Estimate managed Redis cache costs for AWS ElastiCache, Azure Cache, or GCP Memorystore. Calculate node, storage, and data transfer expenses.
Managed Redis services like AWS ElastiCache, Azure Cache for Redis, and Google Cloud Memorystore provide high-performance in-memory caching without the operational burden of running your own Redis cluster. However, costs scale quickly with node size, number of nodes, and replication settings.
Redis pricing primarily depends on the node type (which determines memory and compute), the number of nodes in your cluster, and data transfer between nodes and your application. Multi-AZ replication doubles your node costs but provides automatic failover for production workloads.
This calculator helps you estimate monthly Redis costs across these dimensions, allowing you to compare different node sizes, cluster configurations, and replication strategies to find the sweet spot between performance, reliability, and cost.
This measurement provides a critical foundation for capacity planning and performance budgeting, helping teams align infrastructure resources with application requirements and growth projections. Integrating this calculation into monitoring and reporting workflows ensures that engineering decisions are grounded in real data rather than assumptions about system behavior.
Redis caching dramatically improves application performance but over-provisioning memory is a common and expensive mistake. This calculator helps you estimate costs for different node sizes and cluster configurations, ensuring you get the cache capacity you need without paying for unused memory. It also helps compare managed Redis against self-hosted options.
Node Cost = node_hourly_rate × nodes × 730 Transfer Cost = transfer_GB × transfer_rate Total Monthly = Node Cost + Transfer Cost
Result: $149.92/month
Three cache.r6g.large nodes at $0.068/hr running 730 hours cost $148.92 in compute. Adding 100 GB of cross-AZ data transfer at $0.01/GB adds $1.00, for a total of $149.92/month.
ElastiCache offers several node families. The R-series (r6g, r7g) provides memory-optimized instances ideal for large datasets. M-series (m6g) offers balanced compute and memory. T-series (t4g) provides burstable performance for dev/test. Graviton-based nodes (indicated by the g suffix) offer better price-performance than Intel equivalents.
A Redis cluster can have up to 500 nodes across 250 shards, each with up to one primary and five replicas. More replicas increase read throughput and availability but multiply costs linearly. For most production workloads, 1 primary with 1–2 replicas per shard provides a good balance.
Self-hosted Redis on EC2 eliminates the managed service premium but adds operational overhead. DynamoDB DAX provides a Redis-like caching layer specifically for DynamoDB. ElastiCache-compatible alternatives like KeyDB or Dragonfly offer open-source options with different performance profiles.
Start by estimating your working dataset size and add 25% overhead for Redis internal structures. Choose a node type with at least that much memory. For example, if your dataset is 10 GB, a cache.r6g.large (13.07 GB) provides adequate headroom.
Use Cluster Mode when you need more than the memory available on a single node, or when you need higher throughput across multiple shards. Non-clustered mode is simpler and sufficient for datasets that fit in a single node with replicas.
Multi-AZ deployments require at least one replica node in a different AZ, effectively doubling your minimum node count. However, cross-AZ replication is free within ElastiCache. You pay only for the additional node hours.
ElastiCache Serverless automatically scales capacity and charges based on data stored and compute units consumed. It's simpler but typically more expensive than provisioned nodes for steady-state workloads.
Keep your application and cache in the same Availability Zone to eliminate cross-AZ transfer charges. If using Multi-AZ for reliability, route reads to the local replica and writes to the primary.
Reserved Nodes for ElastiCache offer 30–40% savings with a 1-year term and up to 55% with a 3-year term. They apply to a specific node type in a specific region and are ideal for production caches that run continuously.