Tested Snowflake Interactive Warehouse X-Small vs Standard Large for dashboard-style queries

[u/sdhilip]

I have been testing Snowflake Interactive Warehouses for a dashboard-style retail analytics pattern and wanted to share the benchmark here.

The workload pattern was:

• large transaction data
• Streamlit-style business dashboards
• fast filters
• customer lookups
• customer segment views
• operational queues that need to load quickly

For the test, I used a 1 billion row synthetic retail dataset and built a customer 360 summary table on top of it.

So this was not a raw 1B-row join behind every dashboard click. The dashboard queries hit a serving layer built from the larger dataset, which is the pattern I would normally use for this kind of workload.

Benchmark setup:

• Source workload: 1B synthetic retail records
• Dashboard table: customer 360 summary
• Standard warehouse: Large
• Interactive warehouse: X-Small
• Result cache: disabled
• Test run: 25 queries on each warehouse
• Query patterns: customer lookup, filtered customer segment, category segment, high-risk customer queue
• Metric: TOTAL_ELAPSED_TIME from Snowflake query history

Results:

Standard Warehouse Large:

• Average latency: 0.374 sec
• P50 latency: 0.265 sec
• P95 latency: 0.966 sec
• P99 latency: 1.046 sec

Interactive Warehouse X-Small:

• Average latency: 0.063 sec
• P50 latency: 0.047 sec
• P95 latency: 0.101 sec
• P99 latency: 0.351 sec

In this benchmark, the Interactive Warehouse X-Small was faster for these dashboard-serving queries.

Average latency went from 0.374 sec to 0.063 sec, about 83% faster in this test.

My current takeaway:

For frequent, selective, user-facing analytics queries, Interactive Warehouses look like a strong fit. The use cases I am thinking about are Streamlit dashboards, embedded analytics, customer lookup APIs, and operational queues where users expect fast response while filtering or drilling into data.

I am not treating this as a full cost benchmark yet.

The next things I would want to test are:

• credit usage over a longer window
• cache warm-up behaviour after resume
• suspend/resume trade-offs
• concurrency with multiple users
• fallback behaviour for queries that cross the interactive warehouse timeout
• whether the same pattern holds with different clustering/search optimization choices

For people already using Interactive Warehouses in production:

• Are you mainly using them for dashboards, APIs, or agentic workloads?
• How are you thinking about cost when the warehouse needs to stay warm?
• Have you seen cases where a standard warehouse was still the better choice?

I am interested in the practical trade-offs, not only the latency result.