Comparing AWS ElastiCache Options: Redis vs Serverless Redis - Constraints, Costs, and Functionality

READER BEWARE: THE FOLLOWING WRITTEN ENTIRELY BY AI WITHOUT HUMAN EDITING.

Introduction

AWS ElastiCache is a fully managed in-memory caching service that supports two open-source engines: Redis and Memcached. As applications scale and demand sub-millisecond response times, choosing the right caching strategy becomes critical. With the introduction of ElastiCache Serverless, AWS has expanded the options for deploying Redis, creating new considerations for architecture, cost, and operational complexity.

This comprehensive guide compares the different ElastiCache options available, with a focus on Redis implementations. We’ll examine the constraints, costs, and functionality of each option to help you make an informed decision for your use case.

Overview of ElastiCache Options

AWS ElastiCache offers several deployment options:

1. ElastiCache for Redis (Node-based) - Traditional cluster deployment with manual node management
2. ElastiCache Serverless for Redis - Fully serverless, automatically scaling Redis
3. ElastiCache for Memcached - Simpler caching engine for specific use cases

This guide primarily focuses on Redis options, as Redis has become the de facto standard for in-memory data stores due to its rich feature set and versatility.

ElastiCache for Redis (Node-based)

Overview

ElastiCache for Redis is the traditional deployment model where you provision and manage specific node types. You have full control over node sizing, cluster configuration, and replication topology.

Architecture Options

Cluster Mode Disabled (Single Shard):

• Single primary node with optional read replicas
• Maximum 5 read replicas per primary
• Up to 250 GB of memory per node (depending on instance type)
• Best for workloads that fit within a single shard

Cluster Mode Enabled (Sharded):

• Horizontal scaling across multiple shards (up to 500 shards)
• Each shard has a primary and optional replicas
• Data is partitioned across shards using hash slots
• Better for large datasets and high throughput requirements

Key Features

Data Structures:

• Strings, Lists, Sets, Sorted Sets, Hashes
• Bitmaps, HyperLogLogs, Geospatial indexes
• Streams (for event sourcing and messaging)
• JSON support (with Redis Stack)

Advanced Capabilities:

• Pub/Sub messaging
• Lua scripting
• Transactions
• Geospatial queries
• Time series data support
• Search and query capabilities (with Redis Stack)

High Availability:

• Automatic failover with Multi-AZ deployment
• Manual failover for planned maintenance
• Backup and restore (RDB snapshots, AOF logs)
• Point-in-time recovery

Security:

• Encryption at rest using KMS
• Encryption in transit (TLS)
• Redis AUTH for authentication
• RBAC (Role-Based Access Control) with Redis 6.0+
• VPC isolation

Constraints

Sizing Limitations:

• Must choose instance type upfront (cache.t3.micro to cache.r7g.16xlarge)
• Maximum memory depends on instance type (up to 317 GB for r7g.16xlarge)
• Cannot exceed 500 shards in cluster mode
• Limited to 5 read replicas per shard

Operational Overhead:

• Manual scaling requires changing instance types or adding shards
• Downtime may be required for some configuration changes
• Need to monitor memory usage and eviction policies
• Manual capacity planning required

Configuration Complexity:

• Must understand cluster mode implications
• Need to configure parameter groups
• Manual replication lag monitoring
• Complex migration between cluster modes

Network:

• Only accessible within VPC (no public endpoints)
• Cross-region replication requires Global Datastore (additional cost)
• Maximum 6.1 Gbps network throughput per node (varies by instance)

Versioning:

• Must manage Redis version upgrades manually
• Some features require specific Redis versions
• Backwards compatibility considerations

Cost Structure

Pricing Components:

1. Node Hours:

   • Charged per node per hour
   • Varies by instance type and region
   • Example pricing (us-east-1):
     • cache.t3.micro: $0.017/hour ($12.24/month)
     • cache.m7g.large: $0.149/hour ($107.28/month)
     • cache.r7g.xlarge: $0.334/hour ($240.48/month)
     • cache.r7g.16xlarge: $5.344/hour ($3,847.68/month)

