We’re evaluating whether to migrate our quote-configure-price module from on-premise AEC 2021 to cloud hosting. Our sales team generates 200-300 complex quotes daily with multi-tier pricing rules and product configurations.
I’ve been running informal benchmarks and seeing concerning latency differences:
- On-premise: Quote generation averages 1.2-1.8 seconds
- Cloud sandbox: Same quotes taking 2.8-4.5 seconds
The API Gateway logs show most of the delay is in the pricing calculation API calls. Our on-premise setup has dedicated hardware (32GB RAM, SSD storage) while cloud is on standard compute instances.
Before we commit to cloud migration, I’d love to hear real-world experiences:
- What API latency benchmarks have others seen for quote generation in cloud vs on-premise?
- Does cloud auto-scaling actually help during peak quote periods or just add overhead?
- Has anyone successfully maintained quote performance after cloud migration, especially with legacy integrations to ERP systems?
Our sales team is very sensitive to quote speed - anything over 3 seconds gets complaints. Need to understand if cloud performance can match our current on-premise setup.
I managed a similar migration last year for a company with 500+ daily quotes. The auto-scaling question is critical - it DOES help, but only if configured correctly. We set scaling triggers based on API Gateway queue depth, not just CPU usage. During our peak hours (9-11am), cloud auto-scales from 4 to 12 instances and quote latency stays under 2 seconds. Without proper scaling policies, you’re right that it just adds overhead because instances spin up too slowly.
Your benchmarks align with what we saw initially. Cloud standard instances have more network hops and shared infrastructure overhead. However, our latency improved dramatically after we moved to dedicated cloud instances and enabled regional API caching. Went from 4.2s average down to 1.5s. The key is proper cloud architecture, not just lift-and-shift.
Smart caching is the key - you don’t cache final quote results, you cache the product catalog data and pricing rule metadata that rarely changes. In our implementation, we cache product configs for 5 minutes and pricing rules for 15 minutes at the API Gateway level. The actual price calculation still happens real-time but with cached reference data. This cut our database round-trips from 15-20 per quote down to 3-4, reducing latency by 60% without any stale data risk.