MySQL Cost Optimization
Cut your MySQL bill from the inside. OhChimp finds unused indexes, fragmented tables, and buffer pool waste, then writes fixes verified on your real bill.
What OhChimp optimizes for MySQL
Some of the cost levers OhChimp checks for MySQL. Each one becomes a reviewable plan you approve before anything changes.
Drop unused and redundant indexes
Indexes that never serve a query still cost storage and slow every write. OhChimp reads sys.schema_unused_indexes and sys.schema_redundant_indexes to find indexes with no recorded usage and ones already covered by another index, then hands you the exact DROP INDEX statement from sys for each redundant index, with rollback steps.
Reclaim fragmented table space
Tables accumulate free space as rows churn, and you pay to store the gaps. OhChimp scans information_schema for tables over 20% fragmentation, totals the reclaimable bytes from their free space, and plans the OPTIMIZE TABLE so you stop paying for empty space on disk.
Right-size memory from your buffer pool hit ratio
A low InnoDB buffer pool hit ratio means frequent disk reads, and buffer pool wait-free events mean the pool is starved. OhChimp reads both from performance_schema status counters and points you toward an innodb_buffer_pool_size change or an instance resize in the direction your real load supports.
Index the queries doing full table scans
Queries that read whole tables burn CPU and I/O and push you toward a bigger instance. OhChimp reads sys.statements_with_full_table_scans to find the full-scan queries that run most often, and the slowest statement digests from performance_schema by total execution time, so you can add the index that cuts the work.
Partition or archive your largest tables
Very large tables are expensive to query, back up, and maintain. OhChimp lists base tables over 10 GB from information_schema along with their row counts and last-update times, so you can partition by date and archive the cold data instead of keeping it all hot.
Trim binary logs and idle replicas
Binary logs and replicas quietly consume disk and compute. OhChimp reports total binary log size and the current retention setting, and checks replication status for lag or a replica that is no longer earning its keep, so you can shorten retention or retire what you do not need.
MySQL cost optimization FAQ
How does OhChimp connect to MySQL?
OhChimp connects with a database username and password, with optional SSL/TLS. It opens a read-only session with a 30-second query timeout and runs probe queries against catalog and statistics views like information_schema, performance_schema, and the sys schema. It works with self-hosted and managed MySQL.
Does OhChimp need write access or see my data?
No. OhChimp runs in a read-only session and reads schema, index, and engine statistics, never your application rows. At connect time it checks your grants and warns you if it sees INSERT, UPDATE, or DELETE privileges, so you can switch to a read-only user. It never stores your application data, secrets, or workload contents.
What MySQL costs can OhChimp actually reduce?
The ones a cloud billing dashboard never exposes: unused and redundant indexes, fragmented table space you can reclaim with OPTIMIZE TABLE, a buffer pool hit ratio that signals over- or under-sized memory, queries doing full table scans, tables over 10 GB that should be partitioned, and oversized binary logs or idle replicas.
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 MySQL 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. Until then it stays flagged not implemented. Pricing is a flat monthly fee with no cut of your savings, plus a year-one ROI guarantee: a full refund of subscription fees if it does not pay for itself in your first 12 months on a paid plan.
Related integrations
Teams running MySQL usually run these too. OhChimp finds the waste in each and proves it on the bill.
MongoDB
Index usage, document bloat, sizing
PostgreSQL
Unused indexes, bloat, and cache efficiency
Redis
Keyspace, TTLs, eviction, and memory