2. Backup Storage:

   • $0.085/GB per month for automatic backups
   • No charge for one active backup per cluster
   • Additional backups charged at standard rate

3. Data Transfer:

   • Data transfer IN: Free
   • Data transfer OUT to internet: $0.09/GB (first 10 TB/month)
   • Data transfer OUT to same region: Free
   • Data transfer OUT cross-region: $0.02/GB

4. Global Datastore (Cross-Region Replication):

   • Additional charge of ~30% of base node cost
   • Cross-region data transfer charges apply

Example Monthly Costs:

Small Development Environment:

• Configuration: 1x cache.t3.micro (cluster mode disabled)
• Node cost: $12.24/month
• Backup storage (1 GB): $0.09/month
• Total: ~$12-15/month

Medium Production Environment:

• Configuration: 1 primary + 2 replicas, cache.m7g.large (Multi-AZ)
• Node cost: 3 × $107.28 = $321.84/month
• Backup storage (10 GB): $0.85/month
• Total: ~$325-350/month

Large Production Environment:

• Configuration: 10 shards × 3 nodes (primary + 2 replicas), cache.r7g.xlarge
• Node cost: 30 × $240.48 = $7,214.40/month
• Backup storage (100 GB): $8.50/month
• Total: ~$7,250-7,500/month

Enterprise Multi-Region:

• Configuration: 2 regions, 10 shards × 3 nodes, cache.r7g.4xlarge
• Primary region: 30 × $961.92 = $28,857.60/month
• Secondary region (Global Datastore): 30 × $961.92 × 1.3 = $37,514.88/month
• Cross-region transfer (1 TB/month): $20/month
• Total: ~$66,000-68,000/month

Best Use Cases

Ideal For:

• Predictable, consistent workload patterns
• Applications requiring maximum performance and low latency (<1ms)
• Scenarios where you need full control over Redis configuration
• Workloads requiring specific instance types for cost optimization
• Applications with steady-state traffic that you can capacity plan for
• Use cases requiring advanced Redis features (Lua scripts, complex data structures)
• High-throughput applications (>1M requests per second)

Not Ideal For:

• Highly variable or unpredictable traffic patterns
• Small or intermittent workloads with long idle periods
• Development/testing environments with sporadic usage
• Applications requiring instant, automatic scaling
• Teams without Redis expertise or capacity planning experience

ElastiCache Serverless for Redis

Overview

ElastiCache Serverless is a fully serverless deployment option introduced in 2023. It automatically scales capacity based on application traffic patterns, eliminating the need for manual capacity planning and node management.

Architecture

Serverless Model:

• No nodes to provision or manage
• Automatic scaling from minimal to maximum capacity
• Scales in 1 ECPU (ElastiCache Processing Unit) increments
• Storage automatically allocated based on data size

Capacity Units:

• ECPU (ElastiCache Processing Unit): Measures compute capacity
• Storage: Measured in GB, automatically provisioned
• Scales independently: compute and storage can scale separately
• Minimum: 1 ECPU, Maximum: configurable (up to 5,000 ECPUs)

Key Features

Automatic Scaling:

• Scales up within seconds based on traffic
• Scales down during low traffic periods
• No downtime during scaling operations
• Configurable maximum capacity limits

High Availability:

• Built-in Multi-AZ replication (always enabled)
• Automatic failover
• Continuous backups with point-in-time recovery
• 99.99% SLA for multi-AZ deployments

Data Structures (Supported Subset):

• Strings, Lists, Sets, Sorted Sets, Hashes
• Bitmaps, HyperLogLogs
• Streams (limited functionality)
• JSON support

Security:

• Encryption at rest (mandatory)
• Encryption in transit (mandatory)
• VPC isolation
• IAM authentication support
• RBAC support

