Sumo Logic announced Predict for Metrics.
When combined with existing capabilities in Sumo Logic, Predict for Metrics provides a comprehensive way to harness observability analytics to better predict variable applications, cloud and infrastructure usage and resource demands.
Predict for Metrics is designed to provide better visibility into production issues, system downtime, and uncontrolled cloud costs.
“To keep pace with the speed of modern application development, it is important that operations leaders are able to predict their app and cloud usage needs to keep operations running smoothly and avoid unplanned downtime,” said Erez Barak, VP of Product Development for Observability, Sumo Logic. “Predictive analytics for logs and metrics telemetry provides the key to managing cloud infrastructure and app development variables. Our customers will gain valuable insights to ensure better resilience to avoidable production issues.”
Similar to the existing predict operator for Logs, Predict for Metrics uses linear and autoregressive models to make predictions by harnessing past data points to predict future trends. It is a metrics query language operator, which allows users to visualize forecasted values and add resulting charts to Sumo Logic dashboards. Here are some additional use cases for Predict for Metrics.
Predict for Metrics enables users to:
- Optimize Ingest: Understanding and planning for anticipated volume is important. Administrators can now leverage Predict for Metrics to forecast volume and adjust ingest accordingly to avoid any surprises or disruptions.
- Forecast App Resource Requirements: Sumo Logic customers can use predictive analytics on APM trace metrics to forecast the load on an application or its underlying microservice. They can also forecast potential infrastructure bottlenecks such as how much CPU, memory or disk space to provision across AWS EC2 or AWS DynamoDB instances.
- Reduce Data Bottlenecks: Unplanned resource bottlenecks are a common root cause for application outages. Organizations can now determine which critical resources will run out of capacity, such as provisioned throughput for AWS DynamoDB or Provisioned Memory for AWS Lambda functions and more.
The Latest
Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...
Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...
When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...
In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...
Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...
AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...
Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...
For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...
Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...
Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...