Build vs. Buy: AI Infrastructure for Proptech Companies
When to use managed AI services vs. building your own pipelines, vector stores, and evaluation harnesses.
Bart Korpershoek
2 May 2026 · 7 min read
Proptech founders face build-vs-buy decisions on every AI layer: embeddings, vector DB, orchestration, evaluation, and observability. Buying too early creates vendor lock-in; building too early burns runway.
Buy when speed and compliance matter
Managed embedding APIs, hosted vector search, and LLM gateways with EU data residency are appropriate for MVPs and pilots. You optimize time-to-learning, not cost-at-scale.
Build when differentiation is the model workflow
If your moat is domain-specific valuation, matching, or risk models, invest in custom feature pipelines, evaluation datasets, and retraining automation. Infrastructure can stay managed; the workflow is yours.
Hybrid is the default in 2026
Most clients we see use managed inference plus owned orchestration code in TypeScript or Python, with observability tied to existing Datadog or Grafana stacks. Revisit the decision every six months as volume grows.
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