Constraints

Feature Limitations:

• Lua scripting not supported
• Pub/Sub limited to basic functionality
• Some Redis commands restricted or limited
• Redis Stack features not available
• No MULTI/EXEC transactions
• Limited Streams functionality

Compatibility:

• Compatible with Redis 7.1+ API
• Not all Redis commands supported
• Some client libraries may require updates
• Module support is limited

Scaling:

• Cannot manually control specific node types
• Scaling is automatic but may not be instant for extreme spikes
• Cannot guarantee specific latency SLAs
• Cold start penalty for completely idle caches

Network:

• VPC-only access (like node-based)
• No Global Datastore support currently
• Cross-region replication not available
• Maximum throughput depends on ECPUs allocated

Operational:

• Less visibility into underlying infrastructure
• Limited control over Redis configuration parameters
• Cannot export/import RDB files directly
• Backup management is automatic (less control)

Size Limitations:

• Maximum 5,000 ECPUs per cache
• Maximum storage: 5 TB per cache
• Request size limits may apply

Cost Structure

Pricing Components:

1. ECPU Hours:

   • Charged per ECPU-hour consumed
   • Example pricing (us-east-1): ~$0.125/ECPU-hour
   • Minimum 1 ECPU-hour per hour of operation

2. Storage:

   • Charged per GB-hour
   • Example pricing (us-east-1): $0.125/GB-hour ($90/GB-month)

3. Data Transfer:

   • Same as node-based ElastiCache
   • Data transfer IN: Free
   • Data transfer OUT: Standard AWS rates

4. Backup Storage:

   • Included in base pricing (continuous backups)
   • No additional charge for backups

Example Monthly Costs:

Small Variable Workload:

• Average: 2 ECPUs, 5 GB storage
• ECPU cost: 2 × $0.125 × 730 hours = $182.50/month
• Storage cost: 5 × $90 = $450/month
• Total: ~$630-650/month

Medium Variable Workload:

• Average: 10 ECPUs, 50 GB storage
• ECPU cost: 10 × $0.125 × 730 hours = $912.50/month
• Storage cost: 50 × $90 = $4,500/month
• Total: ~$5,400-5,500/month

Large Spiky Workload:

• Average: 50 ECPUs (spikes to 200), 200 GB storage
• ECPU cost: 50 × $0.125 × 730 hours = $4,562.50/month
• Storage cost: 200 × $90 = $18,000/month
• Total: ~$22,500-23,000/month

Important Note: The storage pricing for Serverless is significantly higher than node-based ElastiCache. A cache.r7g.xlarge instance with 26 GB costs ~$240/month, while 26 GB in Serverless costs ~$2,340/month in storage alone.

Best Use Cases

Ideal For:

• Variable, unpredictable traffic patterns
• Development and testing environments
• Applications with periodic spikes (hourly, daily, weekly patterns)
• Startups and small teams without Redis operations expertise
• Microservices architectures with many small caches
• Applications that can tolerate feature limitations
• Cost optimization for low-utilization environments

Not Ideal For:

• Consistent, high-throughput workloads (cost inefficient)
• Applications requiring advanced Redis features (Lua, Pub/Sub, transactions)
• Latency-critical applications needing <1ms guarantees
• Large datasets (>1 TB) due to storage costs
• Workloads requiring maximum performance at lowest cost
• Applications needing Global Datastore or cross-region replication

ElastiCache for Memcached (Brief Overview)

When to Consider Memcached

While this guide focuses on Redis, Memcached is still relevant for specific use cases:

Advantages:

• Simpler, more straightforward caching
• Multi-threaded architecture (better CPU utilization)
• Horizontally scalable (up to 40 nodes)
• Slightly lower latency for simple get/set operations

Limitations:

• No data persistence
• Limited data structures (only key-value)
• No replication or failover
• No backup and restore
• No transactions or Pub/Sub

Cost:

• Similar pricing to Redis node-based
• Example: cache.m7g.large $0.149/hour ($107.28/month)

