> ## Documentation Index > Fetch the complete documentation index at: https://docs.activeviam.com/llms.txt > Use this file to discover all available pages before exploring further. # How aggregate providers can speed query time > How Atoti aggregate providers control pre-aggregation strategy, covering the JIT provider for on-demand aggregation, leaf provider for pre-aggregated indexes, and bitmap provider for high-concurrency workloads. Aggregating data at query time can be slow because the system must fetch data from the datastore or an external database (DirectQuery). Aggregate providers allow pre-aggregation, so queries retrieve results from optimized structures instead of recalculating from scratch. Aggregate providers define the strategy for aggregating measures in a cube. They determine whether data is aggregated on demand or pre-aggregated during data loading. This choice directly impacts query speed, memory usage and transaction speed. ## What are the types of aggregate providers and their impact? ### Just-in-time (JIT) provider * **Behavior:** Aggregates data at query time. * **Memory usage for storage:** None. * **Memory usage at query time:** Higher than other aggregate providers * **Query speed:** Slower. * **Use case:** Small datasets or infrequent queries. * **Impact:** No pre-aggregation; queries depend on datastore scans. ### Leaf provider * **Behavior:** Pre-aggregates data during loading at leaf level. * **Memory usage for storage:** Moderate. * **Query speed:** Fast. * **Memory usage at query time:** Moderate. * **Use case:** Balanced performance for most cubes. * **Impact:** Queries retrieve pre-aggregated values using point indexes, reducing computation time. ### Bitmap provider A bitmap aggregate provider pre-aggregates data using a leaf provider. They use a bitmap index for faster data retrieval. * **Behavior:** Pre-aggregates data and builds bitmap indexes. * **Memory usage for storage:** Higher than a leaf provider. * **Query speed:** Fastest. * **Memory usage at query time:** Lower than JIT or leaf. * **Use case:** High query concurrency and large datasets. * **Impact:** Queries use bitmap submasks for instant retrieval of aggregated values. performance-agg-provs.png ## Related reading Find out more about aggregate providers by following the links below: * [Atoti Java SDK](/engine/java-sdk/latest/cube/providers/aggregate_provider) * [Atoti Python SDK](https://docs.activeviam.com/products/atoti/python-sdk/latest/api/atoti.aggregate_provider.aggregate_provider.html#atoti-aggregateprovider)