Sumo Logic nnounced the general availability of Sumo Logic Reliability Management.
Reliability Management enables developers, SREs, and DevOps teams to manage the reliability of their mission-critical apps by adopting a Service Level Objective (SLOs) methodology.
Reliability Management is a new capability of Sumo Logic Observability that helps organizations adopt a fundamentally better approach to measure and improve the reliability of distributed applications. This approach focuses on the reliability of the application from the end user perspective instead of monitoring and alerting across all of the infrastructure, services and application components involved in the response.
“Sumo Logic Reliability Management shifts the focus on reliability from underlying technology components towards the user experience. It enables connecting business concerns with technology behavior. By facilitating alignment between app owners and developers, Reliability Management essentially serves as the master plan required to manage at the business level versus the signal level,” said Erez Barak, VP of Product Development for Observability, Sumo Logic. “Organizations can now find the optimal cadence to balance innovation velocity with service reliability and turn SLO metrics into actionable insights to achieve their performance promises to customers.”
“Some customers are using SLOs to support the journey to the cloud to understand the risk of their transformation better. Now, with this level of detail from Sumo Logic, organizations can not only see where they are stressing infrastructure - they can see the forest for the trees,” said Torsten Volk, Managing Research Director, EMA Research. “You can stop caring about a Kubernetes node misbehaving if it has no impact on your users. You can look at your system outside in. Teams can now focus on what matters and avoid burnout. This is a much-needed piece to gain true observability.”
Sumo Logic Reliability Management also adopted slogen. slogen is built on the OpenSLO standard and uses automation to minimize the effort needed to measure and set SLOs. Sumo Logic customers get the choice of defining SLOs either in Open Source specification or in Sumo Logic.
“We are not reinventing the standard. We are getting behind the OpenSLO standard and have continued our investment in developing this standard for the community,” continued Barak. “With an open solution for service level management, we’re enabling organizations to future-proof their SLOs.”
Sumo Logic Reliability Management helps organizations to be more proactive in delivering digital services by:
- Empowering leaders to balance innovation with service reliability: Sumo Logic Reliability Management provides real-time reliability and performance metrics to power data-driven decision-making. Leaders also gain proactive alerts on SLOs and error budget consumption.
- Delivering a simple, open, and secure approach to service level management: Built on the OpenSLO standard, Reliability Management enables teams to monitor SLIs based on existing Sumo Logic queries. When combined with Terraform support, SREs can effectively manage service levels as code in a versioned and repeatable manner across any number of product and service teams.
- Making good SRE practices a reality: Enables SRE teams to uniformly adopt concepts such as SLIs, SLOs, SLAs and error budgets, and apply them to the business problem of reliability management. By automating data collection and analysis, teams also get a consistent view of SLOs and reliability across various products or services.
Sumo Logic Observability helps reduce downtime and solves customer-impacting issues faster with full-stack observability for all application data including logs, metrics, events and traces across the entire development lifecycle.
The Latest
For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...
Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...
AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...
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 ...