Cube vs. Mosaic for Federal Contract Forecasting and Indirect Rates

No, and this is the single most important thing to understand before evaluating either one. A contractor doing meaningful cost-plus federal work typically needs a timekeeping and accounting system built to DCAA compliance standards, tracking direct labor by contract, indirect cost pools, and an audit trail that a government auditor can trace.

Cube and Mosaic sit above that compliant system of record to help forecast and report, they don't provide the underlying compliant timekeeping and cost accounting infrastructure itself. If you don't have that foundation in place yet, that's the higher-priority gap to close before a forecasting tool choice matters much.

Overhead, fringe, and general and administrative rates get applied to direct costs to determine a contract's fully burdened cost, and those rates are typically calculated annually but should be monitored throughout the year against budget, since a rate that drifts from what was proposed on active contracts can erode margin on cost-reimbursable work. A forecast that applies last year's provisional rates without checking them against current actual indirect costs will misstate margin on every open contract using those rates.

Track provisional versus actual indirect rates throughout the year, not just at year-end true-up, so a drifting rate gets caught and addressed before it compounds across every active contract on the books.

A cost-plus contract's revenue is tied closely to allowable costs incurred, with fee added on top, so revenue and cost move together in a way that a fixed-price contract's revenue doesn't; a fixed-price contract's margin depends entirely on managing cost against a set price, which means cost overruns hit margin directly rather than being reimbursed. A single blended forecasting approach across contract types will misrepresent the real risk profile of a book that mixes cost-plus, fixed-price, and time-and-materials work.

Model each contract type's revenue and margin logic separately, since treating a fixed-price contract like a cost-plus one, or the reverse, produces a forecast that doesn't reflect how the government actually pays for that specific type of work.

Keep these points in the forecast for each contract:

• Forecast cost-plus revenue from allowable costs incurred plus fee, so revenue and cost move together.
• Forecast fixed-price margin against the set price, since cost overruns hit margin directly instead of being reimbursed.
• Apply overhead, fringe, and general and administrative rates to direct costs, and monitor them through the year against what was proposed.
• Keep the DCAA-compliant accounting system as the system of record and forecast on top of it, not in its place.
• Trace every forecast number to contract-level cost, funding, and burn rate so a contracting officer or auditor can follow it.

If your program managers or contracts team already track contract-level cost, funding, and burn rate in a spreadsheet that ties correctly to your compliant accounting system's data, Cube's approach of syncing that spreadsheet keeps the forecasting logic where it's understood and auditable, which matters when a contracting officer or auditor may want to trace a number.

This auditability is worth particular weight in a GovCon setting, since a spreadsheet formula is generally easier to walk a government auditor through than a dashboard's internal calculation, and that trail can matter during an incurred cost submission review.

Once you're managing enough active contracts that consolidating funding status, burn rate, and indirect rate application across all of them in a spreadsheet becomes unwieldy, a dashboard rolling that up can help program and finance leadership see the full portfolio. Confirm in a demo that Mosaic can carry your specific indirect rate structure and contract-type-specific revenue logic accurately, since a generic revenue recognition template is unlikely to match federal contract accounting out of the box.

Also confirm how the platform handles a contract that changes type partway through its life, for example a fixed-price contract with a later cost-reimbursable modification, since that kind of change is common enough on federal work that a rigid template built for only one contract type will need a manual workaround.

Jirav's driver-based approach is useful when you're pursuing new contract awards and need to model what winning them actually requires in cleared personnel, facility, and indirect cost pool capacity, since staffing a federal contract, particularly one requiring security clearances, involves lead time and constraints that a flat headcount assumption misses entirely. A contractor that wins an award before confirming it can actually staff the cleared positions on time risks a real performance problem in the first months of the contract, well before the forecasting tool choice matters at all.