Database Storage, RAM & IOPS Sizing Calculator

A database sizing calculator turns a schema and a traffic estimate into the three numbers that determine which instance class you need: storage, memory, and IOPS. Teams usually estimate the first by multiplying rows by bytes per row and stop there — a figure that is typically off by several multiples once the things that actually consume capacity are included.

Storage starts with raw row data but grows immediately from there. Indexes commonly add anywhere from a quarter to well over the size of the table itself, depending on how many you maintain and how wide the indexed columns are. Growth compounds monthly, so provisioning for today guarantees a migration soon; the calculator applies your monthly growth rate across the months of headroom you want. Replicas multiply the whole figure, because a three-node cluster stores three full copies. Memory is sized differently and matters more: databases perform acceptably when the frequently-accessed working set fits in the buffer pool, and fall off a cliff when it does not. The calculator takes your working-set percentage and adds a margin for connections, sort space, and query execution. IOPS comes from peak queries per second multiplied by reads per query, plus writes multiplied by a write amplification factor — because a single logical write becomes several physical ones once the write-ahead log, index updates, and replication are counted.

The most consequential of the three is RAM, and it is the one most often under-provisioned. A working set that fits in memory means most reads are served without touching disk; a working set slightly too large means a meaningful share of queries fall through to storage, and both latency and IOPS requirements jump by an order of magnitude. This is why a database can perform well for months and then degrade sharply as it grows past its buffer pool — the failure is not gradual. Write amplification is the second common surprise: a factor of three to five is typical for a replicated engine with several indexes, so a workload that looks like a thousand writes per second can present tens of thousands of IOPS to the storage layer. The source research recommends sizing from workload shape, indexes, growth, replicas, and provider price tables, and that remains the right frame — use this to establish requirements, then map them onto a specific provider's instance and volume pricing. This estimates capacity only and excludes backup storage, connection pooling, and query optimization, all of which can change the answer substantially.