Elasticsearch Cost Optimization
Cut your Elasticsearch bill: reduce over-replicated indices, add ILM delete phases, fix over-sharding, right-size nodes. Savings verified on the real bill.
What OhChimp optimizes for Elasticsearch
Some of the cost levers OhChimp checks for Elasticsearch. Each one becomes a reviewable plan you approve before anything changes.
Reduce over-replicated indices
Every extra replica is a full copy of your data sitting on disk and in heap. OhChimp reads index settings to find indices running more than one replica, then drafts a plan to cut them back to one. Going from two replicas to one trims that index's storage by roughly a third with minimal availability impact.
Add a delete phase to ILM policies
An Index Lifecycle Management policy with no delete phase lets data grow forever, and you keep paying to store indices long after they stop mattering. OhChimp audits your ILM policies across the hot, warm, cold, and delete tiers and flags the ones missing a delete phase so retention actually bounds your storage.
Reclaim space from deleted docs
Indices with a high deleted-document ratio carry dead weight in every segment. OhChimp reads per-index store size and deleted-doc ratio to spot the worst offenders, then plans a force-merge to reclaim that space. It also surfaces your largest indices as archive candidates.
Fix over-sharding
Too many small shards burn heap memory and add coordination overhead, which pushes you toward bigger nodes than the data needs. OhChimp reads shard counts per node and flags over-sharded clusters so you can consolidate shards and run leaner.
Right-size nodes from heap, CPU, and disk
Nodes provisioned well above their real heap, CPU, and disk usage are paying for headroom you never touch. OhChimp reads per-node JVM heap, CPU, and filesystem stats, and also checks fielddata cache size, to point you toward smaller instances or a switch to doc_values that lets you downsize.
Force-merge bloated read-only indices
Read-only indices with hundreds of segments waste heap and slow queries. OhChimp reads segment counts and merge overhead per index, then plans a force-merge down to one segment for the indices that have stopped being written.
Trim snapshot storage and egress
Snapshots piling up in S3 or GCS quietly run up storage and egress charges. OhChimp reads your snapshot repositories and counts snapshots and backed-up shards so you can tighten retention and stop paying for backups you'll never restore.
Elasticsearch cost optimization FAQ
How does OhChimp connect to Elasticsearch?
It connects to your cluster using either your hosts list or a cloud_id, authenticated with an API key or a monitor-role user. It runs read-only calls against cluster health, index and node stats, settings, mappings, ILM policies, and snapshot repositories. It works with self-managed clusters and Elastic Cloud.
Does OhChimp need write access or see my data?
No. OhChimp uses a read-only monitor-role connection and reads metadata and statistics only: index sizes, shard counts, replica settings, node heap and CPU, ILM policies, and snapshot repositories. It never stores your documents, secrets, or workload contents.
What Elasticsearch costs can OhChimp actually reduce?
The main ones are over-replicated indices wasting storage, ILM policies with no delete phase that let data grow unbounded, over-sharded nodes burning heap, oversized nodes with idle heap and CPU, bloated read-only indices that need a force-merge, and snapshot retention running up storage and egress.
Who applies the changes, and can they be rolled back?
You do. Each fix is a reviewable plan with a confidence score, a risk level, and rollback steps. Nothing changes until you click apply, and OhChimp applies the matching code and infrastructure changes together. You are always the one who clicks.
How are Elasticsearch savings verified, and what does it cost?
Against your real bill. A plan is marked VERIFIED only after 7 or more days, a drop of at least 10%, and 3 consecutive positive checks. Until then it stays flagged as not implemented. Pricing is a flat monthly fee with no cut of your savings, and paid plans carry a year-one ROI guarantee: a full refund of subscription fees if it does not pay for itself in the first 12 months.
Related integrations
Teams running Elasticsearch usually run these too. OhChimp finds the waste in each and proves it on the bill.
Cassandra
Node sizing, replication, and storage
Redis
Keyspace, TTLs, eviction, and memory
ClickHouse
Query cost, compression, and storage