Cost Optimization Through Right-Sized Instance Selection

infoCost Optimization

Analyzing Redis workload characteristics to select the most cost-effective instance family and size without sacrificing performance.

Prompt: “We're running Redis on AWS ElastiCache using r6g.2xlarge instances but I suspect we might be over-provisioned. Our CPU is averaging 30% and memory is at 45%. Is there a cheaper instance type that would work for our workload, or should we consider data tiering with r6gd instances? How do I balance cost savings against performance risk?”

Agent Playbook

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

When right-sizing Redis instances for cost optimization, start by establishing true resource utilization patterns over time—including peaks, not just averages. Then check for memory fragmentation that might be inflating your memory needs. Finally, analyze your workload characteristics (ops/sec, command mix, connection patterns) to determine whether you're paying for memory-optimized instances when a balanced or compute-optimized family would suffice.

1Establish baseline resource utilization over peak periods

Look at `redis-memory-used`, `redis-memory-peak`, and `redis-memory-maxmemory` over the past 2-4 weeks, not just current averages. If your `redis-memory-peak` is consistently below 60% of `redis-memory-maxmemory` and CPU stays under 40% during business hours and weekend peaks, you're likely over-provisioned. The 30% CPU average you mentioned could spike to 70%+ during brief bursts—those peaks matter for sizing decisions. Identify your true 95th percentile usage, not the average.

redis.memory.usedredis.memory.peakredis.memory.maxmemory

2Check memory fragmentation ratio before concluding you're over-provisioned

Examine `redis-memory-fragmentation-ratio`—if it's consistently above 1.5, your 45% memory usage might actually represent only 30% of effective memory, meaning you're wasting capacity due to fragmentation rather than over-provisioning. The `memory-fragmentation-amplifies-cache-eviction-pressure` insight shows this can trigger premature evictions. If fragmentation is high, consider enabling activedefrag (Redis 4.0+) or scheduling a controlled restart during low-traffic windows before downsizing—you might need the capacity you have once fragmentation is resolved.

Memory Fragmentation Amplifies Cache Eviction Pressure redis.memory.fragmentation_ratioredis.memory.used

3Analyze ops/sec and command patterns to determine workload type

Review `redis-stats-instantaneous-ops-per-sec` and `redis-net-commands` to understand if you're running a high-throughput, low-latency workload or a lower-volume, memory-heavy caching layer. If you're consistently under 50K ops/sec with mostly GET/SET commands and low CPU, you're probably paying for memory-optimized instances (r6g) when balanced instances (m6g or m7g) would work fine. Conversely, if you're hitting 100K+ ops/sec with complex commands (ZRANGE, HGETALL), CPU becomes the bottleneck and you might need compute-optimized options.

redis.stats.instantaneous_ops_per_secredis.net.commands

4Evaluate memory headroom and eviction risk before downsizing

Compare your `redis-memory-peak` to `redis-memory-maxmemory`—if peaks approach 70%+ of maxmemory, you're at risk of evictions despite the 45% average. The `excessive-caching-high-memory` insight warns about cache thrashing when memory gets tight. If your workload has predictable spikes (e.g., morning batch jobs), you need headroom for those peaks. A safe rule of thumb: don't downsize unless your 95th percentile memory usage stays below 65% of maxmemory with at least 20% headroom for growth.

Excessive caching causes high memory usage and cache thrashing redis.memory.peakredis.memory.maxmemoryredis.memory.used

5Assess data tiering viability with access pattern analysis

For r6gd consideration, you need clear hot/cold data separation—analyze if most requests hit a small subset of keys (e.g., 80% of traffic hitting 20% of data). If your workload is evenly distributed or you lack clear temporal access patterns, r6gd's local NVMe won't help and standard r6g or m6g at a smaller size is better. Check `redis-clients-connected` patterns too—if you have many persistent connections, connection overhead might push you toward slightly larger instances even with lower memory/CPU usage.

redis.clients.connectedredis.net.commands

6Model cost-performance tradeoffs across instance families

Based on your findings, compare options: if memory usage stays under 60% and CPU under 40%, try m6g.xlarge (balanced, ~40% cheaper than r6g.2xlarge). If memory is the constraint but you're memory-bound, r6g.xlarge gives you half the memory at half the cost—viable if your peaks stay under 60% of current usage. Run a load test in a staging environment with the target instance size, monitoring `redis-stats-instantaneous-ops-per-sec` under peak load to ensure latency stays acceptable. A 15-20% cost reduction with <5% latency increase is usually a good trade for non-critical caches.

redis.memory.usedredis.stats.instantaneous_ops_per_sec