Sizing depends on query volume, data size and concurrency.

Start by benchmarking typical queries against your warehouse.

If you’re using a cloud data warehouse, choose the appropriate cluster size and autoscaling policies.

On the analytics side, allocate compute for rendering and caching; some vendors provide fully managed back ends.

As your user base grows, monitor CPU, memory and query latency to scale pods or containers accordingly.

Sharding tenants and isolating heavy users can help.

MageMetrics runs serverless for metadata and caching, so customers only need to ensure that their warehouse can handle the query load; scaling is handled automatically.