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There is no reliable universal price per terabyte for storing satellite imagery in the cloud. To estimate your bill, model how many bytes you store over time, which storage classes and regions hold them, how often you read or restore them, and how data moves between services or users. Then price storage, operations, retrieval, network transfer, replication, and management features as separate line items using current provider rates.
What information do you need for a useful estimate?
Start with the workload, not a headline price. An archive of a given size can cost different amounts depending on the provider, region, redundancy, object sizes, retention policy, access frequency, and where processing and downloads occur. A storage-only rate does not represent the full monthly bill.
Define the estimate’s scope
Record the provider or providers, region, currency, redundancy configuration, account assumptions, and time horizon. Decide whether the estimate covers object storage alone or also access, transfer, replication, cataloging, query, and cache services. Keep those boundaries consistent when comparing options.
Inventory every copy and product type
Separate source imagery from calibrated and analysis-ready products, derived outputs, previews, metadata, backups, and replicas. For each product, record object count, file-size distribution, format, compression, bands or layers, retention, and expected growth. Do not assume one scene equals one billable object: a product may comprise multiple files and supplementary layers. NASA’s Harmonized Landsat and Sentinel-2 (HLS) documentation, for example, describes cloud-optimized GeoTIFF granules with core and supplementary layers.
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Use manifests, product catalogs, or object listings to build the inventory. File-size distribution matters because some archive classes have minimum billable object sizes or per-object metadata overhead.
Describe how people and systems use the archive
Estimate reads, writes, listings, validation, periodic cataloging, reprocessing, user downloads, and restore activity. Include partial reads if clients retrieve ranges or windows rather than whole objects. Map where compute runs and where data is delivered: same-region processing, cross-region movement, public internet downloads, replication, and cache or CDN paths are distinct flows.
How do you calculate monthly storage capacity?
Model the bytes held during each month
For each month, calculate opening retained bytes, add new ingest, subtract deletion or expiry, then account for versioned copies, derived products, backups, and replicas. Assign each byte to the storage class and region where it resides during that month.
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Use the average stored capacity for the month, not just the end-of-month or end-of-year total. For steady ingest with no other changes, a rough estimate is opening capacity plus half of that month’s net additions. This approximation is less reliable when ingest, deletion, class transitions, or retention expiry happen unevenly; simulate daily or monthly events in those cases. Google Cloud Storage’s pricing examples likewise use average monthly storage for each class.
Calculate each capacity line item
For each region, class, and month, multiply average billable capacity by the applicable rate and billing unit. Apply the provider’s exact rules for object size, metadata, minimum billable size, storage duration, and capacity band. Keep units and currency consistent: decimal TB and binary TiB are not interchangeable.
In a spreadsheet or script, structure the calculation around a row for each month and storage-class/region combination. Keep quantities and rates in separate columns so a reviewer can change an assumption without rebuilding the model.
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Which cloud-storage charges belong in the estimate?
Build the monthly total as separate line items rather than treating capacity as the whole bill:
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- Requests and operations: reads, writes, listings, lifecycle actions, and other billable operations.
- Retrieval and restore: bytes retrieved, archive restores, and any temporary restored copies.
- Network: internet egress, inter-region transfers, and other charged data movement.
- Replication: destination storage plus any applicable replication operations or transfer.
- Management and additional services: inventory, analytics, monitoring, intelligent tiering, versioning or retention features, query services, and caches where used.
Free ingress does not imply free egress. Nor are retrieval, outbound transfer, and replication the same quantity: estimate each path separately.
Include access operations and lifecycle activity
Count operations generated by users and by background jobs. Catalog refreshes, integrity checks, validation, reprocessing, and lifecycle transitions can all contribute to request totals. Google Cloud’s pricing documentation identifies data storage, data processing—including operations, applicable retrieval fees, and inter-region replication—and network usage as separate components. Its examples model capacity, operations, retrieval, replication, and network transfer independently.
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Account for archive-tier rules
Colder classes can reduce the capacity rate but may introduce retrieval charges, restore delays, minimum storage-duration obligations, minimum billable object sizes, or per-object metadata. Include those rules for the selected provider, class, region, and object-size distribution.
For example, AWS states that S3 Glacier Flexible Retrieval and S3 Glacier Deep Archive use 40 KB of additional metadata per archived object, split between Standard and archive rates; S3 Glacier Instant Retrieval has a 128 KB minimum billable object size. AWS also notes minimum-duration charges for certain classes when objects are deleted or transitioned early. Glacier Flexible Retrieval and Deep Archive require a restore before access, and a restored copy can incur temporary Standard storage charges. These are specific S3 class rules, not generic assumptions for every cloud archive.
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Choose a class based on access pattern and operational requirements, not only its capacity rate. The following distinctions are useful starting points; verify current regional terms and rates before committing.
