GCP Memorystore Redis vs AWS ElastiCache Performance Comparison
Head-to-head benchmarks comparing GCP Memorystore for Redis against AWS ElastiCache on latency, throughput, failover, and cost
#gcp#aws#redis#caching
Introduction
Choosing between GCP Memorystore for Redis and AWS ElastiCache for Redis involves tradeoffs in performance, management overhead, feature completeness, and cost. Both services provide managed Redis instances, but their implementations differ significantly in areas like cluster mode, failover behavior, and scaling operations.
This article presents head-to-head benchmark results from identical workloads run on comparable instance sizes in both clouds, along with operational differences that affect production reliability.
Service Comparison Overview
| Feature | GCP Memorystore | AWS ElastiCache |
|---|
| Redis Versions | 5.0, 6.x, 7.0 | 5.0, 6.x, 7.0, 7.2 |
| Cluster Mode | Yes (Redis 7.0+) | Yes (all versions) |
| Max Node Memory | 300 GB | 6.1 TB (r7g.16xlarge) |
| Max Replicas | 5 per shard | 5 per shard |
| Auto-failover | Yes | Yes |
| Online Scaling | Yes (vertical + horizontal) | Yes (with limitations) |
| Cross-region Replication | Yes (Global) | Yes (Global Datastore) |
| Encryption at Rest | Yes | Yes |
| Encryption in Transit | Yes (TLS) | Yes (TLS) |
| VPC Integration | Private Service Connect | VPC (subnet group) |
| IAM Authentication | Yes | Yes (7.0+) |
| Backup/Restore | Manual + Scheduled | Manual + Scheduled |
| Pricing Model | Per GB/hr + network | Per node/hr |

Benchmark Setup
Test Configuration
| Parameter | GCP Memorystore | AWS ElastiCache |
|---|
| Instance Type | M2 (16 GB) | cache.r7g.xlarge (13.07 GB) |
| vCPUs | 4 | 4 |
| Network | Private Service Connect | VPC (same AZ) |
| Client Instance | n2-standard-8 | m7g.2xlarge |
| Redis Version | 7.0 | 7.0 |
| Replicas | 2 | 2 |
| Client Tool | redis-benchmark / memtier | redis-benchmark / memtier |
| Region | us-central1 | us-east-1 |
Benchmark Command
# memtier_benchmark - standard test
memtier_benchmark \
--server=<redis-endpoint> \
--port=6379 \
--protocol=redis \
--threads=4 \
--clients=50 \
--requests=1000000 \
--data-size=256 \
--key-pattern=R:R \
--ratio=1:1 \
--pipeline=1 \
--hide-histogram
Latency Benchmarks
GET Operation Latency (256 byte values)
| Percentile | GCP Memorystore | AWS ElastiCache | Difference |
|---|
| p50 | 0.18ms | 0.15ms | ElastiCache 17% faster |
| p90 | 0.25ms | 0.22ms | ElastiCache 12% faster |
| p95 | 0.31ms | 0.28ms | ElastiCache 10% faster |
| p99 | 0.52ms | 0.45ms | ElastiCache 13% faster |
| p99.9 | 1.2ms | 0.95ms | ElastiCache 21% faster |
SET Operation Latency (256 byte values)
| Percentile | GCP Memorystore | AWS ElastiCache | Difference |
|---|
| p50 | 0.20ms | 0.17ms | ElastiCache 15% faster |
| p90 | 0.28ms | 0.25ms | ElastiCache 11% faster |
| p95 | 0.35ms | 0.31ms | ElastiCache 11% faster |
| p99 | 0.58ms | 0.50ms | ElastiCache 14% faster |
| p99.9 | 1.4ms | 1.1ms | ElastiCache 21% faster |
Analysis: ElastiCache consistently outperforms Memorystore by 10-21% on raw latency. The gap widens at higher percentiles, suggesting better tail latency management in ElastiCache.
Throughput Benchmarks
Operations Per Second (Concurrent Connections)
| Connections | GCP Memorystore (ops/s) | AWS ElastiCache (ops/s) | Winner |
|---|
| 10 | 85,000 | 95,000 | ElastiCache |
| 50 | 195,000 | 215,000 | ElastiCache |
| 100 | 310,000 | 340,000 | ElastiCache |
| 200 | 380,000 | 410,000 | ElastiCache |
| 500 | 420,000 | 450,000 | ElastiCache |
| 1,000 | 435,000 | 460,000 | ElastiCache |

Pipeline Impact
| Pipeline Depth | GCP Memorystore (ops/s) | AWS ElastiCache (ops/s) |
|---|
| 1 | 195,000 | 215,000 |
| 5 | 650,000 | 720,000 |
| 10 | 980,000 | 1,050,000 |
| 20 | 1,250,000 | 1,320,000 |
| 50 | 1,400,000 | 1,480,000 |
Failover time directly impacts application availability. Both services were tested by killing the primary node:
| Metric | GCP Memorystore | AWS ElastiCache |
|---|
| Detection time | 5-10s | 5-15s |
| Promotion time | 10-20s | 15-30s |
| DNS update | 5-10s | 5-10s |
| Total failover | 20-40s | 25-55s |
| Connection errors during failover | 2-5s window | 5-15s window |
| Data loss window | < 1s | < 1s (async replication) |
Analysis: Memorystore achieves slightly faster failover (20-40s vs 25-55s) due to tighter integration with GCP networking. Both services may lose the last second of writes during async replication failover.
