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Why PLG Products Should Build a Usage-Based Data Warehouse
Description
In this episode of Product-Led Growth with Fexingo, Lucas and Luna dive into why more PLG companies are building their own usage-based data warehouses instead of relying on standard analytics tools. They look at the case of a mid-market SaaS company that saved $1.2 million annually by replacing a third-party event pipeline with a custom warehouse built on open-source columnar storage. The conversation covers the trade-offs between speed of deployment and long-term cost control, the specific metrics that matter for PLG funnels, and why self-serve data infrastructure can actually accelerate product iteration. Lucas argues that for any PLG product processing over 100 million events per month, the math increasingly favors building in-house. Luna pushes back on the engineering lift and maintenance burden. They also discuss how a well-structured data warehouse enables real-time cohort analysis without the per-query costs of cloud-native tools. If you are building a product-led business and wondering when to invest in your own data layer, this episode offers a concrete framework and a real-world number to start from.