Originally published on
Build vs Buy: The Framework for Engineering Leaders
How to make the call without analysis paralysis — and the $200K mistakes we've seen.
The Wrong Question
"Should we build or buy?" is the wrong question. It assumes two clean options. In reality, the decision space looks more like this:
Build from scratch — full control, full cost
Buy SaaS — zero maintenance, vendor dependency
Buy + customize — partial control, integration tax
Open-source + host — free software, your ops burden
Partner / outsource the build — external expertise, internal ownership
Most teams collapse all of these into "build vs buy" and then spend 6 weeks in analysis paralysis because neither pure option feels right.
The 4-Question Framework
After watching dozens of teams agonize over this decision (and making some expensive wrong calls ourselves), we use four questions. Answer them honestly and the decision usually becomes obvious.
Question 1: Is This Core to Your Product?
This is the only question that matters more than cost.
If the capability is what customers pay you for — it's your competitive edge, your differentiation, the reason you exist — you build it. Always. Even if it's expensive. Even if there's a SaaS tool that does 80% of what you need.
Core: Stripe built their own payment processing engine. That IS Stripe. Buying a white-label payment processor would have been absurd.
Not core: Stripe uses Slack for internal communication. Building a custom chat tool would have been absurd.
The trap: everything feels core when you're building it. Teams convince themselves that their CI/CD pipeline is "special" or their internal analytics dashboard needs "custom logic." Test it with this question: Would a customer switch to your competitor if they had a better version of this specific thing?
If no, it's not core. Buy it.
Question 2: Does a Mature Market Solution Exist?
"Mature" means: 3+ years in production at companies your size, public pricing, documented migration paths, active community or support team.
If the market solution is mature:
- The buy option is probably better than what you'd build in 6 months
- The total cost is known and predictable
- You can switch vendors if it doesn't work (mature markets have competition)
If the market is immature (fewer than 3 credible options, all pre-Series B, pricing changes quarterly):
- The buy option will change under you
- You'll spend as much time working around vendor limitations as you would have spent building
- You might end up rebuilding anyway when the vendor pivots or dies
Framework: Mature market + not core = BUY. Immature market + not core = open-source + host, or wait.
Question 3: What's Your True Total Cost of Ownership?
The build side always underestimates. The buy side sometimes does too.
Build costs teams forget:
- Ongoing maintenance (20% of build cost per year, minimum)
- On-call burden (someone has to wake up at 3am for your custom system)
- Opportunity cost (those 3 engineers could be building product features)
- Knowledge concentration risk (what happens when the person who built it leaves?)
- Security patching (you own every CVE in your custom code)
Buy costs teams forget:
- Integration engineering (connecting SaaS to your systems takes real work)
- Per-seat pricing at scale (that $10/user/month is $120K/year at 1,000 employees)
- Migration cost when you eventually switch (data export, retraining, workflow changes)
- Compliance review (every new vendor is a SOC 2 questionnaire)
We built a spreadsheet model: take the vendor quote, multiply by 1.4x for integration and compliance costs. Take the build estimate, multiply by 2.5x for maintenance and opportunity cost over 3 years. Compare the 3-year totals. This model has been right within 20% for every decision we've tracked.
Question 4: What's the Blast Radius of Getting It Wrong?
If you build and it fails, what happens? You've spent 6 months of engineering time and you buy the SaaS tool anyway. Bad, but recoverable.
If you buy and it fails, what happens? You're locked into a contract, your data is in their format, and migrating is a 3-month project. Also bad, but also recoverable.
The real risk isn't choosing wrong — it's choosing slowly. Analysis paralysis costs more than either wrong choice, because while you're deciding, your team is blocked.
Our rule: If the 4 questions don't produce a clear answer within 2 weeks, default to buying. You can always build later with better information. You can't get back the 3 months you spent deliberating.
The Decision Matrix
| Core to Product | Not Core | |
|---|---|---|
| Mature Market | Build (reluctantly consider buying + heavy customization) | Buy |
| Immature Market | Build | Open-source + host, or wait |
This matrix handles 90% of decisions. The remaining 10% are genuinely hard calls — and those are worth spending time on.
Real Examples (Names Changed)
Company A (Series B fintech, 80 engineers): Spent 8 months building a custom feature flag system. Result: works, but fragile, maintained by one engineer. LaunchDarkly would have been $1,200/month. The custom system cost ~$400K in engineering time and continues to cost $80K/year in maintenance. Feature flags are not core to a fintech product. This was a $500K mistake.