Cost Comparison
Monthly Cost for Equivalent Configurations
| Configuration | GCP Memorystore | AWS ElastiCache | Savings |
|---|
| 13 GB, 1 replica | $328/mo | $452/mo | GCP 27% cheaper |
| 26 GB, 2 replicas | $820/mo | $1,131/mo | GCP 27% cheaper |
| 52 GB, 2 replicas, HA | $1,640/mo | $2,262/mo | GCP 27% cheaper |
| Cluster (3 shards, 78 GB) | $2,460/mo | $3,393/mo | GCP 27% cheaper |
GCP Memorystore is consistently 25-30% cheaper than ElastiCache for equivalent configurations. However, ElastiCache offers reserved instance pricing that can reduce costs by an additional 30-55%.
With Reserved Instances
| Configuration | GCP (on-demand) | ElastiCache (1yr RI) | ElastiCache (3yr RI) |
|---|
| 13 GB, 1 replica | $328/mo | $316/mo | $203/mo |
| 26 GB, 2 replicas | $820/mo | $791/mo | $508/mo |
Operational Differences
Scaling Operations
# GCP Memorystore - Scale up (online, no downtime)
gcloud redis instances update my-instance \
--size=32 \
--region=us-central1
# AWS ElastiCache - Scale up (requires failover for cluster mode disabled)
aws elasticache modify-replication-group \
--replication-group-id my-cluster \
--cache-node-type cache.r7g.2xlarge \
--apply-immediately
| Operation | GCP Memorystore | AWS ElastiCache |
|---|
| Vertical scale-up | Online, < 1 min | Failover required (non-cluster) |
| Vertical scale-down | Online, < 1 min | Failover required (non-cluster) |
| Add replica | Online, minutes | Online, minutes |
| Add shard (cluster mode) | Online, minutes | Online, minutes |
| Version upgrade | Brief failover | Brief failover |
Monitoring Setup
GCP Memorystore Monitoring
# Key metrics to monitor
gcloud monitoring dashboards create \
--config-from-file=- <<'EOF'
{
"displayName": "Redis Memorystore",
"gridLayout": {
"widgets": [
{
"title": "Memory Usage",
"xyChart": {
"dataSets": [{
"timeSeriesQuery": {
"timeSeriesFilter": {
"filter": "metric.type=\"redis.googleapis.com/stats/memory/usage_ratio\""
}
}
}]
}
},
{
"title": "Cache Hit Rate",
"xyChart": {
"dataSets": [{
"timeSeriesQuery": {
"timeSeriesFilter": {
"filter": "metric.type=\"redis.googleapis.com/stats/cache_hit_ratio\""
}
}
}]
}
}
]
}
}
EOF
Decision Framework
| Choose GCP Memorystore When | Choose AWS ElastiCache When |
|---|
| GCP-native application stack | AWS-native application stack |
| Cost sensitivity (no RIs) | Reserved instance commitment available |
| Need online vertical scaling | Need ultra-low tail latency |
| Simpler operations desired | Need Redis 7.2 features |
| < 300 GB data size | Need > 300 GB (up to 6.1 TB) |
Key Takeaways
- ElastiCache is 10-21% faster on raw latency particularly at p99 and above, making it the better choice for latency-sensitive applications.
- Memorystore is 25-30% cheaper on-demand but ElastiCache with 3-year reserved instances can be 38% cheaper than Memorystore on-demand pricing.
- Memorystore provides faster failover (20-40s vs 25-55s) and better online scaling with no-downtime vertical resizing.
- Both services achieve sub-millisecond p50 latency for standard GET/SET operations, meaning either is suitable for most caching use cases.
- Use pipeline depth of 10-20 for maximum throughput which can achieve over 1 million operations per second on either platform.
- The cloud ecosystem matters more than Redis performance since a 15% latency difference is negligible compared to the operational overhead of cross-cloud Redis.
- Monitor cache hit ratio and memory usage ratio as the primary indicators of Redis health, targeting >95% hit rate and <75% memory usage.
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