Build vs. buy AI: the decision framework we use

The Default Should Be Buy

Our baseline advice to clients is simple: if a SaaS product exists that solves your problem well enough, buy it. Building custom AI infrastructure is expensive, hard to maintain, and distracts from your core business.

When You Should Build

However, there are three scenarios where building custom AI is not just justifiable, but necessary for survival:

1. Core Differentiator: If the AI system is the primary reason customers choose your product over competitors, you cannot outsource it. You must own the IP, the model, and the data pipeline.
2. Proprietary Data Advantage: If you have a unique dataset that no vendor has access to, generic models will never match the performance of a model fine-tuned on your specific domain.
3. Security and Compliance Strictness: Sometimes, the risk of sending sensitive data to third-party APIs is unacceptable due to regulatory or contractual constraints. In these cases, deploying open-source models within your own VPC is required.

If your use case doesn't fit into one of these three buckets, you're likely better off paying a monthly subscription to an existing vendor.