The Tool Desk
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What you need to estimate
Cloud object-storage charges depend on more than the amount of data at the end of a month. Providers price different storage classes, regions and redundancy configurations differently, and colder classes may add retrieval fees, minimum storage periods or other constraints. How often you read data, how many objects you store and where users or processing jobs access it can change the result materially.
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Before entering numbers in a calculator, define the scope of the estimate:
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- Time horizon: the forecast period, such as a representative month and a multi-year projection.
- Service scope: whether you are estimating object storage alone or including access, replication, cataloging, cache, query or other supporting services.
- Retention and copies: deletion or expiry rules, versioning, backups, derived products and replicas.
Without these inputs—and the archive’s capacity, ingest, object-size distribution, request counts, retrieval volume and network destinations—a defensible project-specific dollar total cannot be calculated.
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Build an inventory of the archive
Count the data you will actually store, not just the headline volume of source imagery. Separate raw or source data, calibrated products, analysis-ready data, derived outputs, previews, metadata and backup or replica copies. For each category, record current bytes, file count, size distribution, format and expected retention.
Product structure matters. For example, NASA’s HLS documentation describes cloud-optimized GeoTIFF granules containing core and supplementary layers. A product may therefore involve multiple objects per granule, rather than one file per scene. Use manifests or product catalogs to establish the actual object counts and sizes; do not assume every scene is one uniform object.
Include noncurrent versions if the system retains them. Google Cloud Storage says noncurrent object versions are billed at the same rate as live versions. Record them as additional retained bytes rather than treating versioning as a free safety net.
Forecast average stored capacity month by month
For each month, model opening retained data, new ingest, deletions or expirations, and any additional copies created by processing, versioning or replication. A simple monthly balance is:
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Ending bytes = opening bytes + new bytes retained − bytes deleted or expired + added versions, derivatives and replicas.
Storage charges generally follow the amount held over time, not only the ending balance. Estimate average capacity during each month. When ingest is steady and additions remain stored, a rough approximation is opening volume plus half that month’s net additions. If ingest, deletion or class transitions are uneven, simulate daily or monthly balances instead of relying on that shortcut.
Google Cloud Storage’s pricing examples explicitly use average monthly storage for each class. Apply the selected provider’s stated billing units and currency consistently, and use its current calculator or price table for the region and configuration you have chosen.
Assign storage classes based on access and object size
Map the archive’s bytes into storage classes over time using an access history or an explicit policy. Record when data transitions between classes and whether an archive-tier object must be restored before it can be read. Compare total workload cost, not just the capacity rate: cheaper cold storage can come with retrieval fees, minimum-duration charges, minimum billable sizes, per-object overhead or restore delay.
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| Provider or class family | What to model |
|---|---|
| Amazon S3 Standard and Standard-IA | AWS describes Standard for frequent access and Standard-IA for long-lived, infrequently accessed data. Include the selected class’s current regional rate, operations and applicable retrieval or minimum-duration terms. |
| Amazon S3 Intelligent-Tiering | AWS positions this for unknown or changing access. Include per-object monitoring and automation fees; AWS says the class has no retrieval fees. |
| Amazon S3 Glacier Instant Retrieval | For archive data needing immediate access, account for retrieval terms and the 128 KB minimum billable object size stated by AWS. |
| Amazon S3 Glacier Flexible Retrieval and Deep Archive | These classes require restoration before access. AWS states that each archived object has 40 KB of additional metadata, split between Standard and archive rates; restored copies can also incur temporary Standard storage charges. Include applicable minimum-duration charges if objects are deleted or transitioned early. |
| Google Cloud Storage Standard | No minimum storage duration or retrieval fee is listed in Google’s class table. Operations, network usage and other applicable charges still belong in the model. |
| Google Cloud Storage Nearline, Coldline and Archive | Google’s class table lists minimum durations of 30, 90 and 365 days, respectively, and retrieval fees for all three. Include these terms when modeling reads and early deletion or transitions. |
| Azure Blob Storage access tiers | Use Microsoft’s live calculator for the chosen location, redundancy, access tier, request pattern and capacity band. Its estimation guide describes operation, retrieval, capacity and bandwidth costs; sample figures on that page are not a universal quote. |
Object-size distribution can change a cold-tier estimate. AWS’s stated 128 KB minimum for Glacier Instant Retrieval means objects smaller than that are billed at the minimum size, while the 40 KB Glacier metadata overhead applies per archived object in Flexible Retrieval and Deep Archive. Many small files can therefore produce a different bill than the same total bytes stored in fewer large files.