Best For:

• Pure caching use cases (not data store)
• Applications where data loss on restart is acceptable
• Workloads benefiting from multi-threading
• Simple distributed caching without advanced features

Detailed Comparison Matrix

Functionality Comparison

Cost Comparison by Scenario

Scenario 1: Small Development Cache (5 GB, Low Traffic)

Node-based (cache.t3.micro):

• Compute: $12.24/month
• Storage: Included
• Total: ~$12-15/month ✅ Winner for small, consistent workloads

Serverless:

• Compute: 1 ECPU × $0.125 × 730 = $91.25/month
• Storage: 5 GB × $90 = $450/month
• Total: ~$540-550/month

Winner: Node-based (45x cheaper)

Scenario 2: Medium Production (50 GB, Moderate Traffic, 95% Time Low)

Node-based (cache.m7g.large with 1 replica):

• Compute: 2 × $107.28 = $214.56/month
• Storage: Included
• Total: ~$215-225/month ✅ Winner if traffic is consistent

Serverless (scales 1-10 ECPUs, avg 3):

• Compute: 3 ECPU × $0.125 × 730 = $273.75/month
• Storage: 50 GB × $90 = $4,500/month
• Total: ~$4,750-4,800/month

Winner: Node-based (21x cheaper)

Scenario 3: Large Spiky Workload (100 GB, High Spikes, Low Baseline)

Node-based (cache.r7g.xlarge, must size for peak):

• Compute: $240.48/month (single node)
• Or 3 nodes with replicas: $721.44/month
• Storage: Included
• Total: ~$240-750/month

Serverless (1-100 ECPUs, avg 20 ECPUs):

• Compute: 20 ECPU × $0.125 × 730 = $1,825/month
• Storage: 100 GB × $90 = $9,000/month
• Total: ~$10,800-11,000/month

Winner: Node-based (even with over-provisioning)

Scenario 4: Intermittent Development/Testing (10 GB, Used 8 Hours/Day)

Node-based (cache.t3.small, running 24/7):

• Compute: $24.48/month (cannot shut down)
• Storage: Included
• Total: ~$24-30/month

Serverless (1-5 ECPUs, avg 1.5, scales to 0 when idle):

• Compute: 1.5 ECPU × $0.125 × 240 hours = $45/month
• Storage: 10 GB × $90 × (240/730) = $296/month
• Total: ~$340-350/month

Winner: Node-based (still cheaper despite 24/7 operation)

Key Insight: Serverless storage costs dominate the pricing equation, making it more expensive than node-based even for intermittent workloads.

Performance Comparison

When to Choose Each Option

Choose Node-based Redis When:

✅ Performance is Critical:

• You need <1ms latency consistently
• High throughput requirements (>500K ops/sec)
• Maximum performance per dollar

✅ Advanced Features Required:

• Lua scripting for complex operations
• Full Pub/Sub implementation
• MULTI/EXEC transactions
• Redis Stack features (Search, JSON, Time Series)

✅ Cost Optimization:

• Predictable, consistent workload
• Large memory requirements (>100 GB)
• Long-running production workloads
• You can capacity plan effectively

✅ Global Requirements:

• Cross-region replication needed
• Global Datastore for multi-region active-active

✅ Full Control:

• Need specific Redis version
• Custom parameter configurations
• Specific instance types for workload

Choose Serverless Redis When:

✅ Variable Workloads:

• Traffic spikes at unpredictable times
• Seasonal or event-driven applications
• Development/testing with sporadic usage

✅ Operational Simplicity:

• Small team without Redis expertise
• Want to avoid capacity planning
• Prefer automatic scaling and management

✅ Small Memory Requirements:

• Dataset < 10 GB
• Storage costs are manageable

✅ Specific Constraints:

• Can work within feature limitations
• Don’t need Lua, complex Pub/Sub, or transactions
• Acceptable latency is 1-5ms

