Amazon CloudWatch is a great tool for DevOps engineers, developers, SREs, and other IT personnel who require basic Amazon Web Services (AWS) log processing and analytics for cloud services and applications deployed on AWS. However, most developer teams will ultimately need more logging functionality than a basic AWS log analyzer like Amazon Cloudwatch can provide. For example: That’s why, although CloudWatch may be one tool in your log analytics strategy, it probably should not be the only one.

Sumo Logic Mo Copilot is a natural language assistant that helps first responders derive insights from logs and resolve issues faster using contextual suggestions and plain English queries. It has been in preview since May 2024 with dozens of customers. Choosing a foundation model was a critical step in its development. Let’s explore our high-level requirements for Copilot, the role of foundation models and the rationale for standardizing on Amazon Bedrock.

Today’s IT and security environments have gone from “big” to “massive” in just a decade or two—endpoints have practically exploded (think hundreds of thousands of servers, not just a hundred). Add in a dizzying array of data types and vendors, and what do you get? A whole lot of chaos. So why, oh why, does agent management still feel like it’s stuck in the early 2000s?

Logz.io introduces its AI Agent in Beta, using GenAI to revolutionize observability. The AI Agent simplifies monitoring with automated data analysis and root cause detection, accelerating issue resolution by 3-5x for beta users—marking a critical step toward fully autonomous observability.

Ensuring scalability, performance, and cost-effectiveness is a constant challenge for cloud-native log management and observability. At Sumo Logic, we faced this challenge head-on by transitioning from a stateful, Lucene-based architecture to a completely stateless, Parquet-based architecture. This transformation lets us improve data storage efficiency, streamline operational complexity, and meet the demands of an ever-increasing data scale.

Imagine you’re the protector of a castle. Your walls are tall, the gates are strong, and the guards are well-trained. But what if an intruder was still able to slip past your defenses? Even with the best security tools, not every threat will be caught. Threat hunting is the proactive approach to finding attackers that might have bypassed your defenses.

Autonomous observability for system monitoring and management aims to use GenAI and machine learning to automatically detect, diagnose and resolve issues. In conversations about cloud observability today, discussions often shift from “what’s possible” to “what’s practical.” Too often, these conversations highlight the shortcomings of current observability processes, tools and financial models.

Log Analysis has been a beneficial practice for organizations for numerous years, and over these years it has continuously evolved. This has been in part driven by the increasing volume of logs that companies are required to monitor. Now, log analysis is shifting again, incorporating machine learning (ML) and artificial intelligence (AI) to assist data analysts in identifying system log patterns and anomalies.

Standardized logging formats are important for teams that rely on logging for observability, troubleshooting, and workflow integration. Using structured formats simplifies parsing and eliminates the need to interpret fields manually, ensuring consistency across logging formats. This reduces manual work, prevents brittleness from unstructured logs, and simplifies integration between teams that feed logs into a shared aggregation system.

Cribl’s recent $319M Series E round marks a significant milestone in our journey to becoming a generational company. While this growth opens the door to new opportunities for our company, it also presents a challenge: how do we ensure our amazing culture scales alongside the business? At Cribl, we believe in Culture on Purpose—an intentional, values-led approach to evolving our culture as we grow.