What Is Cloud Spend?

Cloud spend includes the infrastructure costs associated with running cloud data and AI workloads. These costs can include compute, storage, networking, data services, and the resources consumed when queries execute in cloud data platforms.

For enterprise AI, cloud spend is distinct from LLM token consumption. An organization may pay for tokens while an AI system interprets and reasons through a request, then also pay for Snowflake, Databricks, or BigQuery compute when the resulting query reaches the data platform.

• Data-platform compute: The warehouse credits or processing resources consumed when analytics and AI queries run.

• Repeated query activity: Dashboard refreshes, recurring analysis, and repeated requests that can continue to consume cloud compute.

• AI-driven workload growth: Agents can explore data, retry requests, and ask follow-up questions continuously, increasing demand on live data platforms.

Cloud bills show what infrastructure consumed, but they do not always show which governed dataset, model, dashboard, or AI workload caused the work. That makes a warehouse-level spike difficult to investigate and even harder to address with the right architectural change.

A high-cost dataset is not necessarily waste. A widely used business-critical data product may have high but justified compute or memory costs. The useful question is not simply whether the cloud bill is expensive, but which governed asset is driving the spend, why it is being used, and whether its execution mode matches demand.

Cloud spend is a workload decision. Visibility becomes actionable when cost signals are connected to the analytics assets and model decisions behind them.

Strategy Sentinel Cost Intelligence connects cloud data-platform costs to governed analytics assets. When an administrator enables a billing source, Sentinel applies tracking tags to associated data sources and uses runtime context to connect warehouse activity to the Mosaic datasets behind it.

Billing data is collected asynchronously through a daily background process rather than through every interactive query. Sentinel combines billing information with runtime activity to provide dataset-level cost attribution, execution signals, cost trends, potential savings, and optimization recommendations.

A semantic layer provides the governed business context that helps connect infrastructure activity to the datasets and metrics that users and AI systems consume.

Reducing cloud spend does not mean minimizing every query. It means using cloud compute where freshness and scale require it, using memory where repeated use makes it economical, and removing work that no longer delivers value.

• Live execution: Best suited to fast-changing operational data where freshness is critical, though repeated requests continue to consume cloud compute.

• In-memory execution: Well suited to stable, frequently queried datasets because repeated dashboard and AI requests can be served without sending every query back to the cloud platform.

• Refresh and asset optimization: Inactive or over-refreshed assets may consume memory and processing capacity without corresponding business demand.

Strategy Mosaic can cache repeated queries as in-memory cubes, allowing repeated analysis to be served from memory instead of repeatedly consuming warehouse compute.

As analytics and AI usage scales, cloud spend becomes a governance issue as well as a financial one. Without governed business logic, AI systems may reload schemas, generate inefficient queries, and repeatedly consume tokens and cloud compute to rediscover context that could be reusable.

Shared definitions through governed metrics and a metrics layer can help teams make business logic reusable across analytics tools and AI workloads.

Organizations can also explore Governance and trust solutions for approaches to extending governed context, security policies, and consistent definitions across analytics and AI.