Memorystore Capacity Calculator
Size Memorystore for Redis from your dataset plus fragmentation headroom, then price replicas and high availability.
Inputs
Redis needs spare memory for copy-on-write during snapshots and for the replication backlog.
Standard tier supports up to five read replicas, each billed like another copy of the instance.
Memorystore for Redis prices capacity in GB-hours, with a lower rate on larger tiers.
Memorystore network bandwidth scales with provisioned capacity up to the tier ceiling.
Capacity to Provision
27.0GB
Total Billable Capacity
81.0GB
Monthly Cost
$3,193.02
Billable Copies of the Data
3copies
Network Throughput
324MB/s
Keys the Dataset Holds
5,000,000keys
Cost per Usable GB
$159.65
Failover Behaviour
Automatic failover to the replica in a second zone, typically under a minute
Step by step
Values used
Working dataset size = 20 GB; Headroom for fragmentation and replication buffers = 25 %; Service tier = Standard (replica in a second zone); Additional read replicas = 1 replicas; Price per GB-hour = 0.0540 USD/GB-hour; Hours in the billing month = 730 hours; Capacity step = 1 GB; Network throughput per GB = 12 MB/s; Network throughput ceiling = 16,000 MB/s; Average value size = 4 KB
Memorystore Capacity
provisioned GB = ceil(dataset ÷ (1 − headroom) ÷ step) × step; billable GB = provisioned × copies, where Standard counts 2 copies plus one per read replica; cost = billable GB × GB-hour price × hours.
Capacity to Provision
= 27.0 GB
Total Billable Capacity
= 81.0 GB
Monthly Cost
= 3,193.02
Billable Copies of the Data
= 3 copies
Network Throughput
= 324 MB/s
Keys the Dataset Holds
= 5,000,000 keys
How it works
Redis needs materially more memory than the dataset it holds, because fragmentation and copy-on-write during a snapshot both consume headroom — sizing to exactly the dataset is how instances start evicting keys under load. Every copy is billed, so Standard tier plus one read replica is three times the capacity charge of a Basic instance. A cache that starts evicting keys silently shifts load back onto your database, which looks like a database problem, and the fix is memory headroom rather than tuning. Confirm the GB-hour rate for your capacity tier and region in the Google Cloud pricing calculator, since the rate steps down as instances get larger.
Formula
Memorystore Capacity
provisioned GB = ceil(dataset ÷ (1 − headroom) ÷ step) × step; billable GB = provisioned × copies, where Standard counts 2 copies plus one per read replica; cost = billable GB × GB-hour price × hours.
- headroom
- Spare memory Redis needs for fragmentation, snapshots and the replication backlog
- copies
- Primary plus the Standard-tier failover replica plus any read replicas
- throughput per GB
- Network bandwidth Memorystore grants per provisioned GB, up to the tier ceiling
Frequently Asked Questions
How is Memorystore Capacity calculated?
provisioned GB = ceil(dataset ÷ (1 − headroom) ÷ step) × step; billable GB = provisioned × copies, where Standard counts 2 copies plus one per read replica; cost = billable GB × GB-hour price × hours. Redis needs materially more memory than the dataset it holds, because fragmentation and copy-on-write during a snapshot both consume headroom — sizing to exactly the dataset is how instances start evicting keys under load. Every copy is billed, so Standard tier plus one read replica is three times the capacity charge of a Basic instance.
Why does Memorystore Capacity matter?
A cache that starts evicting keys silently shifts load back onto your database, which looks like a database problem, and the fix is memory headroom rather than tuning. Confirm the GB-hour rate for your capacity tier and region in the Google Cloud pricing calculator, since the rate steps down as instances get larger.
What values do I need to enter?
This calculator takes 10 inputs: Working dataset size, Headroom for fragmentation and replication buffers, Service tier, Additional read replicas, Price per GB-hour, Hours in the billing month, Capacity step, Network throughput per GB, Network throughput ceiling, Average value size. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.
Is a read replica a substitute for high availability?
No. Read replicas scale read throughput; automatic failover comes from the Standard tier. A Basic instance with no replica loses its entire dataset on a node failure and every request falls through to the origin datastore until the cache is warm again.