Determine Your Build Versus Buy Strategy

Choosing between building a custom Product Lifecycle Management (PLM), Product Information Management (PIM), or Digital Asset Management (DAM) platform versus purchasing a commercial solution is a pivotal decision for any organization managing complex product data. While the appeal of a bespoke system is understandable—especially for companies with unique workflows or legacy constraints—the long-term implications of that choice can be profound. Build Versus Buy isn’t new in any way: Businesses have struggled with these decisions for decades.

Many companies vacillate between these two options, building in an attempt to satisfy a corporate directive to create systems that seamlessly function as one, and then to commercial solutions that have already been developed to satisfy the needs from a business perspective. Models that swing between these two options are often the most expensive to manage, as these companies resort to trying to integrate homegrown systems with commercial platforms that don’t speak the same programming language, data models, or have overlapping use cases. Therefore, understanding which strategy is right for your business isn’t just a short-term decision that can be easily undone: It has long-term consequences that have significant costs.

This post explores the strategic trade-offs, lessons learned from real-world implementations, and the evolving advantages of commercial platforms, particularly in the context of SaaS and CI:CD (Continuous Integration:Continuous Deployment) platforms.

Commercial Platforms

One of the most significant advantages of commercial product data platforms is their evolution through exposure to a wide range of use cases. Platform vendors have refined their platforms by serving hundreds or thousands of customers across industries—from retail and manufacturing to healthcare and media, and have lessons learned regarding the scalability, security, and use case needs of these types of businesses. This diversity in learning has led to:

• Robust Feature Sets: Commercial platforms often include flexible data modeling, multilingual support, automated asset tagging, AI integrations, and syndication tools that address common pain points across verticals.
• Scalable Architecture: These platforms are built to handle large volumes of data and assets, with performance optimizations that benefit from years of refinement. SaaS hosting has allowed the scalability to be fine-tuned by the client, ensuring the right services have the right memory, storage, and processing power at the time of need. Older on-prem tools require constant availability of memory, storage, and processing horsepower at the highest level required to support the largest possible need at any one moment in time, wasting resources during times of low usage.
• Continuous Improvement: SaaS platforms leverage CI:CD (Continuous Integration and Continuous Deployment) pipelines to roll out updates, security patches, and new features regularly—without disrupting users or requiring manual upgrades. Many of these platforms don’t require downtime to make these changes, as MACH principles and message queues have allowed individual servers to be upgraded while not shutting down the entire platform.
• Shared Cost: This shared development model means that the cost of innovation is distributed across a broad customer base, making enhancements more affordable and sustainable. In contrast, custom-built platforms rely on a single organization to fund every improvement, bug fix, and integration—often leading to ballooning costs and slower progress.

Lessons from the Field: The Hidden Costs of Building

Organizations that opt to build often underestimate the long-tail costs of maintaining and evolving their platforms. Consider the case of a mid-market consumer goods company that developed its own PIM to support a proprietary product taxonomy. While the initial rollout met internal needs, subsequent changes—such as integrating with new retail channels or supporting GS1 standards—required extensive rework. Each enhancement had to be scoped, funded, and tested internally, with no external roadmap to lean on. In many businesses, these changes never occur, as the budgets to build and support do not involve roadmaps for how to evolve those platforms over time.

Take an example we at KKMD ran into recently, where the database chosen by a company was no longer meeting the needs of the tool they had created. Because of the choice of a Mongo database and a desire to switch to a more standard SQL-based database required a rewrite of much of the code was required, as the technologies to read and write from these two differing database styles are completely different. The company was looking at a 1x budget compared to their original build. Instead of making the change, they simply lived with the lower performance of their document store rather than incur the cost of a near-total rewrite. The business had to suffer through a choice made years before because the cost of making the change they needed to support their business partners was paid by a single company.

PIM and DAM companies, which change their database formats to match the newest technology on a regular basis, can absorb this cost because they have hundreds of customers each paying a little towards these improvements. No one customer bears the entire cost, making it efficient to change technologies rapidly and often.

AI advancements, hosting technologies, and other rapidly evolving technologies are easier for a commercial tool to bear than a single business, making in-house tools much more static and having far fewer features over time.

Key lessons include:

• Remediation Costs Are Higher: When a commercial platform needs a change, that request may be shared by dozens of other customers, making it more likely to be prioritized and cost-effective. In a custom build, you’re the only customer—every change is yours to fund.
• Technical Debt Accumulates Quickly: Without a dedicated product team and disciplined release cycles, custom platforms often fall behind in security, performance, and usability.
• Innovation Is Isolated: Commercial vendors benefit from customer feedback loops, industry trends, and competitive pressure. Internal builds rarely have the same momentum.

The Power of SaaS and CI/CD

Modern commercial platforms are typically delivered as Software-as-a-Service (SaaS), which brings several operational advantages:

• Automatic Updates: New features and security patches are deployed seamlessly, reducing IT overhead.
• Elastic Infrastructure: SaaS platforms scale with your data and user base, without requiring hardware provisioning or manual tuning.
• Rapid Deployment: CI/CD pipelines allow vendors to iterate quickly, respond to customer feedback, and deliver improvements on a predictable cadence.

These capabilities are especially valuable in fast-moving industries where agility and uptime are critical.

The Pros of the Build Methodology

Despite the advantages of buying, there are scenarios where building a bespoke solution is the right call:

• Highly Specialized Use Cases: If your organization has unique workflows, data structures, or compliance requirements that no commercial platform supports, a custom build may be necessary. As PIMs and DAMs have evolved, with more advanced data modelling and workflows, this has become less of an issue. However, it may be that the primary use case your business has will never be satisfied by off-the-shelf tools.
• Integration Complexity: In rare cases, legacy systems or proprietary infrastructure may make integration with commercial platforms prohibitively complex. Dealing with older batch-based systems like DB2 or early iSeries systems may not function well in API-enabled systems, or the security requirements for integrations between on-prem and cloud systems may not be possible.
• Mission Critical Requirements: A solution may be so mission critical to a business that purchasing an outside tool is too risky for its business continuity. Customer-facing, high-availability systems like Ecommerce shopping carts and internal search may be required to have functions that are either not configurable, customizable, or secure enough for the requirements of some businesses. Although a commercial platform might have the features a business wants, support, up-time availability, real-time availability, or other factors may dictate a build decision to mean particular SLAs or security concerns.

In these cases, organizations should approach building with a product mindset: invest in modular architecture, prioritize maintainability, and plan for long-term evolution.

Conclusion

For most organizations in the product data space, buying a commercial PLM, PIM, or DAM platform offers faster time-to-value, lower total cost of ownership, and greater scalability. The shared development model, combined with SaaS delivery and CI/CD innovation, ensures that these platforms stay current, secure, and responsive to evolving needs.

Building a bespoke solution should be reserved for cases where commercial tools cannot meet critical requirements—and even then, with a clear understanding of the long-term investment required. By aligning platform strategy with business goals, organizations can make informed decisions that support growth, agility, and operational excellence.

When considering your business’s Build Versus Buy methodology, short-term gains in functionality never equate to long-term cost savings on future development. Ensuring your plan includes the total cost of ownership (TCO) on both potential methodologies for a 10-year span, including road map development, is vital to ensuring that your build project doesn’t become a resource black hole and that your buy decision doesn’t lock you in to poor functionality for your business use cases.