Elasticsearch FinOps
Cutting Costs Without Cutting Performance
Elasticsearch costs rarely explode overnight — they’re sneaking up via oversharding, stale indices, and peak-load provisioning you don’t actually need. This post reframes Elasticsearch through a FinOps lens — how to link engineering choices to financial impact, measure cost KPIs, and reclaim 30-50% of wasted spend without losing performance.
3 levers that matter most:
Lifecycle automation (delete or freeze what you don’t query)
Rightsizing (pay for 80% utilization, not 40%)
Cost visibility (make engineers accountable for query cost)
Micro-insight: Your biggest savings won’t come from instance discounts—they come from lifecycle and query discipline visible in cost KPIs.
If your Elasticsearch bill went up 30% this year, which lever would you pull first?
Lifecycle automation seems like the lowest hanging fruit since most teams don't have visibility into their old indices anyway. We've seen similar patterns where engneers keep everything hot by default because cold tier migrations feel risky. The accountability piece is critical though - without cost visibility by query or team, it's hard to make lifecycle rules stick.