As a VP of Product Management at HighLevel, Inc., I wrestle with the build-versus-buy question nearly every week. It’s a timeless dilemma, now intensified by generative AI. As one summary puts it, “One topic that has been around since the beginning of the tech industry, is whether we should build or buy in order to solve some problem? This question applies to traditional IT, as well as to every product team. There are often one or more buy alternatives, but each comes with an associated cost, and…”
My take: build vs buy is not a procurement question—it’s a product strategy decision. The right answer depends on whether the capability creates durable differentiation, how quickly we need to learn, total cost of ownership, and the risks around data, compliance, and vendor lock-in. In practice, I anchor the debate in product discovery: what problem are we solving, for whom, and how will we know we’ve succeeded?
When I choose to build, it’s because the capability is core to our product’s competitive advantage, relies on proprietary data or unique workflows, or demands tight integration across the end-to-end customer journey. In these cases, my team and I accept the higher upfront investment because it compounds into long-term strategic control and faster iteration.
When I choose to buy, it’s because the capability is commoditized, speed-to-market matters more than novelty, or the vendor brings specialized compliance, uptime, or scale that would be expensive to replicate. Buying can be the fastest path to validated learning—especially when we need to unblock a roadmap dependency or de-risk a complex integration.
The AI era changes the calculus but not the fundamentals. With gen ai, we can prototype quickly using off-the-shelf models, then decide if we should converge on a managed service, an open-source stack, or a hybrid. The hidden work is real: evaluation harnesses, prompt governance, data pipelines, monitoring for model drift, and cost controls for inference. These become part of the true total cost of ownership—not just license fees versus engineering hours.
In my teams, I often deploy forward deployed engineers alongside product discovery to co-create solutions with customers. We use gen ai for product prototyping to validate value early, test prompts and retrieval patterns, and stress-test edge cases. If the prototype proves the value, we assess whether to keep the vendor in place or transition to a build for differentiation, control, and margin.
Here’s the practical playbook I use. First, define the outcome and non-negotiables: data privacy, latency, SLAs, and compliance. Second, run rapid experiments to quantify value—speed beats speculation. Third, model TCO across 12–24 months, including staffing, MLOps, eval frameworks, and expected usage growth. Fourth, pressure-test vendor lock-in: portability of prompts, embeddings, and fine-tunes; data ownership; exit paths. Fifth, stage-gate the decision: buy to learn fast, then build (or stay bought) based on evidence.
One recent example: we launched a gen ai capability using a vendor to achieve immediate time-to-value and validate demand. In parallel, we scoped a build option gated by adoption and unit economics. The vendor path gave us customer outcomes within weeks; the build path unlocked deeper integration and margin once the signal was strong. That dual-path strategy reduced risk without slowing us down.
Ultimately, the smartest build-versus-buy choices align with product management leadership principles: focus on customer outcomes, quantify opportunity cost, design for learning, and avoid irreversible commitments when uncertainty is high. In the age of AI, those principles still apply—only faster.
Inspired by this post on SVPG.