✅ Experimentation:

• Prototyping new applications
• Testing different caching strategies
• Proof of concept projects

Choose Memcached When:

✅ Pure Caching:

• No need for persistence
• Data loss on restart is acceptable
• Only need simple key-value operations

✅ Multi-threaded Workloads:

• Benefit from multi-core CPU utilization
• Large objects being cached

✅ Simplicity:

• Straightforward caching requirements
• No advanced features needed

Migration and Transition Strategies

Migrating from Node-based to Serverless

Preparation:

1. Audit current Redis usage for unsupported features
2. Remove or refactor Lua scripts
3. Update applications to handle slightly higher latency
4. Test with Redis 7.1+ compatible clients

Migration Steps:

1. Create Serverless cache in parallel
2. Dual-write to both caches (temporary)
3. Gradually shift reads to Serverless
4. Monitor performance and costs
5. Decommission node-based cache after validation

Considerations:

• Cannot use replication to migrate (different architecture)
• May need application-level migration strategy
• Watch for storage costs

Migrating from Serverless to Node-based

Reasons to Migrate:

• Storage costs are prohibitive
• Need advanced features (Lua, transactions)
• Consistent high load makes node-based cheaper
• Require cross-region replication

Migration Steps:

1. Provision node-based cluster
2. Application-level dual-write pattern
3. Warm up node-based cache
4. Switch reads to node-based
5. Decommission Serverless

Capacity Planning:

• Use Serverless metrics to size nodes
• Look at peak ECPU and storage usage
• Add 20-30% buffer for growth

Best Practices

For Node-based Redis

Capacity Planning:

# Monitor key metrics
- Evictions: Should be zero or very low
- Memory usage: Stay below 80% to allow for overhead
- CPU utilization: Below 70% for headroom
- Network throughput: Monitor for saturation

High Availability:

• Always use Multi-AZ for production
• Use cluster mode for datasets > 100 GB
• Configure automatic backups
• Test failover scenarios regularly

Performance Optimization:

• Use read replicas for read-heavy workloads
• Enable cluster mode for horizontal scaling
• Choose instance types matching workload (memory vs compute)
• Use connection pooling in applications

Cost Optimization:

• Use Reserved Instances for long-term workloads (save up to 55%)
• Right-size instances based on actual usage
• Consider Graviton-based instances (r7g, m7g) for better price/performance
• Delete unused snapshots
• Use S3 for backup storage (cheaper than ElastiCache backup storage)

Security:

• Enable encryption at rest and in transit
• Use AUTH and RBAC for access control
• Rotate credentials regularly
• Enable VPC security groups and NACLs
• Use IAM roles where possible

For Serverless Redis

Capacity Planning:

• Set appropriate maximum ECPU limits
• Monitor scaling patterns
• Watch for cold start impacts

Cost Management:

# Monitor and alert on costs
- Set CloudWatch alarms for ECPU usage
- Track storage growth
- Consider node-based if costs exceed threshold

Performance:

• Use connection pooling (especially important with scaling)
• Implement retry logic for scaling events
• Cache warm-up strategies for predictable traffic

Feature Workarounds:

# Instead of Lua scripting
# Option 1: Application-level logic
# Option 2: DynamoDB Transactions for complex operations
# Option 3: Migrate to node-based if critical

# Instead of MULTI/EXEC
# Use application-level transactions
# Or consider Amazon DynamoDB for ACID requirements

Real-World Cost Examples

E-commerce Application

Requirements:

• 200 GB cache
• Peak traffic: 500K requests/second
• 99.99% availability
• Cross-region failover

Node-based Solution:

• 10 shards × 3 nodes (primary + 2 replicas)
• Instance: cache.r7g.2xlarge (52 GB each)
• Global Datastore for cross-region
• Monthly Cost:
  • Primary: 30 × $480.96 = $14,428.80
  • Secondary: 30 × $480.96 × 1.3 = $18,757.44
  • Total: ~$33,000/month