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| Provider and class | Typical fit or documented rule | Cost or access point to model |
|---|---|---|
| Amazon S3 Standard | Frequent access | Compare its capacity charge with expected requests and network use. |
| Amazon S3 Standard-IA | Long-lived, infrequently accessed data | Check retrieval charges and minimum-duration or object-size rules that apply to the selected class. |
| Amazon S3 Intelligent-Tiering | Access patterns that are unknown or change over time | AWS describes per-object monitoring and automation fees and no retrieval fees; include the management charge. |
| Amazon S3 Glacier Instant Retrieval | Archive data requiring immediate access | Include retrieval and the 128 KB minimum billable object size. |
| Amazon S3 Glacier Flexible Retrieval and Deep Archive | Less-frequent archive access with a restore workflow | Model restore timing, retrieval, temporary restored-copy storage, metadata, and applicable minimum-duration charges. |
| Google Cloud Storage Standard | Standard class | No minimum duration or retrieval fee is listed in Google’s class table. |
| Google Cloud Storage Nearline | Colder access tier | 30-day minimum duration; retrieval fees apply. |
| Google Cloud Storage Coldline | Colder access tier | 90-day minimum duration; retrieval fees apply. |
| Google Cloud Storage Archive | Archive class | 365-day minimum duration; retrieval fees apply. |
| Azure Blob Storage access tiers | Compare the tiers available for the chosen account and location | Estimate operations, retrieval, capacity, bandwidth, and account redundancy using the applicable regional configuration. |
For Google Cloud Storage, changing a class can generate Class A operations. Google’s pricing documentation also says charges for partial reads can depend on bytes actually accessed, and noncurrent object versions are billed at the same rate as live versions. Check how versioning and read patterns affect your own model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare Amazon S3, Google Cloud Storage, and Azure Blob Storage?
Give each provider the same workload quantities and geographic assumptions, then compare the resulting line items. A class name or low capacity rate alone is not enough to identify the least expensive fit.
| Comparison dimension | Questions for the estimate |
|---|---|
| Location and redundancy | Are the selected regions, redundancy settings, and processing locations equivalent? Does replication add a second stored copy or cross-region movement? |
| Capacity and billing units | Are capacity bands, units, average-month calculations, and currency aligned? |
| Requests and data processing | Are read, write, list, lifecycle, and other operations counted using the provider’s applicable billing categories? |
| Retrieval and retention | Do access charges or minimum-duration obligations apply, and what happens if data is deleted or transitioned early? |
| Object size and archive mechanics | Do minimum billable sizes, per-object overhead, restore delays, or temporary copies affect the archive’s size distribution? |
| Network paths | What are the costs for public downloads, inter-region movement, replication, and transfer to the processing environment? |
| Management and integration | Which lifecycle, inventory, monitoring, query, or cache features are enabled, and how well does the storage location match the compute location? |
Use live, region-specific pricing tools
Amazon S3 pricing lists storage, requests and retrieval, transfer, management and insights, replication, and transform and query as cost components. Google Cloud’s pricing materials separate storage, processing, and network usage. Microsoft Learn’s Azure estimation guide likewise lays out operations, retrieval, capacity tiers, and bandwidth, and shows that prices differ with redundancy assumptions. Its displayed figures are sample pricing, the page was last updated 2025-05-19, and they should not be treated as a current universal quote.
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For a real comparison, enter the chosen region, redundancy, access tier or class, capacity, request pattern, retrieval volume, and transfer destinations into each provider’s current pricing calculator. For Azure, also consider the selected capacity band and whether reserved capacity is part of the decision. Keep the calculator assumptions with the estimate so the result can be reviewed and updated.
How do you turn the model into a monthly and multi-year estimate?
- Set the scope: write down providers, regions, currency, redundancy, included services, and estimate horizon.
- Build the inventory: group source, processed, derived, preview, metadata, backup, and replica objects; record counts, sizes, formats, and retention.
- Forecast capacity month by month: calculate opening bytes, ingest, expiry or deletion, versions, derived copies, and replicas; then estimate average capacity.
- Assign classes over time: record when objects enter each class and when they transition, expire, or need restoration.
- Forecast usage: count operations, retrieved or restored bytes, partial reads, and transfer by destination and path.
- Apply current rates and rules: use the calculator or pricing table for each exact provider configuration, including billing units and minimums.
- Show monthly line items: report capacity, operations, retrieval, network, replication, and management separately, then sum them.
- Extend the forecast: repeat the monthly model across the chosen horizon rather than multiplying one month’s bill by the number of months when capacity or usage changes.
- Run sensitivities: compare low, base, and high assumptions for growth, retrieval, retention, object size and count, and egress. Identify which changes move the total most.
- Reconcile after deployment: compare forecast categories with billing exports and revise the workload assumptions when observed use differs.
Why don’t public archive totals tell you your project’s bill?
Public figures show the scale and growth pressure of Earth observation archives, but they do not establish a typical project size or a cost per terabyte.
- The U.S. Geological Survey says its Landsat archive is nearly 16 PB and requires daily management; the accessed archive page does not state a year for that figure. It is an agency archive, not a proxy for a new project.
- NASA’s 2024 Science Data Portal reports 150,616 TB in 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, describes more than 128 PB of Earth science data available and over 4.5 billion files distributed; that page does not state a publication year.
These totals underline why ingest, file count, distribution, and retention matter. They do not provide the region, storage-class mix, access workload, or transfer pattern needed to price an individual account.
Quick Recap
What mistakes commonly distort a storage estimate?
- Using ending archive volume for every month instead of accounting for when data arrives and how long it remains.
- Reporting a storage rate as though it were the complete bill.
- Using a rate for the wrong region, currency, redundancy setting, or capacity band.
- Assuming all scenes produce one object or that every object has the same size.
- Omitting small-file effects, archive metadata, minimum billable sizes, lifecycle operations, early deletion charges, or noncurrent versions.
- Combining retrieval, outbound transfer, and replication instead of estimating each path separately.
- Treating an agency-wide archive figure as representative of a project’s data volume or behavior.
- Reusing sample prices as live quotes or applying an example rate without its date and configuration.
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