Collecting data is crucial for observability and security, and ensuring it is quickly searchable with low-latency results is essential for managing and protecting applications and infrastructure effectively. However, storing all of this data incurs ongoing storage costs, creating a key opportunity for cost savings. In Elastic Cloud, you can optimize storage expenses by setting up an index lifecycle policy. This policy allows your data to move from the hot data tier — which provides ultra-fast search results with higher storage costs — to the cost-efficient frozen tier — which remains searchable with reasonably quick results.
For instance, storing 90 days’ worth of logs in a deployment with a single hot tier will give you the best performance, as you would expect from Elasticsearch. But in many cases, you don’t need that super fast performance on all data. Sometimes, you just need the first day to be fast; past logs can be just a little slower to retrieve. This approach will significantly reduce your total cost of ownership since the frozen tier can store up to 20 times the amount of data as the hot tier at the same cost.
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