How to Decide: Build, Buy, or Partner for Your Next AI Product?
Every AI product team faces the same critical decision at some point: Should we build it ourselves, buy an existing solution, or partner with someone who already has it?
It’s not just a technical choice—it’s a strategic one that shapes your speed, cost, risk, and long-term differentiation. In AI, this question matters even more because the landscape changes fast, and what seems like a smart “build” today could be obsolete in six months.
Here’s how to make that call intelligently as an AI product manager.
Step 1 Clarify Your Core Strategy
Before diving into cost or timelines, start with a strategic question:
Is this AI capability central to our product’s value or just a supporting function?
If it’s core (the thing users pay for or that defines your differentiation), you may need to build.
If it’s supporting (a back-office feature or a non-core enhancement), buying or partnering might be smarter.
Example:
If you’re building a financial assistant app, your recommendation engine is core. Your document OCR pipeline probably isn’t.
Step 2 Compare Time-to-Market vs Customization
AI moves fast. Sometimes, getting to market early matters more than owning every detail.
Option / Speed / Customization / Best For
Build: Speed is Slowest, Customization is Highest, and Best For Proprietary tech, deep integration, competitive edge
Buy: Speed is Fastest, Customization is Lowest, and Best for MVPs, proof-of-concept, non-core AI features
Partner: Speed is Medium, Customization is Moderate, Best for Complex projects requiring shared expertise or domain data
PM Tip: Ask, “Do we need a perfect model, or do we need real feedback from users right now?” Early feedback often beats early perfection.
Step 3 Assess Data Ownership and Control
Data is the fuel of every AI product. Decide how much control you’re willing to trade for speed.
Build: You own the data, the model, and the IP. Highest control, highest cost.
Buy: You rely on an external API or vendor’s model. Fast, but less control and visibility.
Partner: Shared data ownership—offers flexibility but requires legal and compliance alignment.
If data privacy, bias, or regulation are big concerns, control matters more than convenience.
Step 4 Evaluate Long-Term Differentiation
Ask yourself: In three years, will owning this capability set us apart, or will everyone have it?
If it’s a commodity (like basic summarization or speech-to-text), buy it.
If it’s a strategic moat (like domain-specific predictions or proprietary knowledge), build or co-develop it.
Think like an investor—what gives you durable advantage over time?
Step 5 Factor in Risk and Maintenance
AI systems don’t just launch—they live. Models drift, data pipelines break, APIs change pricing overnight.
Build: You control the roadmap but also own the maintenance risk.
Buy: You offload risk but depend on vendor reliability and cost changes.
Partner: You share both the risk and the reward.
A good rule of thumb: if your team doesn’t have the bandwidth to retrain, monitor, and maintain, buying or partnering might save you from future chaos.
Real-World Examples
Build: Tesla built its own autonomous driving models—it’s their competitive edge.
Buy: Many startups use OpenAI APIs for natural language processing to speed up MVPs.
Partner: Shopify and Google Cloud co-develop AI-powered commerce tools, blending expertise and infrastructure.
Final Thought
There’s no one-size-fits-all answer. The smartest AI PMs make the build–buy–partner decision not once, but at each stage of maturity.
Start by buying or partnering to validate value fast.
Then, build when the problem—and opportunity—are proven.
AI is evolving too fast to rely on pride or assumptions. The right decision is the one that keeps you learning, shipping, and scaling responsibly.