Update the budget baseline whenever a scenario change alters what gets processed, which modules run or how AI activity is used. Keep the old assumption visible so the approver can understand why the previous budget no longer applies.
Identify changes worth financial review
Make says non-AI apps generally use one credit per operation, while AI features can vary with connection type and token or other usage factors. Therefore, a blanket assumption that every changed step has the same credit behaviour is unsafe.
As editorial practice, flag changes to module mix, expected bundle volume, branching, scheduling and AI input or output scope. These are investigation prompts, not automatic predictions of extra charges. A change may reduce, increase or leave usage unchanged; evidence should decide.
Maintain a baseline-change log
Create a dated entry containing:
- Scenario identifier and business purpose.
- Previous baseline period and its evidence.
- Change description and approving owner.
- Old and new module mix.
- Old and new expected processing volume.
- AI feature, connection path and relevant usage assumptions.
- Comparison sample and observed credit totals.
- Decision to retain, revise or suspend the baseline.
Separate expected values from measured values. If the comparison uses a different workload, state the difference rather than presenting it as a clean before-and-after test.
For an INR budget, retain the monetary approval separately. A credit variance does not itself establish a rupee variance without a supported commercial basis.
Inspect comparable evidence
Make documents usage views including scenario, per-run, diagram, builder/module and scenario-history levels, alongside broader dashboard and subscription views. Use the documented levels relevant to locating the change rather than relying only on the overall total.
An editorial comparison method is to select a small, defined set of synthetic requests, note their processing characteristics and examine observed usage before and after an authorised change. Do not upload private customer material merely to make the sample realistic.
Evaluate a hypothetical revision
Imagine a fictional Hyderabad team compares two approved versions using 20 synthetic requests. The old sample contains one item per request; the revised design anticipates three items and adds an AI processing step.
The log records 20 versus 60 input items and identifies the added AI feature. It leaves resulting credits blank until inspected. Evaluate both total observed credits and the differences in processed workload; do not multiply the old total by three and call that a forecast.
Stop baseline reuse if the connection path or comparison evidence is unknown. Next, obtain a bounded sample and fresh approval. If access must change, review the AI tools catalog, checking access format and saved terms before buying. Marketplace account, code or token offers are not proof of official subscription equivalence.
Sources and scope
Source material checked 29 October 2026; features/policies may change. Make credits establishes variable credit factors; usage tracking establishes inspection views. The log and comparison method are editorial advice.