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PALOPALO FRAMEWORK

Cost accountability / version 1.3

AI FinOps.

Account for every AI attempt. Reconcile spending, compare model transitions and evaluate when local-learning investment could pay back at acceptable quality and latency.

Local workspaceYour browser. Your records.
No provider connection.

New in 1.3: Local learning compares three frozen configurations offline. Research, evidence limits and reproducibility | End users, IT, administrators and finance.

Untitled FinOps

Empty workspace
Not saved locally

Start with your own workspace, import a snapshot or explore explicitly synthetic records.

01 / Get started

Build a clear account of AI spend.

Set a budget, create a workload and register a route with its rate card. Then reserve an attempt before recording its cost.

Want to see the workflow first?
The synthetic example includes settled costs and an expense awaiting reconciliation.

Budget position

$0.00remaining allowance

Workspace budget$0.00No budget configured
Known cost $0.00Open reservations $0.00Available $0.00
Committed cost$0.00

Known cost + open reservations

Unresolved attempts0

No attempts recorded

Cost / accepted taskNot available

No accepted tasks yet

Accepted / attempted0 / 0

Task-level outcome coverage

Workload allocation

0 workloads
WorkloadKnownReservedRemaining

Known cost breakdown

Recorded amounts

Includes recorded non-token charges. Missing or unreported costs cannot be inferred.

This view accounts for declared records. Estimated comparisons do not establish realised savings. A reference to provider evidence does not independently verify that evidence.

Case File connection

Checking the local Case File...

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