Google Cloud Storage says changing a class can generate Class A operations. Its documentation also says that, for partial reads, charges can depend on the bytes actually accessed rather than the full object. Model the access pattern the way the provider documents it rather than multiplying every read by the whole object size.
Count access, retrieval and network separately
Estimate billable operations and data movement as separate quantities. Include scheduled cataloging, validation, reprocessing, user downloads and application reads if they issue billable requests.
- Requests: estimate read or GET, list, write or PUT, and other relevant operations by class and month.
- Retrieval and restore: estimate bytes read from each class, partial reads, archive restore volume, restoration duration and how often archive data is accessed.
- Network paths: distinguish ingress, same-region compute access, cross-region transfer, public internet downloads, replication and cache or CDN delivery.
Do not combine these into one generic “transfer” number. Ingress being free does not establish that outbound transfer is free, and replication is not interchangeable with a user download. Region and destination can affect network pricing; include the source and destination for each material flow.
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Add management and supporting-service costs
Depending on the design, the estimate may also need lifecycle management requests, inventory or analytics, intelligent-tiering automation, replication, versioning, soft-delete or retention features, metadata, query services and caches. Include only features the workload will use, but do not omit them merely because the capacity calculator’s headline number looks complete.
AWS describes S3 cost components as storage, requests and data retrieval, data transfer and transfer acceleration, management and insights features, replication, and transform and query features. Google Cloud identifies data storage, data processing—including operations, applicable retrieval and inter-region replication—and network usage. Microsoft’s Azure estimation guide likewise separates capacity tiers, operations, retrieval and bandwidth.
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For each month, calculate each cost line with the exact rate for the selected region, class and configuration:
Monthly total = storage + requests + retrieval and restore + network transfer + replication + management and supporting features.
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For capacity, use the average bytes assigned to each class during the month and the provider’s relevant billing unit. For operations, use the estimated billable request counts. For transfer and retrieval, use the bytes and destinations that actually apply. Add minimum storage-duration obligations, billable-size rules and per-object overhead where relevant. Keep each category visible so a team can explain what drives the forecast.
Then enter the same quantities and geographic assumptions into each provider’s current pricing tool. Compare regional storage rate, redundancy, request charges, retrieval terms, minimum-duration penalties, minimum billable object size, metadata overhead, restore behavior, egress and inter-region rates, and management fees. The processing location also matters: a low storage rate may not make a provider the lowest-cost fit if routine access crosses regions or leaves the cloud.
Google’s listed class minimum durations are specific to its classes; AWS and Azure terms must be checked against the chosen class and configuration. Microsoft’s estimation page was updated on 2025-05-19 and labels displayed figures as sample pricing with regional and configuration context. Do not reuse those examples as current universal prices. There is no directly comparable public cross-provider price established here for one identical EO workload, so a single “cheapest” provider cannot be declared without the project’s assumptions and current rates.
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Build low, base and high cases for the assumptions most likely to change the bill:
- Archive growth and ingest timing.
- Retention period, deletions and the share of data kept in each class.
- Object count and file-size distribution.
- Read frequency, retrieval and restore volume.
- Public egress, cross-region access and replication.
Identify which assumptions move the total most; that is where better measurement or a policy change is most useful. After deployment, compare forecast line items with billing exports and update the model when actual request, retrieval or transfer behavior differs from the assumptions.
Use agency archive totals as scale context, not as a project quote
Large public archives illustrate why ingest and distribution matter, but they do not establish a typical project size or cost per terabyte. The U.S. Geological Survey says its Landsat archive is nearly 16 PB and requires daily management; the accessed Landsat archive page does not state a year for that figure. NASA’s Science Data Portal reported 150,616 TB of total dataset volume, growth of 31,447 TB per year as of 2024, and a projection of 529,750 TB for 2030 across listed NASA Science Mission Directorate divisions; the page notes that the volume excludes duplicative holdings. NASA Earthdata’s landing page, accessed 2026-10-04, says more than 128 PB of Earth science data are available and over 4.5 billion files are distributed, without stating a publication year on that page.
These totals describe agency-scale holdings and services, not the workload, access pattern or bill of an individual Landsat or Sentinel user. To estimate your own archive, use your inventory and workload rather than scaling an agency total.
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