Microsoft Corp. is making it easier to keep data on its Azure cloud for extended periods of time.

Today the technology giant introduced a new storage tier for the cloud platform that provides an affordable way of archiving information. Dubbed Archive Blob Storage, it targets companies that seek to reliably store large volumes of low-priority but important records such as call logs. This type of data often needs to be kept around for years for legal and other operational requirements.

Until now, the only native archiving option in Azure was the Cool Blob Storage tier that Microsoft rolled out last year. It’s much cheaper than the standard plan, but often not as economical as rival offerings from Amazon Web Services Inc. and Google Inc.’s cloud platform. That’s because Cool Block Storage focuses on the subset of archiving use cases which require the ability to quickly pull up records when they are needed, a feature that comes at a premium.

Archive Block Storage trades off access speed for affordability. Retrieving a dataset takes as much as 15 hours from the moment the request is made. In exchange, companies are charged only 18/100th of a cent per stored gigabyte per month, compared with 1 cent on Cool Blob Storage.

The new archiving tier will enable Microsoft to compete for the same extended range of storage use cases as its top cloud competitors. More importantly, it means that companies can now keep all their datasets on Azure in a way that meets their respective cost requirements, which should make the platform as a whole more appealing.

The latter point is particularly important since firms are storing more and more of their records for analytics purposes. Even data as mundane as call logs can be put to use, for example by helping salespeople draw lessons from past conversations with prospects. Such information is handy for training machine learning and deep learning models.

Offering more storage tiers is not the only way Microsoft hopes to address these trends. Just yesterday, the company revealed plans to make a new class of processors available through Azure that are specifically optimized for deep learning workloads.

Image: Microsoft