Managing costs without sacrificing insights is essential for today’s data-driven teams. With Sumo Logic’s Scan Budgets, your organization can better control and predict costs by setting budget boundaries that align with the value of your insights. Get visibility into which queries and dashboards deliver the greatest impact for your business, so you can invest in the insights that matter most while also managing costs when setting up new searches or monitors.

The original premise of observability was to ensure system health, identify issues, and resolve those issues efficiently. As I recently outlined, the legacy approach (sometimes called Observability 1.0 now) relied heavily on metrics and tracing because logs were seen as too noisy or challenging. But, as most forward thinkers have identified now, logs are exactly the telemetry type that we need the most.

We live in an “always on” world, so unplanned outages are more than just inconvenient. They can result in lost revenue, damaged reputations, and, more importantly, frustrated customers. While preventing outages is impossible, the most resilient teams must be prepared with a solid plan, a “technical go bag,” so to speak: a collection of tools, plans, and resources ready to activate at the first sign of trouble.

With remote and hybrid working environments as the norm, organizations need to embrace a modern security paradigm across cross-functional teams. While the primary goal is to deliver confidence, visibility, and robust protection to safeguard their future, balancing the digital transformation journey with budgets can be particularly challenging. Going into budget planning season, these are the challenges to keep top of mind. You can even allocate a line item as you defend your budget and your systems.

If you are moving in the observability circles, chances are that you have heard the phrase “Observability 2.0,” which refers to how we need a new approach to observability. I am incredibly excited about the energy and discussion around a shift to “Observability 2.0,” as we now have a second chance to develop observability the way it was originally envisioned.

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.

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.