The real question isn't build vs. buy
Most teams frame this as a binary choice: use an off-the-shelf AI platform, or build a custom system from scratch. In practice, the right answer is almost always a spectrum — and the decision hinges less on cost and more on how central the AI capability is to your competitive advantage.
If the capability is commodity — summarization, basic classification, generic chat — buying is usually correct. If the capability is the product, or touches proprietary data in a way competitors can't replicate, custom investment tends to pay for itself quickly.
A simple framework for the decision
Ask three questions: Does this capability sit on the critical path of your product's differentiation? Do you have (or can you get) proprietary data that materially improves outcomes over a generic model? And will usage scale to a point where per-call platform pricing becomes a structural cost disadvantage?
Two or more 'yes' answers is a strong signal to invest in a custom-built or hybrid system — typically a fine-tuned or RAG-augmented pipeline built on top of foundation models rather than a fully bespoke model.
The hybrid path most teams should take
In our experience, the highest-leverage approach for most product teams isn't full build or full buy — it's building the orchestration, retrieval, and evaluation layer in-house while leveraging foundation models as a component, not the whole system.
This gives you ownership of the parts that matter (your data pipeline, your evaluation harness, your prompt and retrieval logic) while avoiding the multi-year cost of training foundation models from scratch.
Marcus Ortega
VP of AI & Data Engineering