MemoryDB delivers microsecond latency for real-time workloads by providing a fully managed, Redis-compatible, in-memory database. As with any cloud service, its pricing model is key to understand before adopting it. This guide covers MemoryDB's pricing structure to help you estimate and optimize costs.
Factors That Influence MemoryDB Pricing
MemoryDB pricing is primarily influenced by:
- Node Type - The EC2 instance type used for MemoryDB nodes. Larger instance types cost more.
- Node Hours - Duration for which node runs. Billed per hour.
- Number of Nodes - Total nodes in the MemoryDB cluster.
- Data Written - GB of data written to MemoryDB daily.
- Snapshot Storage - GB of snapshot data stored in S3.
- Data Transfer - Data transferred between services and out to internet.
Let's look at each factor more closely.
Node Types and Hourly Cost
MemoryDB offers a range of EC2 instance types optimized for memory or general purpose use. Larger instance types have more vCPUs, memory, and network bandwidth. Hourly price varies based on instance size.
For example, an r6g.xlarge node with 4 vCPUs and 26GB memory costs
You pay per node hour based on provisioned cluster capacity, whether fully utilized or not.
Number of Nodes
MemoryDB pricing is node-based. Your total hourly cost is the node hourly price multiplied by number of nodes.
A 2 node cluster of db.r6g.xlarge at
So node count along with instance size drives the total compute costs.
Data Written
You pay per GB for data written to MemoryDB daily above the free tier. Writes within a region are free for the first 5GB per day.
For example, writing 10GB per day above the 5GB free tier at
Snapshot Storage
Automatic daily snapshots and any manual snapshots are stored in S3. The first snapshot copy each day is free. Additional storage is $0.021 per GB-month for US regions.
Storing 50GB of snapshot data for 10 days a month would cost around
Data Transfer
Data transfer between services is free in the same region. Transferring snapshot data to other regions incurs standard AWS data transfer rates, which depends on source and destination locations.
Usage Examples and Estimates
Here are some examples to illustrate MemoryDB node-hour and data costs:
// 2 Node Cluster of r6g.xlarge in US East
Nodes: 2
Node Type: r6g.xlarge
Node Hourly Price: $0.812
Monthly Node Hours: 730 (24 * 365 / 12)
Node Cost = Nodes * Node Hourly Price * Monthly Hours
= 2 * $0.812 * 730 = $1,169/month
// 10GB Daily Write Volume
Data Written: 10GB
Data Written Unit Price: $0.20/GB
Daily Write Cost = Data Written * Data Written Unit Price
= 10GB * $0.20/GB = $2
Monthly Data Write Cost = Daily Cost * 30 = $2 * 30 = $60/month
Total Monthly Cost = Node Cost + Data Write Cost
= $1,169 + $60 = $1,229
Based on your node performance needs, write throughput, and snapshot storage, you can estimate overall MemoryDB costs.
Comparing MemoryDB Pricing with Other Databases
When it comes to database selection, understanding the pricing models is just as important as evaluating their technical capabilities. For those considering MemoryDB, we'll break down its cost relative to other popular databases and analyze its price-performance ratio.
Comparison with Traditional SQL Databases
Traditional SQL databases, like MySQL or PostgreSQL, have been a mainstay for developers due to their robustness and proven durability. These databases typically follow an instance-based pricing model. That means you're charged based on the size of the computing capacity (CPU and memory) and storage you select. Additional costs can also include data transfer and backup storage.
MemoryDB, on the other hand, uses a slightly different approach. Pricing is largely driven by the amount of memory provisioned – reflecting its purpose as an in-memory database. It's important to note that while both traditional SQL databases and MemoryDB have licensing fees, MemoryDB's are often higher due to its superior performance characteristics.
# Example: Cost Calculation for MemoryDB and MySQL
memorydb_cost = total_memory_gb * memorydb_price_per_gb
mysql_cost = instance_size_cost + storage_gb * storage_price_per_gb
Comparison with Other In-Memory Databases
When comparing MemoryDB to other in-memory databases such as Redis or Memcached, one must look beyond just the raw price per GB of memory. Many in-memory databases charge based on a combination of memory size and network throughput.
