AlertD this week emerged from stealth to launch a DevOps platform that leverages generative artificial intelligence (AI) to provide deeper insights into Amazon Web Services (AWS) environments by automatically generating dashboards that make it possible to more easily discover and visualize issues.
Fresh off raising $3 million in initial funding, AlertD CEO Geoff Hendrey said via a natural language chat interface the observability platform makes it possible to, for example, generate a report that identifies all the instances of a specific type of workload that might be running much more widely across multiple AWS regions than anyone realizes.
AlertD has also included search capabilities that enable DevOps teams to better understand the AWS metrics they are collecting on a deeper level by using AI to analyze the data that has been aggregated in the dashboard created by AlertD, added Hendrey.
These capabilities eliminate the need for site reliability engineers (SREs) to continually create and maintain dashboards using manual workflows, said Hendrey. While dashboards are tools of the DevOps trade, it’s apparent that they spend far too much time building and maintaining them, said Hendrey.
At a time when the overall complexity of application environments only continues to increase, the AlertD platform ultimately provides DevOps teams with deeper insights that can be visualized in a matter of seconds without having to manually instrument every AWS service without relying on scripts, noted Hendrey.
DevOps teams are then able to filter noise, synthesize complex data across systems, and surface actionable insights that make it simpler to proactively address issues in a way that ultimately democratizes observability, said Hendrey. That approach eliminates much of the toil that prevents many DevOps teams from fully instrumenting their application environments, he added.
The AlertD platform is agnostic when it comes to large language models (LLMs) employed, with interfaces for invoking OpenAI, Anthropic, Meta, and other leading LLM providers provided. DevOps teams can also deploy the AlertD platform on their own AWS Virtual Private Cloud (VPC) to maintain control of their data.
Historically, observability platforms required software engineers to master often arcane programming languages to determine the root cause of an issue. Organizations that lacked the resources to train and hire those engineers, however, tended to continue to rely on monitoring tools to track a set of pre-defined metrics. The issue is that those metrics are not rich enough to enable IT teams to proactively identify issues before they lead to an inevitable disruption. AI tools are now making it possible to not only ask natural language queries to explain how applications are constructed; they can also be used as a front end for invoking a programming tool without having to learn an additional language.
Hopefully, these capabilities will eventually lead to fewer outages as application environments become more resilient. In the meantime, however, DevOps teams would be well-advised to at the very least audit the workloads running in their environments because, if history is any guide, there are a lot more of them than anyone responsible for managing than many may care to admit.
