palantir-cost-tuning

name	palantir-cost-tuning
description	Optimize Palantir Foundry costs through compute tuning, incremental builds, and usage monitoring. Use when analyzing Foundry compute costs, reducing API usage, or implementing cost monitoring for Foundry workloads. Trigger with phrases like "palantir cost", "foundry billing", "reduce foundry costs", "foundry pricing", "foundry expensive".
allowed-tools	Read, Grep
version	1.5.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","palantir","foundry","cost","optimization"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

Palantir Cost Tuning

Overview

Optimize Foundry compute and API costs through incremental transforms, right-sized Spark profiles, efficient pagination, and usage monitoring.

Prerequisites

• Active Foundry enrollment with build history
• Access to Foundry resource usage metrics
• Understanding of transform build patterns

Instructions

Step 1: Cost Drivers in Foundry

Cost Category	Driver	Optimization
Compute	Full rebuilds of large transforms	Use @incremental()
Compute	Oversized Spark profiles	Right-size @configure profiles
Storage	Redundant dataset snapshots	Configure retention policies
API	High-frequency polling	Use webhooks instead
API	Small page sizes	Use max page_size (500)

Step 2: Convert Full Rebuilds to Incremental

from transforms.api import transform_df, Input, Output, incremental

# BEFORE: Full rebuild every run (expensive for large datasets)
@transform_df(Output("/out"), data=Input("/in"))
def expensive(data):
    return data.filter(data.status == "active")

# AFTER: Only processes new/changed rows
@incremental()
@transform_df(Output("/out"), data=Input("/in"))
def cheap(data):
     data.(data.status == )