GeoRetina AI uses credits to meter analysis work. Credit usage depends on what the system needs to run for the request, not just the words in the prompt. A small single-layer Core request usually costs less than a broad Super report that combines multiple data sources, time periods, and follow-up-ready outputs.
This page explains the main factors that affect credit usage. Exact usage can vary as analysis engines, data sources, and account plans evolve.
Use the app as the source of truth
Use in-app usage and plan information for current credit availability, account limits, and plan gates. Documentation examples are directional and are not a substitute for the app's current credit rules.
What Affects Credit Usage
| Factor | Why it matters |
|---|---|
| Mode | Super mode uses 3x more credits than Core mode because it can perform deeper synthesis, dynamic visualizations, report creation, and follow-up-ready analysis. |
| Region size | Larger regions usually require more data retrieval, processing, summarization, and validation. |
| Analysis type | Different risk and conservation workflows use different data sources and processing steps. |
| Time range | Multi-year or multi-period analyses can cost more than a single-date or current-condition layer. |
| Output depth | Maps and metrics are lighter than report assets, dynamic charts, rich summaries, and presentation-ready outputs. |
| Raster and vector synthesis | Combining raster outputs with parcels, OSM features, asset inventories, or uploaded layers can require additional intersection, attribution, and table generation. |
| Follow-up analyses | Follow-ups that run new calculations, generate new assets, or combine additional layers can consume additional credits. |
Simple Examples
These examples describe relative usage patterns without exposing the internal formula.
Lower usage
A Core land-cover map for one saved ROI with a current-season summary typically uses fewer credits because it returns one map layer and basic metrics.
Higher usage
A Super physical-risk screening report for the same ROI uses more credits because Super is 3x Core and can create deeper synthesis, dynamic visuals, and report assets.
More time periods
A land-cover change analysis from 2018 to 2025 can use more credits than a single-year land-cover map because it compares multiple periods.
Raster plus vector
A physics-based flood-depth analysis that also classifies requested OSM features by exposure can use more credits than the flood layer alone.
How to Control Usage
- Start with Core when you need a quick screening map or metric.
- Use Super when deeper synthesis, dynamic visualizations, or in-depth follow-up analysis are worth the additional credits.
- Keep the region focused on the decision area instead of asking for a very broad geography.
- Request the time range and output type you actually need.
- Run the core layer first, then ask targeted follow-ups instead of combining every possible output in the first request.
- Check Usage & Plan when a workflow is gated by credits, document limits, or analysis limits.