decision-maker in regulated, front-office environments where explainability, determinism, and auditability are essential. AI should therefore be deployed with appropriate governance, observability, and meaningful human oversight, enhancing decision-making rather than replacing it. 

While AI is rapidly improving, its strengths today lie in accelerating analysis, automation, and software development rather than generating the highly optimised, ultra-low-latency code required for production trading infrastructure. General-purpose AI can produce high-quality application code, but performance-critical trading systems still rely on specialist engineering expertise and rigorous optimisation. 

AI is also accelerating interoperability initiatives, helping firms map data models, generate message transformations, automate testing, document APIs, and simplify integration tasks. Combined with established messaging standards such as FIX and purpose-built interoperability platforms, AI reduces the time and effort required to connect complex technology estates without replacing the robust connectivity, governance, resilience, and operational controls required in production environments. Rather than acting as the integration layer itself, AI is making integration faster, more efficient, and easier to maintain. 

Firms also need a coherent, enterprise-wide AI strategy to govern how different teams adopt and deploy new capabilities. A useful framework is to think of AI adoption as a layered journey, starting from data platform foundations, through internal workflow efficiency, to hyper-personalised client interactions, and ultimately toward AI-assisted alpha generation. Each layer depends on the one beneath it. Skipping steps may create new risks rather than new capabilities. 

The Cost of Standing Still 

Firms that delay rethinking their technology strategy face compounding risk. Legacy off-the-shelf systems generate what practitioners call the patchwork effect — accumulated layers of independent vendor solutions that create technical debt, limit customisation, and erode a firm’s capacity to respond to new opportunities. The operational cost of maintaining this complexity grows steadily, even as the competitive cost of technological inflexibility becomes more acute. 

Launching new funds, entering new asset classes, onboarding businesses through M&A, and expanding into new regions all become materially faster and cheaper when the underlying operating model is designed for adaptability rather than stability. Modern technology platforms also help firms attract and retain engineering talent by enabling teams to work with contemporary tools and architectures rather than maintaining legacy systems. 

The buy-and-build model is not a shortcut. It requires upfront investment in architectural thinking, disciplined vendor selection, meaningful organisational alignment, and a clear long-term vision. But firms that make that investment are building technology platforms capable of sustaining competitive advantage through market disruption, regulatory change, and technological shifts that cannot yet be fully anticipated. 

The alternative of maintaining a legacy architecture, accepting vendor-imposed constraints on innovation, and deferring the hard work of modernisation is itself a strategic choice. In capital markets in 2026, it is a choice with an increasingly visible and accelerating cost. 

Key Takeaways: Buy and Build in Capital Markets 

• The buy-and-build model has replaced the traditional buy-vs-build debate as the dominant technology strategy in capital markets. 

• Buy common infrastructure from specialist vendors; build proprietary layers that generate differentiated alpha. 

• The boundary between commodity and differentiator shifts continuously and firms must reassess it regularly. 

• Effective buy-and-build architecture requires context awareness, real-time state propagation, and well-defined data contracts. 

• Open standards are foundational to interoperability and vendor independence 

• Vendor relationships are evolving from transactional procurement to long-term strategic partnerships; vendors must collaborate or risk being replaced. 

• The cultural and organisational dimensions of buy and build are as important as the technical ones. 

• AI accelerates the economics of building in-house but requires careful governance as a trading architecture component. 

• Firms that delay modernisation face compounding operational, competitive, and compliance risk. 

• Proven, production-grade infrastructure provides the resilience and performance required for low-latency trading.