To say "Cast AI saved you money," Cast AI needs a reference point: what would this cluster have cost, and how would it have looked, without Cast AI? That reference point is the baseline, and it's the foundation every number in the Savings Report is measured against.

What a baseline is

The baseline isn't a single dollar figure — it's a small model of your cluster's behavior before Cast AI's optimizations took effect, made up of four numbers plus a time window:

1. CPU overprovisioning factor — how much more CPU was provisioned than workloads actually requested.
2. RAM overprovisioning factor — the same, for memory.
3. CPU unit cost — the effective price you paid per CPU core, per hour.
4. RAM unit cost — the effective price you paid per GiB of RAM, per hour.

These four numbers are measured over a baseline period — a historical window representing your cluster before Cast AI optimization. Once captured, the model stays fixed and is reapplied going forward to reconstruct what your projected (no-Cast-AI) cost would be on any given day.

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Note

Why a model, and not just an old bill? Clusters grow and shrink over time, so comparing today's bill to a fixed dollar amount from months ago wouldn't be fair. Instead, the baseline captures ratios and rates — how much you tended to overprovision, and at what price — and reapplies those ratios to today's actual workload demand. That keeps the comparison fair as your cluster scales up or down.

What the numbers mean in practice

These four values are averaged across your entire baseline period and applied uniformly — they don't currently distinguish between on-demand, spot, and fallback capacity.

How your baseline is determined

Cast AI computes baselines automatically, once per day, for every eligible cluster. It tries three methods in order, from most to least specific to your cluster, and uses the first one that has enough data:

1. Your cluster's own history      →  most reliable
2. Other clusters in your org      →  fallback
3. Cast AI's broader fleet average →  last resort

A cluster only moves to the next method if the previous one didn't have enough data to compute a baseline. Once a cluster gets a baseline from an earlier method, it's never re-evaluated against a later one.

Method 1: Your cluster's own history

This is the highest-quality baseline, because it's built entirely from your cluster's own pre-optimization behavior. Cast AI looks at the period between when your cluster first connected and when Cast AI took over active node management (specifically, the point at which more than 20% of your nodes became Cast AI–managed), and calculates the four parameters from your cluster's actual metrics during that window.

Your cluster qualifies for this method only if there's at least 7 days of history between cluster creation and the point Cast AI took over. Without a week of pre-optimization data, the cluster falls through to method 2.

In the report and API, this baseline type is labeled Cluster history.

Method 2: Other clusters in your organization

If your cluster doesn't have enough of its own history, Cast AI looks for other clusters in your organization that already have a Cluster history baseline, and averages their overprovisioning factors and unit costs. The baseline window becomes the combined span of those peer clusters' own baseline windows.

The assumption here is that clusters within the same organization tend to share infrastructure patterns and pricing, making a sibling cluster's measured behavior a reasonable stand-in for yours.

In the report and API, this baseline type is labeled Peer clusters.

Method 3: Industry average

If your cluster has no usable history of its own, and no eligible peers in your organization, Cast AI falls back to averaging overprovisioning and cost across all clusters observed on the fleet, on the single day your cluster connected to Cast AI's active management. This is a genuine cross-fleet average — it isn't limited to your organization.

In the report and API, this baseline type is labeled Industry average. Think of it as "we didn't have anything more specific to compare you against."

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Note

An industry-average baseline is the least specific of the three, since it isn't based on your own workloads or infrastructure. If your Savings Report is using an industry-average baseline and the numbers look off, contact your Cast AI representative about a manual baseline review.

Baseline quality, at a glance

When a baseline can't be computed yet

A cluster needs to be at least 14 days old, and Cast AI needs to be actively managing it, before a baseline is computed at all. If your cluster is younger than that, or hasn't yet reached active management, it simply won't appear in the Savings Report yet — not with zero savings, but absent entirely. This is intentional: it avoids showing noisy, unstable savings numbers for clusters that just connected.

Once your cluster clears both thresholds, Cast AI computes a baseline on its next daily run, and the cluster starts appearing in your report.

Manual baseline adjustments

Sometimes the modeled baseline doesn't reflect reality — for example, if you have out-of-band knowledge of your actual pre-Cast-AI costs, or the automatic calculation landed on a window that wasn't representative. Two adjustments are available through your Cast AI representative:

• Override the baseline — hand-set any of the four parameters directly. Fields you don't specify keep their current values. Once a baseline is overridden, Cast AI's automatic daily calculation will never touch that cluster again, so the override sticks until it's changed manually.
• Recalculate over a custom date range — re-run one of the three calculation methods above, but over a date range you choose instead of the one Cast AI selected automatically.

Both baseline types are labeled Overridden in the report once applied, so you can always tell when a baseline reflects a manual adjustment rather than the automatic calculation.

Related resources

• Savings Report overview — what the Savings Report shows and how autoscaler and workload autoscaler savings differ.
• Savings report calculations — the exact formulas that turn your baseline into a projected cost and a savings number.
• Organization-level savings report
• Cluster-level savings report