Build Vs Buy

Build vs Buy

Make the build/buy/adopt decision on total cost and strategic fit, not on whichever option the room already favors. The two systematic errors this skill exists to counter: engineers underestimate the ongoing cost of building (maintenance routinely exceeds the initial build within a few years), and everyone underestimates the exit cost of buying (discovered only when leaving). Boundary: this skill decides whether the capability comes from outside; how you structure your own code is architecture-design's ADR territory, and vetting an individual library is dependency-management.

Workflow

Step 1: Frame the Capability — Core or Commodity?

Name the capability precisely, then ask the question that dominates the whole decision: does doing this better than anyone else win you customers? If yes (core differentiator), building earns strategic control; if it's commodity plumbing (auth, feature flags, payments, analytics infra, CRUD admin), every engineer-month spent building it is a month not spent on what differentiates you. Then split requirements into must-have and nice-to-have — a vendor covering 90% of must-haves usually beats a build covering 100% of a wishlist.

Step 2: Inventory the Real Options

There are usually more than two:

• Build in-house
• Buy a vendor/SaaS product
• Adopt OSS self-hosted (you're not buying — you're taking an operations job)
• Hybrid — buy/adopt the commodity core, build the thin differentiating layer
• Defer — do nothing yet; a legitimate option when the need is unproven

Verify current facts (pricing, licensing, maintenance status) with live sources — vendor pricing pages and OSS repos, not memory.

Step 3: TCO Over 3 Years — Not the Sticker Price

Cost each finalist over a 3-year horizon; year-1 comparisons systematically favor building (the build cost is visible, the ownership cost isn't):

• Build: initial engineering + ongoing ownership (maintenance, on-call, security patching, feature parity as needs grow — commonly comparable to the initial build per year for actively-used systems) + opportunity cost (what those engineers would otherwise ship — usually the largest and least-counted line).
• Buy: subscription at projected usage, not today's (model the pricing curve at 3× growth — usage-based pricing is where vendors recover their discounts) + integration + admin.
• Adopt (OSS): hosting + operations + upgrades + security response + the expertise to run it; "free" describes the license, not the ownership.
• Everyone forgets: migration of existing data/flows, training, compliance and security review, vendor management overhead.

Step 4: Risk — Lock-in, Exit, and Viability

Write the exit plan now, while you have leverage — the exit cost you can't estimate is the one that traps you:

• What does leaving take? Data export (format? completeness? egress fees?), proprietary APIs/features you'd re-implement, contract terms (lock-in period, auto-renewal, price-increase caps).
• Vendor viability: funding/profitability, roadmap alignment, acquisition risk, what happens to your data if they fold.
• OSS health: maintainer bus factor, release cadence, CVE responsiveness, license (copyleft implications, and watch relicensing-to-source-available history in the space) — delegate a deep package-level check to dependency-management.
• Build risk is real too: key-person dependency, and the "internal vendor" failure mode — a team obligated to maintain a product nobody funds.

Step 5: Decide and Record

Score the finalists in a weighted matrix — criteria and weights fixed before scoring (after, the matrix just rationalizes the favorite). State a clear recommendation with the dominant reasons, name the runner-up and what would have flipped the decision, and set a revisit trigger ("revisit if usage exceeds X / pricing changes >Y% / the maintainer goes dark"). Record it like an ADR — decision, context, alternatives, consequences — so the next team understands why, not just what.

Step 6: De-risk Before Committing

For close calls or large commitments, run a time-boxed pilot against your top must-haves and your real data volumes before signing multi-year terms or staffing a build. A two-week PoC is the cheapest insurance in this workflow — and pilot the exit too (run the data export once and look at what comes out).

Principles Applied

• Commodity → buy bias; differentiator → build bias — most wrong decisions invert this because building is more fun than integrating.
• Compare TCO, not sticker price — the build's cost is mostly after launch; the vendor's is mostly at renewal and exit.
• A decision without a revisit trigger is dogma — pricing, vendors, and your scale all change.

Cross-Skill References

• architecture-design — internal-structure decisions and the ADR format
• dependency-management — deep evaluation of a specific library/package
• project-proposal — wrap the decision in a business case for approval
• effort-estimation — sizing the build option honestly
• strategic-review — when the decision touches positioning or the moat