Abstract: In various embodiments, a process for determining metrics including resource expenditures of a digital service includes discovering a plurality of configuration items of a computing infrastructure. The process includes identifying a subset of the plurality of configuration items utilized to provide a digital service, obtaining a plurality of resource expenditures respectively associated with at least a portion of the plurality of configuration items, and associating a subset of the plurality of resource expenditures with the subset of the plurality of configuration items. The process includes aggregating the subset of the plurality of resource expenditures to generate a metric of the digital service.

Abstract: Methods are provided for leveraging generative artificial intelligence to generate commands and other aspects of modification actions that can be used by users to create, delete, and/or modify virtual machines in a cloud computing environment or to interact with aspects of some other computing environment. The generation and execution of such modification actions can implicate extensive computational and other requirements and may also require the performance of multiple tasks requiring differing levels of access credential. For example, updating a database to reflect changes made to a computing environment by execution of a modification action may require a higher level of credential than performing the changes themselves. The action generation and execution methods described herein allow users with such lower-level credentials to enact such changes while also performing associated database updates or other higher-credential actions.

Abstract: An example embodiment may involve determining, based on a predefined policy, one or more configuration settings, wherein the predefined policy specifies a permitted value of a key within the configuration settings; obtaining, from a cloud-based service provider, an actual value of the key; determining, by applying the predefined policy, that the actual value of the key does not match the permitted value of the key; identifying a remediation for the key that includes determining an updated value of the key, wherein the updated value of the key satisfies the predefined policy; and causing the updated value to be applied to the key in the cloud-based service provider.

Abstract: In various embodiments, a process for determining metrics including resource expenditures of a digital service includes discovering a plurality of configuration items of a computing infrastructure. The process includes identifying a subset of the plurality of configuration items utilized to provide a digital service, obtaining a plurality of resource expenditures respectively associated with at least a portion of the plurality of configuration items, and associating a subset of the plurality of resource expenditures with the subset of the plurality of configuration items. The process includes aggregating the subset of the plurality of resource expenditures to generate a metric of the digital service.

Abstract: An example embodiment may involve determining, based on a predefined policy, one or more configuration settings, wherein the predefined policy specifies a permitted value of a key within the configuration settings; obtaining, from a cloud-based service provider, an actual value of the key; determining, by applying the predefined policy, that the actual value of the key does not match the permitted value of the key; identifying a remediation for the key that includes determining an updated value of the key, wherein the updated value of the key satisfies the predefined policy; and causing the updated value to be applied to the key in the cloud-based service provider.

Abstract: Persistent storage may contain a definition of a user interface that includes a plurality of inputs, a specification of a graph of dependencies between pairs of inputs in the plurality of inputs, and collected data representing previously-submitted combinations of the plurality of inputs and corresponding indications of successes or errors. One or more processors may be configured to: transmit, to a client device, a representation of the user interface; receive, from the client device, a set of input values corresponding to the plurality of inputs; determine that the set of input values, in combination, results in an error; based on the collected data and the graph, calculate likelihoods respectively corresponding to one or more of the input values, wherein the likelihoods are estimates that the input values corresponding thereto caused the error; and transmit, to the client device, at least one of the likelihoods.