Why this matters
As an AI Product Manager, you will routinely decide whether to build an AI capability in-house, buy a vendor solution, or partner with another company. These choices affect your time-to-market, differentiation, cost structure, compliance exposure, team hiring plans, roadmap risk, and ultimately customer outcomes.
- Launch a feature on time: choose a pathway that fits deadline and quality.
- Protect differentiation: avoid commoditizing core IP.
- Control cost and risk: model total cost of ownership and vendor lock-in.
- Accelerate distribution: leverage partners when they provide data, channels, or credibility you lack.
Concept explained simply
Build means your team develops the solution. Buy means you procure a third-party product or API. Partner means you co-create, integrate deeply, or go-to-market together, sharing value and risks.
When to BUILD
- Core differentiation or strategic IP
- High privacy/compliance constraints
- Long-term cost control and flexibility matter
- Your team has strong capabilities and capacity
When to BUY
- Commodity capability or solved problem
- Speed-to-market is critical
- Total cost lower vs. in-house over 1–3 years
- Vendor meets security, SLAs, and compliance
When to PARTNER
- You need unique data, distribution, or credibility
- Co-investment reduces risk or accelerates scale
- You plan a joint roadmap or bundled offer
- You want optionality to later build in-house
Mental model: 4D+R
Decide with 4D+R:
- Differentiation: Does this feature create proprietary advantage?
- Delivery speed: How fast must we ship?
- Dollars (TCO): What is 1–3 year total cost of ownership?
- Dependencies: Do we rely on data, expertise, compliance, or channels?
- Risk: Security, privacy, IP, model drift, and vendor lock-in.
If differentiation is high and timeline allows, build. If speed is crucial and the capability is commoditized, buy. If success hinges on data or channel access, partner.
Step-by-step decision method
Write a crisp problem statement, target users, success metrics (e.g., resolution rate, MAU impact, cost per ticket). Identify constraints (privacy, latency, region, uptime).
Classify the capability as core (differentiating) or non-core (commodity). Note change-rate: how fast the tech or need will evolve.
List Build, Buy, Partner options. For each, collect SLA, security, model performance, adaptability, roadmaps, pricing, and legal terms.
Include engineering time, infra, licenses/API usage, maintenance, monitoring, retraining, QA, vendor fees, integration, and support. Use sensitivity ranges.
Assess privacy, data residency, IP, vendor concentration, model drift, bias, and failure modes. Define mitigations and exit plan.
Run a small pilot with success criteria: accuracy, latency, cost per request, safety metrics, user satisfaction, and operational burden.
Write a one-page decision: chosen path, why now, alternatives considered, TCO/ROI, risks, pilot results, and measurable next milestones.
Worked examples
Example 1: Customer support chatbot for common questions
Need: Deflect 40% of tier-1 tickets within 3 months. Privacy moderate, content public FAQ.
- Build: Custom RAG system + orchestration; 2 engineers for 3 months; ongoing maintenance.
- Buy: SaaS chatbot with FAQ ingestion and analytics; per-conversation fee; fast setup.
- Partner: Co-market with vendor to reach enterprise buyers.
Evaluation:
- Differentiation: Low (FAQ deflection is commodity).
- Delivery speed: High urgency.
- TCO: Buy cheaper for 12 months; build breaks even around 24–30 months if volume high.
- Risk: Vendor lock-in mitigated by exporting content and conversation logs.
Decision: Buy now to hit target; reassess build in 12 months if volume explodes.
Example 2: On-device inference for mobile app personalization
Need: Sub-150ms latency, offline privacy, proprietary on-device signals.
- Build: Fine-tune small model + optimize on-device runtime.
- Buy: Cloud API (latency risk, recurring cost).
- Partner: Chip vendor for optimization and marketing.
Evaluation:
- Differentiation: High (proprietary signals).
- Delivery speed: Medium.
- TCO: Build cost higher upfront, lower variable cost.
- Risk: Model drift manageable with in-app telemetry.
Decision: Build core; partner with chip vendor to optimize and gain distribution.
Example 3: Healthcare document extraction (PHI)
Need: HIPAA-grade compliance, high accuracy on medical forms.
- Build: In-house OCR + domain models; heavy compliance lift.
- Buy: Healthcare-focused vendor with BAA and domain datasets.
- Partner: Co-develop specialty templates with EHR vendor.
Evaluation:
- Differentiation: Moderate.
- Delivery speed: High due to client contract.
- TCO: Buy cheaper short-term; build expensive and slower.
- Risk: Compliance risk high if self-managed; vendor mitigates with audits.
Decision: Buy compliant vendor; partner with EHR for templates and distribution.
Decision checklists
Build checklist
- Clear strategic IP or unique data advantage
- Team capacity and skills confirmed
- Latency/edge or privacy needs not met by vendors
- Exit plan from any interim vendor usage
- TCO over 2–3 years favorable or justified by differentiation
Buy checklist
- Capability is commodity for your market
- Vendor meets security, SLA, and compliance
- Pricing predictable, with usage tiers understood
- APIs portable; data export available
- Pilot hits accuracy, latency, and cost targets
Partner checklist
- Partner offers unique data or channel access
- Aligned incentives (co-selling, revenue share)
- Clear governance and roadmap ownership
- Defined KPIs and termination clauses
- IP ownership and brand guidelines agreed
Common mistakes and self-check
- Chasing novelty: Building non-core features. Self-check: Does this create sustainable advantage?
- Ignoring TCO: Comparing only upfront cost. Self-check: Did you model 1–3 year maintenance and scale?
- Vendor lock-in blindness: No exit plan. Self-check: Can you switch within 4–8 weeks?
- Vague pilots: No pass/fail metrics. Self-check: Are success metrics quantified?
- Compliance gaps: Late security review. Self-check: Did security/legal review the option before pilot?
Exercises
Do these now. Then open the Quick Test when ready.
Use the numbers provided below to compute 3-year TCO and choose Build, Buy, or Partner. Then justify.
Choose a partner structure and define 5 KPIs and a 90-day pilot plan using the scenario below.
Practical projects
- Create a one-page decision doc for a current backlog item using 4D+R and the step method.
- Build a TCO calculator in a spreadsheet with sensitivity inputs (volume, error rate, retraining cadence).
- Draft a vendor evaluation rubric and run a mock RFP on 3 vendors.
- Write a partner term sheet outline with roles, IP, KPIs, and exit clauses.
Who this is for
- AI/ML Product Managers and aspiring PMs
- Founders and tech leads deciding AI investments
- Procurement and strategy collaborators
Prerequisites
- Basic understanding of ML/LLM capabilities and limitations
- Comfort with simple cost modeling and ROI concepts
- Awareness of security/privacy basics (PII/PHI, encryption, data residency)
Learning path
- Start: Problem framing and success metrics
- Then: 4D+R decision method and TCO modeling
- Next: Vendor evaluation and pilot design
- Finally: Partnerships, governance, and exit plans
Mini challenge
Your CEO wants an AI meeting notes feature in 8 weeks to match competitors. Users are SMBs; privacy is important but not regulated. Pick Build, Buy, or Partner and write 3 bullet reasons. Add one risk and mitigation.
Quick Test
Take the Quick Test on this page to check your understanding. Everyone can take it for free; logged-in learners will have progress saved.
Next steps
- Complete the two exercises and the Quick Test
- Apply the method to one real feature in your roadmap
- Review outcomes in 4 weeks and iterate your decision framework