MemoryDB stands out in this competition because it provides automated management features - like backups, fault tolerance, and data partitioning - that many other in-memory databases do not offer by default. The cost of these additional services may appear to make MemoryDB more expensive upfront, but they can save considerable expense in terms of time and resources over the life of your project.
# Example: Cost Calculation Comparison Between MemoryDB and Redis
memorydb_cost = memory_provisioned_gb * memorydb_price_per_gb
redis_cost = redis_memory_gb * redis_price_per_gb
Analysis of Price-Performance Ratio
The real value of any database isn't just what it costs, but what it delivers for that cost. This is commonly referred to as the price-performance ratio. An optimal price-performance ratio means you're getting the most bang for your buck.
MemoryDB, being an in-memory database, offers exceptional performance for high-throughput, low-latency workloads. While its unit cost may be higher than a traditional SQL database or even some other in-memory databases, when adjusted for performance - especially for read-heavy or latency-sensitive applications - MemoryDB can often outperform these alternatives.
To calculate price-performance, take the total cost of running the database and divide it by the number of transactions it can handle per second (TPS). Less cost per transaction indicates a better price-performance ratio.
# Example: Price-Performance Ratio Calculation
memorydb_pp_ratio = memorydb_cost / (transactions_per_sec * 60 * 60 * 24 * 30)
mysql_pp_ratio = mysql_cost / mysql_tps
In conclusion, while MemoryDB might seem more expensive at first glance, its price-performance ratio can make it a very cost-effective option, particularly for applications demanding top-tier performance.
Strategies to Optimize MemoryDB Costs
Here are some strategies that can help optimize MemoryDB costs:
Effective Data Management
As an in-memory database, optimizing data usage is key for MemoryDB cost savings:
- Use appropriate data structures - hashes vs lists, strings vs json
- Set expiration times for temporary keys
- Prune unnecessary data
This minimizes memory footprint and storage costs.
Scaling Strategies
MemoryDB allows scaling cluster capacity on demand:
- Enable auto-scaling to match workload needs
- Use sharding to divide data across smaller clusters
- Scale down during non-peak hours if workload fluctuates
Right-sizing cluster capacity is essential for cost optimization.
Reservations and Savings Plans
AWS offers options to reserve capacity and usage for discounts:
- Savings Plans offer flexible 1-3 year commitment for usage discounts
- Reserved Instances provide significant discounts for steady-state workloads
Combining scaling flexibility with reserved capacity can optimize MemoryDB costs.
Final Thoughts
MemoryDB provides a performant managed in-memory database optimized for real-time workloads. Its node-based pricing model allows predictable cost estimates based on instance size, cluster scale, and workload patterns. Accounting for all pricing dimensions and optimizing usage helps minimize MemoryDB expenses.
Frequently Asked Questions
What happens if I exceed my allotted resources on a MemoryDB plan?
Your app will start seeing errors if you go over the memory size or connection limit. You will need to upgrade to the next size plan to resolve.
Can I change (upgrade or downgrade) the MemoryDB plan at any time?
Yes, you can instantly change plans without downtime at any time based on your application's needs.
How does MemoryDB pricing compare to AWS ElastiCache?
MemoryDB starts at
What are the pricing differences between MemoryDB and self-managed Redis?
MemoryDB eliminates server management costs but has monthly pricing for the service. Self-managed Redis avoids monthly costs but requires maintaining your own servers and infrastructure.
How does MemoryDB compare to other fully managed services like Redis Labs Cloud?
Redis Labs Cloud has comparable pricing to MemoryDB for basic plans. But Redis Labs offers more flexibility like custom databases and cross-cloud support. MemoryDB is simpler to use but has fewer advanced configurations.
How does MemoryDB pricing compare to in-memory databases like DynamoDB?
DynamoDB pricing follows pay-per-request model rather than MemoryDB's fixed monthly pricing. It may be more cost efficient at very low usage levels. MemoryDB provides specialized Redis data structures compared to DynamoDB's key-value store.