Serverless Solution:

• Not recommended: Storage cost alone would be 200 GB × $90 = $18,000/month
• ECPUs for 500K ops/sec would add ~$15,000+/month
• Total: ~$33,000+/month (without cross-region support)

Winner: Node-based (same cost but better performance and features)

Startup API Gateway Cache

Requirements:

• 5 GB cache
• Variable traffic: 100-10K requests/second
• 95% of time at low load
• Development + Production

Node-based Solution:

• Dev: 1x cache.t3.micro = $12/month
• Prod: 3x cache.t3.small = $73/month
• Total: ~$85/month

Serverless Solution:

• Dev: 1 ECPU + 5 GB storage = $541/month
• Prod: 5 ECPU + 5 GB storage = $906/month
• Total: ~$1,450/month

Winner: Node-based (17x cheaper)

Microservices Architecture (10 Services)

Requirements:

• 10 separate caches
• 2 GB each (20 GB total)
• Low-moderate traffic per service

Node-based Solution:

• Option 1: 10x cache.t3.micro = $122/month (separate caches)
• Option 2: 1x cache.m7g.large shared = $107/month
• Total: ~$107-122/month

Serverless Solution:

• 10 caches × (1 ECPU + 2 GB storage)
• 10 × ($91 + $180) = $2,710/month
• Total: ~$2,710/month

Winner: Node-based (22x cheaper)

Decision Framework

Questions to Guide Your Choice

1. What is your data size?

• < 5 GB: Consider Serverless or small node
• 5-50 GB: Node-based typically cheaper
• 50-200 GB: Node-based cluster mode

• 200 GB: Definitely node-based

2. What is your traffic pattern?

• Consistent 24/7: Node-based
• Variable with spikes: Evaluate both (likely node-based still cheaper)
• Intermittent (dev/test): Node-based (t3 instances)
• Truly unpredictable: Consider Serverless (but watch costs)

3. What features do you need?

• Lua scripting: Node-based only
• Transactions: Node-based only
• Basic data structures: Both work
• Pub/Sub (full): Node-based
• Cross-region: Node-based only

4. What is your performance requirement?

• <1ms latency: Node-based
• 1-5ms acceptable: Both work

• 100K ops/sec: Node-based

• <100K ops/sec: Both work

5. What is your team’s expertise?

• Redis experts: Node-based (more control)
• Small team: Serverless (less operations)
• DevOps team: Either works
• No Redis experience: Serverless (easier to start)

6. What is your budget?

• Cost-sensitive: Node-based (almost always cheaper)
• Willing to pay for simplicity: Serverless (if dataset is small)
• Enterprise: Node-based with Reserved Instances

Decision Tree

Start
├─ Need Lua, transactions, or cross-region? → Node-based
├─ Dataset > 50 GB? → Node-based
├─ Consistent high traffic (>100K ops/sec)? → Node-based
├─ Development/test environment?
│  ├─ Intermittent usage? → Node-based (t3.micro)
│  └─ Very sporadic? → Consider Serverless
├─ Small dataset (<10 GB) + variable traffic?
│  ├─ Can tolerate 2-5ms latency? → Evaluate costs (likely Node-based)
│  └─ Need <1ms latency? → Node-based
└─ Default: Node-based (better cost/performance ratio)

Monitoring and Observability

Key Metrics to Monitor

Node-based Redis:

- CPUUtilization: Keep below 70%
- DatabaseMemoryUsagePercentage: Keep below 80%
- EvictionCount: Should be minimal
- ReplicationLag: For read replicas
- NetworkBytesIn/Out: Track bandwidth
- CacheHits vs CacheMisses: Cache effectiveness
- CommandCount: Operations per second

Serverless Redis:

- ElastiCacheProcessingUnits: Current ECPU usage
- BytesUsedForCache: Storage consumption
- CacheHits vs CacheMisses: Cache effectiveness
- CommandCount: Operations per second
- SuccessfulCommandLatency: Performance tracking

