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Build vs Buy

Definition

Build vs buy is a decision framework product teams use when they need a capability that could either be developed internally or acquired from a third-party vendor. The decision hinges on four criteria: whether the capability is a core competency, the time-to-market pressure, the total cost of ownership (including maintenance), and the strategic importance of controlling the technology.

Slack, for example, built its own real-time messaging infrastructure because message delivery is fundamental to its product. But Slack bought Screenhero for screen sharing rather than building it from scratch -- screen sharing was valuable but not the core differentiation. That distinction drives most good build-vs-buy decisions.

Why It Matters for Product Managers

PMs own this decision more often than they realize. Every time a team evaluates an analytics tool, a notification service, a payment processor, or an AI model provider, there is an implicit build-vs-buy choice. Getting it wrong is expensive in both directions -- building what you should have bought wastes engineering capacity, while buying what you should have built creates vendor lock-in on a critical capability.

The decision also has compounding effects. Shopify chose to build its own checkout infrastructure early on. That decision felt expensive at the time, but it later enabled Shop Pay, which now drives 10%+ higher conversion rates for merchants -- a competitive advantage that would have been impossible if checkout was outsourced to a third party.

How It Works in Practice

  • Define the capability clearly -- Write down exactly what the feature or system needs to do, the performance requirements, and the integration points. Vague scoping is the number one reason build-vs-buy analyses produce bad answers.
  • Score against four criteria -- Rate each option (build, buy, or hybrid) on: (a) core competency alignment, (b) time-to-market, (c) total cost of ownership over 3 years, and (d) strategic control. Weight the criteria based on your company's current stage.
  • Estimate the true build cost -- Multiply your initial engineering estimate by 2-3x to account for edge cases, testing, documentation, and ongoing maintenance. Most teams underestimate by at least 50%.
  • Evaluate vendor risk -- Check the vendor's financial stability, API reliability track record, contract lock-in terms, and what happens if they get acquired or sunset the product.
  • Decide and document -- Write a one-page decision record with your reasoning. Revisit in 6-12 months to see if the assumptions still hold.
  • Common Pitfalls

  • Not-invented-here syndrome -- Engineering teams default to building because it is more interesting, even when buying saves months and the capability is not a differentiator.
  • Underestimating maintenance -- A system that takes 3 months to build might require 1-2 engineers permanently for upkeep, bug fixes, and scaling. Factor that into the total cost.
  • Ignoring the hybrid option -- Sometimes you buy for speed and plan to build later once you understand the requirements deeply. Figma initially used third-party font rendering before building their own.
  • Treating the decision as permanent -- Markets change. A vendor that was the right call two years ago might now be a bottleneck. Re-evaluate annually.
  • The AI Build vs Buy framework applies this thinking specifically to AI/ML capabilities, where the build-vs-buy tradeoffs are especially sharp. For quantifying the financial side, see Cost-Benefit Analysis. Understanding technical debt is essential because building in-house always accumulates maintenance burden over time.

    Frequently Asked Questions

    When should a PM choose to build instead of buy?+
    Build when the capability is a core differentiator and you have the engineering capacity to maintain it long-term. Stripe built its own fraud detection system because payment security is central to its value proposition -- outsourcing that would mean ceding control over a critical customer experience.
    What hidden costs do PMs miss in build-vs-buy decisions?+
    Teams consistently underestimate ongoing maintenance costs for built solutions. A custom analytics pipeline might take 3 months to build, but consume 20% of an engineer's time indefinitely for bug fixes, scaling, and schema changes. Vendor solutions shift that burden but introduce dependency and integration costs.

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