For high-query-volume BI dashboards, the most cost-effective setup is usually:

1) Use a cloud-managed columnar warehouse or OLAP engine

If you want the best balance of low ops + good performance + predictable cost, start with:

• BigQuery
• Snowflake
• Redshift RA3 / Redshift Serverless
• ClickHouse Cloud
• Apache Doris / StarRocks if you’re self-managing or on a managed service

Why this is cost-effective

• Columnar storage is ideal for dashboard queries that scan a few columns over lots of rows.
• These systems compress well and minimize I/O.
• Managed services reduce engineering overhead, which is often the real cost driver.

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2) Add a pre-aggregation layer for dashboards

The biggest cost savings usually come from not querying raw fact tables repeatedly.

Use:

• materialized views
• summary tables
• rollups / aggregates by day, region, product, tenant, etc.
• cube layers like dbt aggregates, Apache Druid-style rollups, or semantic layers

Best practice

Design dashboards so most queries hit:

• daily/hourly aggregates
• top-N dimensions
• precomputed metrics

This can cut compute costs by 10x–100x compared with ad hoc raw scans.

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3) Partition and cluster by access patterns

For a columnar DB, make sure tables are organized for common filters:

• partition by date
• cluster/sort by tenant_id, customer_id, region, event_type, etc.

This reduces scanned data and improves cache hit rates.

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4) Cache aggressively

If dashboards are repeatedly queried with similar filters:

• use query result caching
• application-side caching
• Redis/memory cache
• BI tool cache if available

For highly repeated dashboard views, caching can be the cheapest “compute.”

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5) Separate interactive BI from raw analytics

A very cost-effective architecture is:

• Raw data lake / warehouse for ingestion and occasional deep analysis
• Serving OLAP store for dashboard reads

Examples:

• S3 + Parquet + Spark/dbt for ETL
• ClickHouse/Doris/Redshift for dashboard serving

This avoids making your main warehouse do all the expensive high-concurrency read work.

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6) Scale with concurrency-efficient engines

If you expect many simultaneous dashboard users, prefer engines that handle concurrency well:

• ClickHouse
• Druid
• Doris/StarRocks
• BigQuery (good for bursty workloads)
• Snowflake with appropriately sized warehouses and caching

Traditional MPP warehouses can get expensive if you keep scaling clusters just to handle concurrency.

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A practical “most cost-effective” pattern

If I had to pick a general-purpose cost-effective approach:

For most teams

ClickHouse + pre-aggregated tables + caching

• very strong price/performance
• excellent for dashboard-style queries
• often cheaper than general-purpose warehouses at high read volume

For low-ops managed option

BigQuery or Snowflake + materialized views + BI cache

• simplest to run
• usually higher query cost than ClickHouse at extreme dashboard concurrency, but lower engineering cost

For very high concurrency and time-series/event dashboards

Druid / StarRocks / Doris / ClickHouse

• optimized for fast aggregations and many concurrent reads

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Rule of thumb

• If you have lots of repeated dashboard queries: invest in pre-aggregation and caching
• If you have massive ad hoc scans: use a managed warehouse
• If you need the best cost per query at high volume: often ClickHouse-like OLAP wins

If you want, I can give you a recommendation by cloud/provider, or a reference architecture for a BI stack with estimated costs.