Metricly is a machine-learning adaptive monitoring solution that helps organizations monitor cloud services, applications, infrastructure, and public cloud costs. Metricly’s advanced machine learning algorithms learn the behavior and workload patterns of your environment to optimize your resource allocation, reduce your cloud spending, and identify performance anomalies that matter to your business.
Metricly Solves Real-World Problems:
- AWS Cost: The AWS Cost integration allows Metricly to analyze your billing data as well as your performance data. On average, our cost report recommendations identify 32%+ in actionable savings in your AWS bill without increasing performance risk.
- Cloud Monitoring: Apply machine learning and analytics when monitoring your cloud infrastructure and web application performance. Our multi-conditional alerting powered by anomaly detection can detect all of your creeping performance problems before your users do. Our pre-configured alerts avoid the need for manual configuration and evaluate multiple conditions simultaneously to detect only real operational problems. Send graphical notifications via PagerDuty, Slack, Webhook and more.
Get Smarter Monitoring & Lower Cloud Costs. Monitor and manage your AWS performance, capacity and costs - together!
In the past, users had to manually end maintenance mode for elements in their inventory. Now, you can set a maintenance duration that expires on its own! This means you won’t have to worry about remembering to re-activate your element monitoring. It also saves you quite a few clicks in the UI.
Now you can setup (and update) SSO from the UI! It’s easy and takes only minutes.
Metricly’s Windows Agent now offers support for HTTP Checks. You can configure HTTP checks to send an HTTP GET request to a URL; if a successful response is returned, then a check is sent to Metricly.
Metricly, a SaaS-based cloud monitoring service built on a machine learning platform, today announced a $9 million Series A round of financing led by Rembrandt Venture Partners and including Bowery Capital.
In this article, I’ll be sharing AWS tagging best practices, more information about why tagging is a process your organization needs and how to implement a strategy which meets the requirements of the organization without placing an undue burden on development teams.
Is your performance monitoring using real-time analytics in a way that will produce results or frustration? Real-time analytics can improve the value of performance monitoring by enabling operations teams to pinpoint problems faster and proactively manage applications. If not used correctly or completely, analytics can make or break your monitoring solution.
Get this 12-page essential guide to help you navigate the challenges of cloud monitoring.
When is it appropriate for static thresholds to operate on their own? When should operations teams utilize them as baselines within anomaly detection solutions leveraging machine learning algorithms? Read on to find out.