Phase 0: The FDE Role & Deployment Mindset · 45 min · Python

Dev vs Delta — The Deployment Boundary

One capability for many customers, or many capabilities for one customer.

What you will learn

Apply the Dev vs Delta architectural boundary to deployment decisions
Determine when custom client work should become a platform feature (the gravel road pattern)
Avoid the anti-patterns: building everything custom (no leverage) or everything generic (no client fit)
Communicate product insights from field work back to the platform team effectively

Objective

What you'll learn

What you'll walk away with:

Apply the Dev vs Delta architectural boundary to deployment decisions
Determine when custom client work should become a platform feature (the gravel road pattern)
Avoid the anti-patterns: building everything custom (no leverage) or everything generic (no client fit)
Communicate product insights from field work back to the platform team effectively

The Problem

Palantir's distinction between "Dev" and "Delta" engineers maps directly onto modern AI teams. Dev engineers build the platform — model APIs, inference infrastructure, SDKs. Delta (forward deployed) engineers take that platform and make it work inside a specific client environment. The boundary between the two is where most AI deployments fail: Dev engineers don't understand client constraints, and Delta engineers don't understand platform internals.

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