Company B (Seed-stage dev tools, 12 engineers): Bought a popular observability SaaS. 6 months later, their specific use case (eBPF-based kernel tracing) wasn't supported. They spent 4 months building custom integrations. Then the vendor raised prices 3x. They rebuilt on open-source (Grafana + Prometheus + custom exporters) in 6 weeks. The initial "buy" decision cost them 10 months. Observability WAS core to their product.
Company C (Growth-stage SaaS, 40 engineers): Deliberated for 4 months about whether to build or buy an internal developer portal. While they deliberated, developer onboarding time stayed at 3 weeks. They eventually bought Backstage (open-source + host). The 4-month delay cost more than either option would have.
The Build-vs-Buy Anti-Patterns
"We can build it in a weekend." No you can't. Building it takes a weekend. Making it production-ready takes a quarter. Maintaining it takes forever.
"The vendor is too expensive." Compare the vendor cost to the fully loaded cost of the engineering team that would build and maintain it. Include their salary, benefits, management overhead, and opportunity cost.
"We need full control." Of what, specifically? If you can articulate exactly what control you need and why, that's a valid argument. If "full control" is a vague feeling, it's not.
"What if the vendor goes away?" What if your key engineer goes away? Both are risks. Mature vendors with public pricing and data export capabilities are lower risk than most people think.
"Let's build an MVP and see." MVPs become permanent. If you're going to build, commit to building it properly. If you're not ready to commit, buy.
The Conversation to Have With Your Team
Before the next build-vs-buy decision, align on these principles:
Default to buying unless there's a clear reason to build. This is counterintuitive for engineering teams, but it's the right default.
Set a 2-week decision deadline. If you can't decide in 2 weeks, you don't have enough information, and more deliberation won't help. Default to buying and revisit in 6 months.
Document the decision and the reasoning. In 18 months, you'll either validate or learn from it.
Review build-vs-buy decisions annually. What you bought 2 years ago might be worth building now. What you built 2 years ago might be worth replacing.
The Annual Review Process
Build-vs-buy decisions aren't permanent. The market changes, your team grows, and what was the right call 18 months ago may not be the right call today.
We run an annual "infrastructure review" where we revisit every significant build-vs-buy decision from the past year. The template:
| Decision | Date | Choice | Reasoning | Outcome | Would We Decide Differently Today? |
|---|---|---|---|---|---|
| Monitoring stack | 2025-Q1 | Build (Grafana+Prometheus) | Core to our ops, no SaaS matched our needs | Excellent — saved ~$4K/month vs Datadog | No change |
| Feature flags | 2025-Q2 | Buy (LaunchDarkly) | Not core, mature market | Good — $1,200/month, zero maintenance | No change |
| AI inference | 2025-Q3 | Build (self-hosted) | Cost at scale, data residency | Mixed — 3.8x savings but ops burden is real | Would start hybrid earlier |
| CI/CD | 2025-Q1 | Optimize existing (GitHub Actions) | Already invested, just needed tuning | Excellent — — the point is that optimization of existing tools often outperforms buying replacements. AI/ML infrastructure: Increasingly a build decision at scale. When your API bill crosses $10K/month, the build case strengthens dramatically. We documented the — there's no build case here unless you're HashiCorp. Cloud strategy: The build-vs-buy mindset applies to cloud decisions too. Going — a detailed build vs buy analysis for AI infrastructure — build vs buy applied to cloud strategy decisions We help engineering teams make infrastructure decisions that stick. If you're facing a build-vs-buy decision on infrastructure or platform tooling, we've been through it dozens of times. Talk to our engineering team → Subscribe to our newsletter for weekly deep-dives into engineering leadership decisions. Vollständiger Original-Artikel Den kompletten Beitrag mit allen Details direkt auf dev.to lesen. Wie bewertest du diesen Beitrag? 1 Klick Feedback Teilen mit Netzwerk & Team: Hat Ihnen dieser Tipp / Anleitung geholfen? Community-Analysen & Experten-Meinungen 0Verfasse deine eigene Analyse, teile Workarounds oder diskutiere diesen Vorfall im Blog. Noch keine Community-Analyse verfasst. Markiere einen Textabschnitt oder klicke oben auf „ Eigene Analyse verfassen“! Community Pulse: Relevanz-Einschätzung 1 Klick Experten-Votum 🔴 Akute Relevanz 0% 🟡 In Evaluierung 0% 🟢 Keine Auswirkung 0% Spannende Innovation 0% Verwandte Story-Cluster & Quellen (Vektor-KI) Tipp: Mit Pfeiltasten [ ← ] und [ → ] blättern 
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