Cassandra Cost Optimization
OhChimp probes Cassandra for over-replicated keyspaces, oversized partitions, and MV overhead. You press apply, savings verified on your real bill.
What OhChimp optimizes for Cassandra
Some of the cost levers OhChimp checks for Cassandra. Each one becomes a reviewable plan you approve before anything changes.
Trim over-replicated keyspaces
Replication factor multiplies your storage cost linearly, so an RF of 4 or 5 stores every byte one or two extra times. OhChimp reads the replication settings on every keyspace, flags any with RF greater than 3, and drafts the change with rollback steps so you stop paying to store copies you don't need.
Split oversized partitions
Partitions over 100MB pile on GC pressure and compaction overhead, which can push you to bigger or more nodes. OhChimp estimates mean partition size per table from system.size_estimates, flags the hotspots, and plans a partition-key change (such as adding a bucket column) to bring them back in line.
Fix tombstone and GC pressure
Tables with a TTL and a high gc_grace_seconds accumulate tombstones, and tombstone-heavy reads cause JVM GC pauses that can force a larger heap or extra nodes. OhChimp finds tables where TTL is set and gc_grace_seconds runs over a day, then drafts a lower gc_grace or a compaction switch to cut the read amplification.
Match compaction strategy to the workload
SizeTieredCompactionStrategy on a read-heavy table burns extra I/O, the kind that quietly grows your node footprint. OhChimp maps the compaction strategy on every table and the cluster-wide distribution, then plans a switch (STCS for write-heavy, LCS for read-heavy) so compaction stops doing avoidable work.
Cut materialized view overhead
Each materialized view doubles the write cost of its base table and adds its own storage on top. OhChimp lists every view, estimates its size, and totals the maintenance overhead, then plans a denormalization or async-query alternative for the views that aren't earning their keep.
Drop blocking read repair
Tables set to BLOCKING read repair add latency and extra cross-node traffic on every read, which shows up as I/O cost. OhChimp counts the tables still on BLOCKING and drafts a switch to scheduled repairs instead, so reads stop paying a repair tax they don't need.
Cassandra cost optimization FAQ
How does OhChimp find Cassandra cost savings the node bill can't show?
OhChimp connects a read-only user and runs probe queries against Cassandra's system catalog and size estimates. That lets it see over-replicated keyspaces, oversized partitions, tombstone and GC pressure, mismatched compaction strategies, materialized view overhead, and blocking read repair, which are sources of waste a cloud billing dashboard never exposes.
Does OhChimp need write access or see my data?
No. OhChimp uses a read-only user and reads schema metadata and size estimates through probe queries against system_schema and system tables. It never stores your application rows, secrets, or workload contents.
What Cassandra costs does it actually reduce?
Storage and node footprint, mostly. It targets replication factors above 3 that multiply storage, partitions over 100MB and STCS-on-read-heavy tables that drive compaction and GC load, tombstone-prone tables, materialized views that double writes, and BLOCKING read repair that adds cross-node I/O.
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.
How are Cassandra savings verified, and what does it cost?
Savings are checked 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, and stays flagged not implemented until then. Pricing is a flat monthly fee with no cut of your savings, backed by a year-one ROI guarantee that refunds your subscription if it doesn't pay for itself in the first 12 months.
Related integrations
Teams running Cassandra usually run these too. OhChimp finds the waste in each and proves it on the bill.
ClickHouse
Query cost, compression, and storage
Elasticsearch
Index sizing, shard count, retention
Neo4j
Instance sizing, indexes, and storage