CloudWatch Alarms (Examples)

Node-based:

# Memory usage alarm
Metric: DatabaseMemoryUsagePercentage
Threshold: > 80%
Action: SNS notification + consider scaling

# CPU utilization alarm
Metric: CPUUtilization
Threshold: > 70%
Action: SNS notification + evaluate instance size

# Eviction alarm
Metric: Evictions
Threshold: > 100 per minute
Action: SNS notification + investigate memory pressure

Serverless:

# ECPU usage alarm
Metric: ElastiCacheProcessingUnits
Threshold: > 80% of configured maximum
Action: SNS notification + consider increasing max limit

# Storage cost alarm
Metric: BytesUsedForCache
Threshold: Custom (e.g., > 100 GB)
Action: SNS notification + evaluate cost implications

Conclusion

AWS ElastiCache offers multiple deployment options, each with distinct tradeoffs:

Summary of Key Findings

Node-based Redis:

• ✅ Best cost-performance ratio for most workloads
• ✅ Full Redis feature set
• ✅ Predictable costs
• ✅ Maximum performance and control
• ⚠️ Requires capacity planning and management
• ⚠️ Manual scaling

Serverless Redis:

• ✅ Automatic scaling
• ✅ Simplified operations
• ✅ Good for truly variable workloads
• ⚠️ Storage costs are very high
• ⚠️ Feature limitations
• ⚠️ More expensive for most use cases

Memcached:

• ✅ Simple caching
• ✅ Multi-threaded performance
• ⚠️ No persistence or advanced features
• ⚠️ Limited use cases

Final Recommendations

For 90% of use cases: Start with node-based ElastiCache for Redis

• Better cost-performance ratio
• More features and flexibility
• Predictable costs
• Mature and battle-tested

For specific scenarios: Consider Serverless Redis

• True variable/unpredictable workloads
• Small datasets (<10 GB)
• Prototyping and experimentation
• Teams without Redis expertise
• When simplicity trumps cost

For simple caching: Consider Memcached

• No persistence needed
• Simple key-value caching only
• Multi-threaded benefits

Storage Cost Reality Check

The most surprising finding is that Serverless storage costs (~$90/GB/month) are approximately 100x more expensive than the effective storage cost in node-based deployments. This makes Serverless impractical for anything beyond small datasets, even with its automatic scaling benefits.

Cost Optimization Tips

1. Always start with t3 instances for dev/test environments
2. Use Reserved Instances for production (save 30-55%)
3. Consider Graviton instances (r7g, m7g) for 20-40% better price/performance
4. Right-size regularly based on actual metrics
5. Use read replicas instead of larger instances for read-heavy workloads
6. Evaluate cluster mode for horizontal scaling vs vertical scaling
7. Monitor evictions as a signal to scale up
8. Clean up snapshots older than retention requirements

Looking Forward

AWS continues to enhance ElastiCache:

• Redis 7.x features: Improved performance and functionality
• Graviton3 instances: Better price-performance
• Enhanced Serverless: Potential feature additions and cost optimizations
• Integration improvements: Better AWS service integration

Choose based on your specific requirements, but for most production workloads, traditional node-based ElastiCache for Redis remains the most cost-effective and feature-rich option.

Resources and Further Reading

Official AWS Documentation

• ElastiCache for Redis Documentation
• ElastiCache Serverless Documentation
• ElastiCache Pricing
• Best Practices for ElastiCache

Redis Resources

• Redis Commands Documentation
• Redis Data Types
• Redis Persistence

Monitoring and Performance

• CloudWatch Metrics for ElastiCache
• Performance Tuning Guide

Cost Optimization

• AWS Cost Explorer
• Reserved Cache Nodes
• Right Sizing Recommendations

By understanding the constraints, costs, and functionality of each ElastiCache option, you can make informed decisions that balance performance, operational complexity, and budget for your specific use case.