65 articles on storage — what drives the cost, how it is priced, and where the savings actually are.
Filestore comes in tiers from Basic HDD to Enterprise, with per-gigabyte prices that vary several-fold and minimum capacities that set a cost floor. Here is how the tiers compare on cost and which fits your file workload.
EBS snapshots are cheap individually but accumulate silently: automated backups, forgotten manual snapshots, and orphaned volumes pile up until snapshot storage rivals or exceeds your live volume cost. Here is how to find and stop the creep.
EBS is block storage attached to one instance; EFS is a shared file system many instances mount at once. EFS costs more per GB but removes provisioning and scaling overhead. Here is how to choose on cost and fit.
S3 has a genuine free tier for the first year, but requests, data transfer out, and storage class choices shape the bill the moment you outgrow it. Here is what the free tier covers and what actually drives S3 cost.
S3 Intelligent-Tiering moves objects between access tiers automatically based on usage, cutting storage cost for data with unknown or changing access patterns, for a small monitoring fee. Here is how it works and when to use it.
S3 lifecycle policies automatically transition objects to cheaper storage classes and expire them on a schedule, cutting storage cost for data with a known access pattern. Here is how to design policies that save without losing what you need.
S3 charges per request, not just per GB stored, so a workload making millions of small GET, PUT, or LIST calls can pay more for requests than for storage. Here is why it happens and how to cut request cost.
S3 offers storage classes from Standard to Glacier Deep Archive, trading storage price against retrieval cost and latency. Matching each object to the right class is a major cost lever. Here is how the classes compare.
Azure Blob storage offers hot, cool, cold, and archive access tiers at very different prices, trading storage cost against access cost and retrieval latency. Matching data to the right tier can cut storage spend dramatically. Here is how.
Object storage bills for stored GB, requests, and data retrieval or egress, with storage classes that trade retrieval cost for lower storage price. Here is how S3, Google Cloud Storage, and Azure Blob compare and how to pick the right tier.
Amazon FSx offers four managed file systems (Windows, Lustre, NetApp ONTAP, OpenZFS), each priced differently for capacity, throughput, and IOPS. Costs vary widely by type and deployment. Here is how FSx pricing works and how to choose the cheapest fit.
Google Cloud Storage has four classes trading storage price against retrieval cost and minimum durations. Standard is cheapest to access, Archive cheapest to store. Here is how the classes are priced and how to pick the right one for each data set.
S3 Glacier Flexible Retrieval, Glacier Instant Retrieval, and Glacier Deep Archive trade storage price against retrieval speed and cost. Deep Archive is the cheapest storage at around a dollar per TB per month. Here is how the cold tiers compare and when to use each.
Logs are cheap to write and expensive to keep forever. A retention strategy that tiers, expires, and archives logs by value cuts storage and ingestion cost without losing what you actually need. Here is how to build one.
DataSync bills about $0.0125 per GB of data copied, plus any underlying storage and transfer at the endpoints. Cost tracks how much you move, so incremental transfers and filters keep it down. Here is the simple model.
Transfer Family bills about $0.30 per hour for each enabled protocol endpoint (SFTP, FTPS, FTP) plus about $0.04 per GB uploaded and downloaded. The always-on endpoint fee is the surprise for low-volume use. Here is the breakdown.
Filestore bills per GB-month of provisioned capacity by tier (Basic HDD, Basic SSD, Zonal, Enterprise), with tier minimums. You pay for the capacity you allocate, not what you use, so tier and size choice drive the bill. Here is how to pick.
Build artifacts, packages, and container images accumulate in storage, billed per GB, and unpruned artifacts grow the bill quietly. Retention policies, pruning, and tiering keep it flat. Here is how.
A data lake's cost is object storage (often huge volumes) plus the query engines that read it. Storage tiering, lifecycle policies, compaction, and efficient formats cut it. Here is how to keep a growing lake affordable.
Azure NetApp Files bills provisioned capacity pool size per hour by service level (Standard, Premium, Ultra), which set throughput per TB. You pay for the pool whether or not it is full, so right-sizing capacity and tier is the lever.
Azure Files bills by tier, transaction-optimized, hot, and cool (pay per GB stored plus transactions), or premium (provisioned capacity with bundled performance). Matching tier to access pattern and using premium only when needed are the levers.
Hyperdisk is GCP's newer block storage that decouples capacity, IOPS, and throughput, so you provision and pay for each independently. This flexibility avoids over-buying, but over-provisioning any dimension wastes money. Here is the model.
Images are often the largest bytes a site serves, so optimizing them, right-sizing dimensions, modern formats like WebP and AVIF, and compression, cuts egress and storage substantially. Here is where image optimization saves cloud cost.
Terraform remote state on S3, GCS, or Azure Blob costs almost nothing, tiny storage plus a few requests per run. The only real cost is state locking (a DynamoDB table on AWS) and, at scale, request volume. Here is the tiny model.