Gokul Rajaram recently shared a post I found insightful. While intended at startup leaders, it is broadly applicable to anyone interesting in building technology products as, simple mental model aside, the central message is “be clear why you exist and measure what matters.”
Sharing in full below – thank you for sharing, Gokul.
Every startup needs to make a choice: is their product a dashboard product or a pipes product?
Dashboard products are used directly and regularly by end users as their primary interface for accomplishing tasks. The goal for these products is to get customers to live in the product. The primary North Star metric for these companies is active users (daily / weekly / monthly, depending on the natural frequency of customer usage for the category). Facebook’s first product (aka Facebook :)) was a dashboard product.
Pipes products are used in the background to process transactions, data, payments, etc, and customers rarely interact with them directly after initial setup. The goal for these products is to for their customers to send as much of their data / payments / etc through them. Their North Star metrics is a volume metric (eg GPV). Databricks’ core product is a pipes product.
Companies can have both types of products in their portfolio. For example, ChatGPT is a dashboard product while OpenAI’s APIs are a pipes product. However, a given product has to determine which camp it’s primarily in.
This choice dictates product development, growth strategy, and org structure. For example, dashboard products require heavy investment in UI/UX polish, engagement features, and retention loops, while pipes products prioritize reliability, throughput, integration breadth, and seamless embedding into customer workflows. Dashboard products have consumer-style growth teams focused on activation and habit formation to grow [DWM]AUs, while pipes products focus on making their product invisible infrastructure that “just works” and on capturing more and more of their end customers’ volume.
Most teams fail by mixing the two too early — chasing DAUs while selling pipes, or overbuilding infra for a dashboard.
Clarity on where value accrues should come before features, metrics, or hiring.
A Learning a Day 