When should a life sciences organization build technology in-house? When is it smarter to buy off-the-shelf? And when does a strategic partnership make the most sense? These are the questions Derk Arts (CEO, Castor) and Nick Darwall Smith tackled in a candid LinkedIn Live discussion, breaking down the real costs, risks, and trade-offs of each approach.
The Core Considerations
Many organizations have faced the painful consequences of making the wrong build vs. buy decision—whether it’s an in-house project that spirals into a money pit, or an off-the-shelf solution that never quite delivers.
“One of the big pitfalls with internal development is not taking the full scope into consideration. What starts as a simple solution can become a long-term maintenance burden.” – Nick Darwall Smith
So how do you make the right call? The discussion highlighted eight critical factors:
- Uniqueness – Does this technology provide a true competitive advantage? If your solution gives you an edge that can’t be replicated, building might make sense. But if you’re reinventing the wheel, why not leverage existing solutions?
- Understanding of Requirements – If requirements are unclear or frequently changing, internal builds risk becoming endless development projects with scope creep.
- Complexity – The more complex the system, the more it demands ongoing maintenance, integrations, and updates—costs that are often underestimated.
- Time – Can you afford the internal build timeline? Delays could mean missing key opportunities or regulatory shifts that make your work obsolete before launch.
- Cost – Beyond licensing vs. FTEs, consider total cost of ownership—ongoing support, security, and compliance are often bigger expenses than the initial build.
- Support – Can your internal teams sustainably manage this technology? Vendors have dedicated support teams—does your org?
- Resources – Do you have the right talent in-house? If not, will gaps create bottlenecks?
- Infrastructure – Security, scalability, and compliance add significant burdens. Who owns the risk?
“It’s easy to underestimate the costs of maintaining a system. The first version is never the final version—business needs shift, and suddenly you’re dedicating an entire team just to keeping the lights on.” – Derk Arts
Spotting Red Flags
Both speakers emphasized the importance of recognizing warning signs early to prevent costly missteps:
- Internal projects that spiral out of control – The sunk cost fallacy often keeps teams investing in failing projects rather than pivoting to a better solution.
- Vendors that overpromise and underdeliver – If a vendor claims they can do it all without asking the right questions, that’s a red flag.
“The best vendors are the ones that ask tough questions—because they understand what’s truly required.” – Nick Darwall Smith
Making the Right Call
A strategic approach is crucial. In many cases, buying or partnering is the smarter, lower-risk option. But there are exceptions—such as when proprietary data or highly unique business processes demand internal control.
Nick shared a cautionary tale about an in-house project that ran three times over budget due to underestimated maintenance needs. The team assumed developers could support the system post-launch, but that assumption led to constant disruptions and ultimately required a full rebuild.
Another common mistake? Not factoring in industry evolution. Some solutions—like regulatory information management systems—require frequent updates to stay compliant. If you can’t keep pace, a vendor with dedicated regulatory teams may be the better option.
“What works today might not work tomorrow. The best decisions are the ones that leave room for future flexibility.” – Derk Arts
The Role of AI in Build vs. Buy
AI is reshaping the traditional build vs. buy framework, but common pitfalls remain:
- Don’t build your own foundational models – It’s tempting, but for most organizations, this is an unnecessary distraction.
- AI can accelerate development—but only if properly integrated – Junior developers can leverage AI tools to speed up coding, but complex systems still require human oversight.
- Regulatory concerns are real – Hallucinations, data leakage, and compliance risks mean AI-based tools must be carefully vetted.
- Experimentation has value – Even if you don’t end up building AI solutions in-house, small internal experiments can help you choose better vendors.
Final Takeaways
✅ If it’s not a differentiator, don’t build it – Owning technology that offers no strategic advantage is a waste of resources.
✅ Account for full lifecycle costs – The initial build is just the beginning—long-term maintenance often costs far more.
✅ Vet vendors aggressively – Look beyond sales pitches. Ask about reference customers, real-world performance, and support capabilities.
✅ AI adds complexity – If integrating AI, ensure your team has the expertise to manage it effectively.
For the full range of insights—including real-world case studies and practical advice on navigating build vs. buy vs. partner decisions—watch the complete discussion.