VP of Engineering Build vs Buy Decisions at Series B Companies: Operational Clarity, Stage-Aware Execution, and Technology Leadership Constraints

Stage-Specific Build vs Buy Decision Framework

Series B VPs of Engineering have to ground build vs buy calls in two questions: What do we absolutely need to own to stand out? And do market solutions meet our technical and operational needs without locking us in or creating integration headaches?

Defining Core Competencies and Business Criticality

Core competency assessment matrix:

Business criticality scoring (1-5 scale):

• Impact on revenue (customer-facing)
• Dependency depth (blocks multiple teams if down)
• Data sensitivity (proprietary algorithms, customer data)
• Competitive moat (hard for rivals to copy)

Rule → Example:

• Rule: If a system scores 4-5 on multiple dimensions, build internally.
• Example: A custom pricing engine with unique algorithms and direct revenue impact.

Systems scoring 1-2 across all categories? Default to buying.

Evaluating Solution Fit: Custom, Commercial, and Hybrid Approaches

Solution evaluation framework:

Fit assessment checklist:

• API quality and extensibility
• Data export and portability
• Security and compliance (SOC 2, GDPR, etc.)
• Vendor financial health
• Integration effort (time to production vs. build timeline)

Rule → Example:

• Rule: Use microservices to wrap commercial solutions with internal APIs to reduce switching costs.
• Example: Stripe payments behind a custom API gateway.

Decision-Making Models and Scoring Frameworks

Organizations using structured decision frameworks see 30-40% fewer failures than those making gut calls.

Decision matrix template:

Scoring rules:

• Time to market: Buy scores higher if launch is under 3 months.
• Total cost: Add engineering salaries, infra, and lost feature opportunity.
• Core expertise: Build only if your team has the skills.
• Customization: Score by % of needs met off-the-shelf.
• Maintenance: Factor in team size and existing tech debt.
• Vendor risk: Score by vendor stability and data portability.

Common Series B Failure Modes

• Building features mature SaaS already nails (auth, payments, analytics)
• Buying rigid enterprise tools that need heavy customization
• Underestimating integration pain for commercial tools
• Skipping decision criteria before vendor shopping
• Locking into permanent tech for temporary problems

Rule → Example:

• Rule: Always run a reversal cost analysis - what does it take to switch after a year?
• Example: Migrating from a custom analytics tool to a SaaS platform after 12 months.

Operational and Financial Execution: Cost, Scale, and Risk for Series B

Series B engineering leaders have to nail three things: true ownership cost (not just sticker price), integration complexity and scalability, and how to allocate limited engineering resources.

Total Cost of Ownership and Ongoing Maintenance Considerations

Build vs Buy TCO Components

Hidden Maintenance Multipliers

• Data migration: Legacy exits cost 15-25% of original build
• Feature parity: Competing with vendors needs 2-3 engineers
• Performance monitoring: Custom builds need extra observability

Rule → Example:

• Rule: Calculate TCO for 3-5 years, not just year one.
• Example: Stripe’s fees eliminate PCI compliance and maintenance costs.

Integration, Scalability, and Technical Debt Management

Integration Complexity by System Type

Scalability Decision Matrix

• 5-10× growth: Buy if vendor has reference customers at target scale
• 10-50× growth: Hybrid - build core, buy supporting systems
• Vendor lock-in: Can you export data and rebuild in 6-12 months?

Technical Debt Management Checklist

• Document all vendor API dependencies and versioning
• Maintain abstraction layers for critical bought tools
• Schedule quarterly architecture reviews for build vs buy
• Track % of engineering time on maintenance vs. new features

Resource Allocation, Team Capabilities, and Opportunity Costs

Engineering Capacity Allocation Framework

Opportunity Cost Calculation

A 5-engineer team spending 6 months on payments:

• Direct cost: $300K-500K in salaries
• Opportunity cost: 2-3 major features delayed
• Competitive risk: Slower to market than Stripe users

Resource Allocation Decision Rules

• Build if the system is a competitive edge and you have the skills
• Buy if speed matters more than customization
• Pilot vendors for 30-60 days before big contracts
• Keep at least 50% of engineers on core product to maintain valuation

Team Capability Gap Assessment

1. List skills needed for the build (ML ops, security, infra automation)
2. Audit current team vs. needs
3. Hiring timeline: 3-6 months for specialized engineers
4. Ramp time: 2-3 months for new systems

User Adoption Urgency Table

Rule → Example:

• Rule: Buy when systems block revenue growth and need fast deployment.
• Example: Payments, onboarding, or core customer features.

End of section.

Frequently Asked Questions

VPs of Engineering at Series B companies have to juggle team size, runway, and tough competition - so every build vs. buy call gets shaped by those limits. Balancing what needs to ship now against what’s sustainable for your team isn’t easy, and team capacity is always a big factor.

What factors should a VP of Engineering consider when making build vs buy decisions?

Critical Decision Factors by Category

Evaluation Sequence

1. Is this capability a core differentiator for your product?
2. Calculate total cost of ownership for 18-24 months.
3. Assess if your team has the skills - or how long it’ll take to hire them.
4. Map the solution to your main business objective (growth, scale, optimize).
5. Check vendor maturity and lock-in risk if buying.
6. Figure out how much leadership time ongoing decision-making will need.

Reported Outcomes

• Using structured decision frameworks: 30-40% fewer implementation failures vs. ad-hoc choices (source)

How does company stage (Series B) impact a VP of Engineering's approach to build vs. buy?

Series B Stage Constraints

• Typical runway: 18-24 months to next milestone
• Engineering team: 15-50 people
• Revenue pressure: $3M-$10M ARR, aiming for 2-3x growth
• Customers: Enterprise buyers want feature parity with rivals

Build vs Buy Priorities by Stage

Series B Decision Rules

• Build if:

  • Drives revenue or retention directly
  • Needs fast iteration from customer feedback
  • No mature vendor solution fits

• Buy if:

  • Commodity capability (auth, payments, monitoring)
  • Needs deep expertise your team lacks
  • Faster to market = hitting customer or revenue milestones

Series B companies pick proven solutions to move fast and save resources for what really sets them apart.

What are the long-term implications of build vs buy decisions for engineering teams in Series B startups?

Technical Debt Accumulation Patterns

Team Capacity Impact (24 Months)

• Building non-core: 20-30% of initial team stays on (1-2 engineers min)
• Scaling: +10-15% for performance and reliability
• Leadership: Ongoing attention for roadmap and prioritization

Long-Term Ownership Costs

• Security patches and compliance
• Scaling infra and reliability
• Feature updates to keep up with market
• Documentation and knowledge transfer
• On-call rotation for incidents

Hiring and Retention Effects

• Series B companies building too many non-core systems: 25% higher engineer turnover due to maintenance overload.