Estimating True Egress Costs: Wasabi Hot Storage Economics for WA Data-Heavy Workloads

Organisations across Western Australia's mining and resources sector accumulate enormous volumes of geospatial data, high resolution imagery, drone survey outputs, and seismic archives. These datasets frequently reach hundreds of terabytes, and they rarely sit still. Teams retrieve historical imagery for comparison, feed archives into machine learning pipelines, and share large files with joint venture partners and regulators. Understanding the full cost of storing and moving this data is essential to setting a predictable storage budget rather than absorbing recurring surprises on a monthly invoice.

The headline that draws attention to storage providers such as Wasabi is the absence of egress fees, which contrasts with the metered data transfer charges common to hyperscale platforms. That difference matters, but it is only one variable in a wider equation that includes storage rates, minimum retention periods, retrieval latency, and the operational cost of managing a second platform alongside an existing Microsoft estate. This article examines how to model these factors honestly so that storage placement decisions rest on total cost and workload behaviour rather than a single advertised figure.

Where egress costs actually accumulate

Egress, meaning data transferred out of a cloud region to the internet or to another network, is charged per gigabyte on most hyperscale platforms including Azure. For a workload that writes data once and reads it rarely, egress is a minor line item and the storage rate dominates. For a workload that repeatedly pulls large imagery sets back into processing environments, distributes files to external partners, or serves data to applications outside the storage region, egress can grow into a substantial and unpredictable component of the monthly spend.

The practical difficulty is that egress volume is often the hardest quantity to forecast. Storage capacity grows in a broadly linear and predictable fashion, but retrieval patterns depend on project cycles, exploration activity, and analytical demand that vary through the year. When modelling a data-heavy archive, it is worth separating the two questions clearly: how much will it cost to hold the data at rest, and how much will it cost to move the data given realistic read and distribution patterns. A provider with no egress charges removes the second variable entirely, which is valuable precisely because it converts an uncertain cost into a known one.

Comparing against Azure Blob tiers

Azure Blob Storage offers several access tiers designed for different retrieval profiles, including hot, cool, cold, and archive. The hot tier carries the highest storage rate but the lowest access charges and is suited to frequently read data. The cool and cold tiers reduce storage cost in exchange for higher access charges and minimum retention periods, while the archive tier offers the lowest storage cost of all but requires rehydration before data can be read, a process that can take hours depending on the retrieval priority selected.

This tiering model rewards accurate classification of data by how often it is accessed. Genuinely dormant archives placed in the archive tier can be very economical to hold, provided the organisation accepts the retrieval delay and the associated rehydration and egress charges when data is eventually needed. The risk arises when data assumed to be cold turns out to be warm in practice, because frequent retrieval from a cool or archive tier can erode or reverse the storage saving. A flat rate, single tier model with no egress charges, such as the one Wasabi markets, trades away the deep discount available on truly dormant data in return for simplicity and freedom from access based surprises.

Building a defensible cost model

A sound comparison begins with measurement rather than assumption. Establish the current stored volume, the monthly growth rate, and, most importantly, the historical egress volume drawn from existing billing data or storage logs. With those three figures, you can project the annual cost of each option: for Azure, the storage rate for the chosen tier plus expected egress and transaction charges; for a flat rate provider, the storage rate alone, adjusted for any minimum storage duration policy. Wasabi, for example, applies a minimum storage retention period, so short lived data may be billed for longer than it is actually held, and that condition should be built into the model.

The decision rarely resolves to a single platform for all data. Many organisations find that a hybrid placement serves them best, retaining active working sets and integration points within Azure where they sit close to Microsoft analytics, identity, and security tooling, while directing large, stable, frequently retrieved archives to a no-egress provider. Governance considerations also belong in the model, including data sovereignty, the location of the storage region, encryption and access controls, and the effort required to operate and secure a second platform. When these operational factors are weighed alongside the raw storage and egress arithmetic, the resulting placement decision is both financially defensible and aligned to how the data is genuinely used.

For resources sector teams in particular, the value lies in replacing an uncertain and variable egress exposure with a budget that can be forecast with confidence. That predictability supports better project planning, cleaner internal cost allocation, and clearer conversations with finance about the true cost of retaining decades of imagery and survey data. The objective is not to declare one platform superior in the abstract, but to match each class of data to the storage model whose economics fit its access behaviour.

References

• Azure Blob Storage access tiers overview
• Azure Blob rehydration from the archive tier