Hey everyone, I've been diving into our AWS costs and wanted to share what we did with our RDS instances. I'm still pretty new to FinOps, but our team lead walked me through this process and the results were surprising.
We had several db.t3.large instances running steady workloads. The first step was enabling Performance Insights on all of them for a week. This showed our CPU was averaging around 30% with occasional peaks. We also looked at CloudWatch metrics for read/write IOPS and network traffic.
Based on that data, we did two things: first, we switched the instances from db.t3.large to db.t3.medium. The performance metrics stayed well within the new instance limits. Second, we identified one instance used only during business hours and applied a start/stop schedule using AWS Instance Scheduler. The combined changes dropped our monthly bill from about $850 to under $500.
Has anyone else tried similar downsizing? I'm curious if there are other quick wins like this for managed services.
That's a solid start, but calling a 42% reduction with no performance loss feels a bit premature. You looked at averages and peaks for a week, but what about your p99 or p99.9 latency during those occasional CPU peaks after the downsizing? Performance Insights is useful for identifying the top SQL offenders, but it won't show you the subtle connection latency spikes that your app might be swallowing and retrying.
Also, switching from t3.large to t3.medium while staying within burst credit limits is fine until you have a sustained workload that drains the CPU credits. Then you're throttled to baseline performance, which on a t3.medium is quite low. The real test will be a few months from now, especially if your data set grows and more operations shift to disk. Did you baseline your query performance before and after, or just assume the CloudWatch green lines were good enough?
The start/stop scheduler is a classic move, but have you factored in the warm-up time for the instance after a cold stop? The first few queries post-start can be painfully slow while the buffer pool fills, which users might notice. It's a trade-off you've implicitly made for the cost saving.
Trust but verify.
Your approach with the Instance Scheduler is a good tactical win, especially for non-production or time-bound workloads. That's a classic pattern.
However, user1015 has a valid underlying concern about data consistency. When you start and stop an RDS instance, any in-memory cache is wiped. The first queries after a morning startup will be significantly slower until the buffer pool warms up again. If this instance handles any customer-facing operations at 9:00 AM, you've just traded cost for inconsistent latency, which can create sporadic bottlenecks in your integration layer that are very hard to trace.
Did you monitor the connection count and query latency for that specific instance during the first 30 minutes after each scheduled start? The CloudWatch metrics for `DatabaseConnections` and `ReadLatency`/`WriteLatency` would show that ramp-up period.
Single source of truth is a myth.
That's a classic first-pass optimization, and seeing those numbers drop is always satisfying. I did something similar a while back, but focused on a different lever: storage.
Your mention of using the Instance Scheduler for the time-bound workload made me think of it. We had a reporting instance on gp2 storage. The baseline IOPS were fine, but we were paying for provisioned throughput we rarely needed. Switching that single instance to gp3 and manually setting the IOPS to match our observed baseline cut that line item by about 60% overnight. The performance profile was identical for our batch jobs.
It's a good reminder that instance size is only one part of the RDS cost equation. The storage type and provisioned IOPS can be silent budget drains, especially for workloads with predictable, low-intensity disk patterns. Did your CloudWatch review include a look at `VolumeReadIOPS` and `VolumeWriteIOPS` against your provisioned levels? You might find another 10-15% hiding there without touching CPU or memory.
throughput first