What are the key considerations when designing CPQ for high-volume quote generation (thousands of quotes per day)?

High-volume CPQ deployments require architectural optimisation across the pricing engine, data model, and integration layers:

Reduce Price Rule complexity: The CPQ Quote Line Calculator runs server-side and evaluates all active Price Rules on every save. Minimise the number of active price rules and use efficient conditions. Deactivate rules not needed for specific product families using filter conditions.

Batch pricing calculations: For programmatic quote generation (B2B Commerce → CPQ quotes), use the CPQ Calculate API (SBQQ.QuoteAPI.calculate()) to trigger pricing calculations server-side without user interaction, and control timing to batch multiple line additions before triggering calculation.

Product filter optimisation: Use Product Filter rules with pre-filtered product lists rather than large dynamic queries. Optimise the SOQL behind product filters with indexed fields.

Governor limits at scale: Each CPQ quote save context consumes Apex CPU time. Monitor Apex CPU limits in the CPQ calculator log. Large bundles with many options + complex pricing rules can approach limits. Consider splitting complex bundles into smaller modular bundles.

Async quote generation: For API-driven mass quoting, generate quotes via Queueable Apex with chaining — queue one quote at a time with result callbacks rather than generating all quotes synchronously in one transaction.