Memcached Cost Optimization
OhChimp reads your Memcached stats to cut your bill: right-size oversized caches, fix evictions, and reclaim slab fragmentation, each as a plan.
What OhChimp optimizes for Memcached
Some of the cost levers OhChimp checks for Memcached. Each one becomes a reviewable plan you approve before anything changes.
Oversized, under-filled instances
An instance running under 30% memory utilization is paying for RAM that holds nothing. OhChimp reads bytes used against limit_maxbytes and flags caches that are sized far larger than your working set, then drafts a downsize plan with rollback steps.
Low hit ratio wasting the cache
A hit ratio under 80% means you're paying to run a cache that misses more than it should. OhChimp computes the ratio from get_hits and get_misses and surfaces it so you can fix the caching strategy or right-size what you actually keep warm.
Evictions from a cache that's too small
A high eviction rate alongside memory pressure means the cache is undersized and thrashing, which pushes load back onto your origin. OhChimp reads the eviction rate and memory utilization, breaks evictions down by slab class, and plans the right memory bump so the cache earns its keep.
Slab fragmentation and wasted chunks
Under-utilized slab classes hold free chunks that count against your memory limit but store nothing. OhChimp reads stats slabs to measure wasted bytes per slab and overall chunk utilization, then flags the fragmentation so you can reclaim the room.
Allocated-but-unused memory
When allocated slab pages exceed the data actually stored, the fragmentation ratio climbs and real RAM sits idle. OhChimp compares total allocated pages against bytes used and recommends a restart when fragmentation passes 1.5, or a downsize when utilization is low enough to pay off.
Connection churn burning CPU
High connection creation rates waste CPU on setup and teardown that persistent connections would avoid. OhChimp reads total_connections against uptime to catch churn above a healthy threshold and points to pooling that lets you run on a leaner instance.
Worked example
For example, say OhChimp reads your stats and finds an instance sitting at 22% memory utilization, a fragmentation ratio above 1.5, and connection churn from clients that reconnect on every request.
It drafts a downsize plan, a memory-reclaim plan, and a pooling change, each with a confidence score, a risk level, and rollback steps. You press apply. OhChimp cannot yet measure what a single Memcached plan saves, so it does not mark one VERIFIED. The saving stays an estimate, and when the 30 days of monitoring end the result reads "Inconclusive."
Memcached cost optimization FAQ
How does OhChimp connect to Memcached?
OhChimp connects over a read-only connection to your Memcached host and port and runs the native stats protocol command, along with stats slabs and stats items. It reads cache metrics only and never writes to the cache or reads your cached values.
Does OhChimp need write access or see my data?
No. OhChimp only runs the read-only stats commands to collect metrics like hit ratio, memory utilization, evictions, and slab allocation. It never stores application data, secrets, or the contents of your cached items.
What Memcached costs can OhChimp actually reduce?
The main ones are oversized instances running at low memory utilization, evictions that mean the cache is too small, slab fragmentation and wasted chunks, allocated-but-unused memory from a high fragmentation ratio, low hit ratios, and connection churn that wastes CPU.
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 Memcached savings verified, and what does it cost?
Today Memcached savings stay estimates. OhChimp cannot yet measure what a single Memcached 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 with no cut of your savings, and paid plans include 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.
Related integrations
Teams running Memcached usually run these too. OhChimp finds the waste in each and drafts every fix as a plan you approve.
Vector DB
Index sizing, dimensions, and storage
Neo4j
Instance sizing, indexes, and storage
PostgreSQL
Unused indexes, bloat, and cache efficiency