Azure Storage Discovery now offers advanced capacity insights

We are excited to announce the preview of advanced capacity insights in Azure Storage Discovery. Azure Storage Discovery provides enterprise-wide visibility into storage usage, activity, egress, and configuration across your data estate. Advanced capacity insights extend this visibility with deeper analysis of data access, age, tier, composition, and object size, along with the ability to drill down to the accounts, containers, and prefixes behind key trends.

As organizations generate and retain more data to power AI, analytics, and data-intensive applications, storage environments continue to grow in both scale and complexity. Azure Storage Discovery continues to expand the visibility customers have across their data estates, helping teams answer increasingly complex questions: Which data is driving growth, and how is it being used? How much capacity is tied up in inactive data? Are billions of small objects creating operational inefficiencies? Which accounts, containers, or prefixes deserve attention first?

Building on the estate-wide insights available in Azure Storage Discovery, advanced capacity insights provide a more granular view into how data is stored and used. Pre-built visualizations help you identify patterns in data age, access tier, storage composition, object-size distribution, and retention. You can also investigate the accounts, containers, prefixes, and tags contributing to these patterns. Together, these experiences help you move from an estate-wide trend to the specific resources that warrant further investigation.

Advanced capacity insights are now included in your Azure Storage Discovery Standard plan at no additional cost. Just enable ‘Capacity details’ to get started!

Find inactive storage and optimize cost

Keeping an eye on the growth of unused data is a critical part of data management, yet pockets of older or inactive data can easily go undetected. Last access time provides a direct signal into how recently data has been used, helping you identify where inactive data is accumulating and how it is distributed across storage tiers. These insights can inform tiering, archival or cleanup decisions and help you determine where lifecycle management policies or intelligent tiering capabilities such as Azure Storage Smart Tier can deliver the greatest value.

The Size by tier and recency chart combines storage tier information with data recency signals such as last access time (creation time and last modified time are also supported) to provide a comprehensive view of data placement across your storage estate. These insights help storage administrators validate lifecycle management policies, evaluate Smart Tier adoption opportunities, and identify data that may be better suited for a different storage tier, enabling more informed decisions around performance, cost, and long-term data management.

Example question: How much data in the Hot tier has not been accessed for an extended period, and which storage accounts or containers contribute most to it?

Understand how much storage is live, soft-deleted, versioned, or snapshot data

Within the Capacity details report, the Storage composition chart helps you understand what is consuming your storage footprint by breaking capacity into live data, previous versions, snapshots, and soft-deleted objects.

Combined with Top 10 items, you can quickly identify the accounts, containers, or prefixes contributing most to growth and focus your investigations where they will have the greatest impact.

To focus on the resources that matter most, you can apply filters such as redundancy type, tags, subscription, region, performance type, container, and prefix. Once you identify an area of interest, the View resources experience lets you drill down to container-, prefix-, and tag-level details to pinpoint the resources behind a trend. Instead of treating every byte the same, teams can understand the type of data driving storage usage and decide where to take action.

Identify workloads dominated by small or large objects

Storage capacity alone does not tell the full story of how data is being used. For example, some storage workloads contain large numbers of small metadata objects, often less than 10 KiB, while others are dominated by large media objects greater than 100 MiB. In both cases, total object counts and average object size reveal little about the underlying distribution. Understanding how objects are distributed across size ranges provides a clearer view of workload characteristics and can help teams assess performance and API costs.

The Size-bucket distribution chart helps visualize how data is distributed across object size ranges and storage tiers. By comparing object counts with capacity consumption, storage administrators can identify workloads dominated by small objects, uncover opportunities for compaction, and better understand how object distribution impacts operational efficiency and storage costs. Once a pattern is identified, users can drill down to determine which accounts, containers, and prefixes contribute most and prioritize optimization efforts.

Once you identify a pattern, you can drill down to the accounts, containers, and prefixes contributing most to the behavior, helping teams move from observation to targeted optimization without building custom reports or analysis pipelines.

Understand retention costs and risks before taking action

Deleting, moving, or re-tiering data without understanding retention requirements can lead to unexpected charges, compliance risks, or data becoming unavailable sooner than intended. Data may still be subject to minimum retention periods, and deleted data can continue consuming billable storage long after it has been removed from applications.

The Minimum retention analysis chart helps identify data that may incur early deletion charges if moved, re-tiered, or deleted today, allowing teams to assess potential cost impacts before making lifecycle changes.

The Soft-deleted retention chart provides visibility into deleted data that remains recoverable and continues consuming storage capacity, helping you understand how much space is tied up in recoverable data and when it will expire.

Drill down from the estate to the resources that matter

Advanced capacity insights help you move from an estate-wide pattern to the specific resources behind it. With View resources, you can drill into the accounts, containers, prefixes, and tags contributing to an insight and combine dimensions such as access time, tier, storage composition, and retention to narrow your investigation.

For example, suppose the Size by tier and recency chart shows that a significant amount of data in the Hot tier has not been accessed in more than 90 days. From the corresponding View resources, you can identify the storage accounts with the highest volume of this data. You can then drill into those accounts and filter or sort by container or prefix to pinpoint where the data is concentrated. Additional signals, such as storage composition, can help you understand whether that data consists of live data, previous versions, snapshots, or soft-deleted data.

To explore Azure Storage Discovery, open a Discovery workspace in the Azure portal and enable Capacity details. Use Capacity overview to understand overall growth and concentration, then use Capacity details to investigate the access tier, age, object-size distribution, and composition of the data behind the insights.

Learn more: Azure Storage Discovery overview | Understand Storage Discovery reports

We would love to hear your feedback. Which capacity insights help your organization prioritize investigations, validate optimization opportunities, or move from insight to action with more confidence? Contact us at StorageDiscoveryFeedback@service.microsoft.com.