PostgreSQL Cost Optimization
Cut your PostgreSQL bill from inside the engine. OhChimp finds unused indexes, bloat, idle connections, and slow queries, and drafts each fix as a plan.
What OhChimp optimizes for PostgreSQL
Some of the cost levers OhChimp checks for PostgreSQL. Each one becomes a reviewable plan you approve before anything changes.
Drop unused and duplicate indexes
Indexes that never serve a scan still cost storage and slow every write. OhChimp reads pg_stat_user_indexes for indexes with zero scans, and finds duplicate indexes with the same definition on the same columns. It reports the wasted storage, then drafts the DROP with rollback steps.
Reclaim table and index bloat
Dead tuples and bloated B-tree indexes inflate storage and waste cache. OhChimp measures the dead-row percentage per table and estimates index bloat from page statistics, flagging tables over 10% dead rows and indexes over 30% bloat. The plan runs VACUUM, REINDEX, or VACUUM FULL so you stop paying to store empty space.
Right-size memory from cache hit ratio
OhChimp reads your buffer cache hit ratio from pg_stat_database. A ratio below 95% points to memory too small for your working set, so the plan moves shared_buffers or instance size in the right direction. It also audits your memory settings against the live ratio.
Pool idle connections
Idle and idle-in-transaction connections hold memory and can push you to a bigger instance than your query volume needs. OhChimp counts them against max_connections in pg_stat_activity, and when utilization passes 70% it plans a connection pooler like PgBouncer so you can run leaner.
Fix slow queries and missing indexes
With pg_stat_statements enabled, OhChimp ranks queries by total execution time and finds large tables doing repeated sequential scans where an index would help. EXPLAIN plans (without ANALYZE) confirm the inefficient scans and joins inflating your compute, and the plan adds the index or rewrites the query.
Tune work_mem and cut temp-file spill
Sorts and hashes that exceed work_mem spill to temp files on disk, burning I/O. OhChimp reads temp-file volume from pg_stat_database and audits memory and parallelism settings, then flags a work_mem change when spill passes 1 GB so queries stay in RAM.
Partition oversized tables
Tables past 10 GB are slow and expensive to query, vacuum, and back up. OhChimp finds them from table sizes and scan patterns, then plans partitioning so the engine prunes irrelevant data and you can archive cold partitions to a cheaper tier.
Worked example
For example, say OhChimp probes your primary and finds 40 GB of indexes with zero scans, a table that is 60% dead rows, and a cache hit ratio sitting below 95% on an instance that has room to shed memory.
It drafts the index drops, a VACUUM and REINDEX plan, and a memory right-size, each with a confidence score and rollback steps. You press apply. OhChimp cannot yet measure what a single PostgreSQL plan saves, so it does not mark one VERIFIED. The projected saving stays an estimate, and when the 30 days of monitoring end the result reads "Inconclusive."
PostgreSQL cost optimization FAQ
How does OhChimp find PostgreSQL savings the instance bill can't show?
OhChimp connects a read-only database user and runs probe queries against Postgres catalog and statistics views. That lets it see unused and duplicate indexes, table and index bloat, cache hit ratio, idle connections, slow queries, and temp-file spill, 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, sets the session read-only with a 10-second statement timeout, and reads metadata and table or index statistics only. It never stores your application rows, secrets, or workload contents, and it warns you if the connecting role still has write privileges.
What PostgreSQL costs can OhChimp reduce?
Storage from unused, duplicate, and bloated indexes and dead-row table bloat. Compute from idle connections, slow queries doing sequential scans, lock contention, and temp-file spill that wants more work_mem. It also right-sizes memory from your cache hit ratio and flags oversized tables for partitioning and tiered archiving.
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 the savings verified, and what does it cost?
Today PostgreSQL savings stay estimates. OhChimp cannot yet measure what a single PostgreSQL plan saves, so it does not mark one VERIFIED, and when the 30 days of monitoring end the result reads "Inconclusive." The only savings OhChimp measures today come from lowering the CPU and memory requests of a Kubernetes Deployment, StatefulSet, or DaemonSet. Pricing is a flat monthly fee, OhChimp never takes a cut of savings, and paid plans carry a year-one ROI guarantee that lets you request a refund of your first 12 months of subscription fees if the verified savings on your Costs tab fall short of them. Estimated savings do not count.
The unused-index lever, start to finish: Find unused Postgres indexes and drop them safely.
Related integrations
Teams running PostgreSQL usually run these too. OhChimp finds the waste in each and drafts every fix as a plan you approve.
MySQL
Unused indexes, slow queries, instance sizing
Vector DB
Index sizing, dimensions, and storage
MongoDB
Index usage, document bloat, sizing