Customer Usage & Capacity Data

What these pipelines support

Distinct from billing (which is transactional and customer-facing), the usage and capacity pipelines support internal decisions:

• How much GPU capacity to procure next quarter.
• Which regions to expand into.
• Which customer segments to invest in.
• What pricing changes to consider.
• Which existing reservations are under- or over-utilized.
• How customer behavior is evolving.

Utilization marts

The utilization mart is the analytics-friendly view of "what's running, what's idle":

• Fact: instance-hours. One row per GPU per hour with state (busy/idle/maintenance) and customer.
• Dimensions: Customer, region, SKU, instance class, contract type.
• Aggregations: Utilization percentage by SKU per region per day; by customer per month; by contract.

This mart is the substrate for fleet-level decisions. "How much B200 capacity is idle at any given moment?" should be a one-line SQL query.

Capacity planning

The capacity-planning pipelines blend utilization history with forward forecasts:

• Committed capacity model. What capacity is committed to which customers across what time periods. From contracts and reservations.
• Demand forecast. What additional demand to expect from existing customers (growth) and new ones (sales pipeline).
• Supply roadmap. When new GPUs come online, when datacenter buildouts complete.
• Capacity gap. Where forecast demand exceeds supply (need to procure / build) and where it's below (idle / writedown risk).

The DE function builds the data layer; product / finance / leadership consume the dashboards. Some neoclouds invest substantial DS effort in the forecasting models (covered in the DS guide); the DE provides the data they need.

Commitment tracking

Reserved-capacity customers commit to GPU-hours over multi-year terms. Tracking commitments is critical:

• Are customers drawing down their commitments at the expected pace?
• Are reservations approaching expiry without renewal discussions?
• Are commitments under-utilized (red flag for renewal)?
• Are commitments over-utilized (selling more than committed; opportunity for upsell)?

The DE team owns the commitment fact table joining contracts (from CRM / contract management) with usage (from billing / telemetry). Account managers use the resulting dashboards to manage renewals.

Customer cohorts

Cohort analysis is standard SaaS analytics applied to GPU customers:

• Acquisition cohort: when the customer signed up.
• Workload cohort: training-heavy vs inference-heavy vs mixed.
• Size cohort: mid-market vs enterprise.
• Geography cohort: regional distribution of usage.

Cohort marts let product and growth teams ask "are this quarter's signups behaving differently from last quarter's?" The answer often drives marketing and product investment.

Takeaway

Customer usage and capacity data is the engine of strategic decisions inside a neocloud. The DE pipelines that surface utilization, commitments, and cohorts let leadership reason quantitatively about growth. The next chapter examines marketplace event streams — relevant for the marketplace-flavored neoclouds.