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How to accurately track Vertex AI spend using GCP billing exports

This article details how to accurately track spending on Google Cloud's Vertex AI by analyzing billing export data. It highlights the importance of using the detailed usage cost export, `gcp_billing_export_resource_v1`, which includes crucial resource-level information. The guide explains common pitfalls, such as undercounting spend by filtering solely on SKU descriptions and misinterpreting the 'cost' column without accounting for credits, committed use discounts, or promotional grants. It provides specific BigQuery SQL queries to correctly calculate net costs and attribute spending to individual models based on SKU descriptions. AI

IMPACT Provides guidance for organizations to better manage and understand their AI model operational costs on Google Cloud.

RANK_REASON Article provides a technical guide on using a specific cloud service's billing export feature.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

How to accurately track Vertex AI spend using GCP billing exports

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Article provides a technical guide on using a specific cloud service's billing export feature.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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infra, product
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High
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45 days old
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  1. dev.to — LLM tag TIER_1 English(EN) · Multigrid ·

    Reading a GCP Billing Export to Find Vertex AI Spend by SKU

    <p>The BigQuery billing export is the only place on Google Cloud where you can see model spend at SKU granularity. The schema is straightforward and there are exactly two traps in it, both of which produce a query that runs, returns numbers, and is wrong.</p> <h2> The right table…