Redis Instance Right-Sizing for Cost and Performance

infoCost Optimization

Evaluating whether current Redis instance type is appropriately sized based on actual workload patterns to optimize cost and performance.

Prompt: “I'm running Redis on ElastiCache r6g.large but CPU is consistently under 20% and memory usage is only 40% - help me determine if I should downsize to save costs, or if there are traffic patterns or growth projections I should consider before changing instance size.”

Agent Playbook

When an agent encounters this scenario, Schema provides these diagnostic steps automatically.

When evaluating Redis instance right-sizing, start by investigating historical peak memory usage to ensure you won't hit OOM during recurring traffic spikes. Then analyze traffic patterns and growth trends before making sizing decisions. Low current utilization doesn't always mean you can safely downsize — burst capacity and headroom matter more than average usage.

1Investigate historical peak memory usage
Check if `redis.memory.peak` significantly exceeds current `redis.memory.used` — if peak is >1.5x your current usage, you've had memory pressure in the past that will likely recur. Compare peak against `redis.memory.maxmemory` to see how close you've come to limits. Look at timestamps of peak usage and correlate with application logs to understand if these are daily/weekly patterns (batch jobs, traffic spikes) or one-time events. Downsizing when peaks are 2x current usage is asking for OOM failures.
2Analyze traffic patterns and operation rates over time
Review `redis.stats.instantaneous_ops_per_sec` and `redis.net.commands` for the past 30 days to identify cyclical patterns. If your ops/sec varies by 3x or more between peak and off-peak times, you need capacity for those bursts even if average utilization is low. A constant 5K ops/sec is very different from 1K baseline with 15K spikes during business hours — the latter justifies higher instance capacity for burst handling.
3Assess cache efficiency and eviction behavior
Calculate your cache hit ratio using `redis.keyspace.hits / (redis.keyspace.hits + redis.keyspace.misses)` — if it's below 80%, you may be caching inefficiently or have data access patterns that don't benefit from your current cache size. Low memory usage with poor hit ratios suggests you could optimize your caching strategy rather than just downsize. Also check if any evictions are occurring despite 40% usage, which would indicate memory pressure from traffic patterns you're not seeing in averages.
4Review connection patterns and client behavior
Examine `redis.clients.connected` over time to understand if you have high connection counts that could stress CPU and network even with low memory usage. ElastiCache instance types differ in network bandwidth and CPU capacity, not just memory — if you have hundreds of concurrent connections or connection churn, the r6g.large's CPU and network capacity might be justified even at 40% memory. Check if CPU spikes correlate with connection spikes rather than just operation volume.
5Calculate growth trends and required operational headroom
Plot `redis.memory.used` trend over the past 3-6 months to determine growth rate. If you're growing >10% per quarter, your current 60% free headroom (at 40% usage) is appropriate for safe operation. Industry best practice is maintaining 40-50% headroom to handle unexpected traffic, deployment issues, or failed evictions. Also consider that at 40% usage with maxmemory set correctly, you have reasonable buffer — downsizing could leave you with <30% headroom on a smaller instance, which is operationally risky.
6Model cost savings against migration risk and test in non-prod
Calculate the actual monthly cost difference between r6g.large and the next size down (r6g.medium saves ~$100/month in us-east-1). Consider if that saving justifies the operational risk of a migration, potential downtime, and the effort required. If the data from previous steps shows stable low usage with no concerning peaks, test with the smaller instance in staging or a read replica first, running realistic load tests including peak traffic simulations before making the production change.

Technologies

Related Insights

Relevant Metrics

Monitoring Interfaces

Redis Datadog
Redis Native Metrics
Redis Prometheus