Logz.io has added AI and ML-powered Anomaly Detection capability to App 360, equipping its application observability solution with automated capabilities that let users respond to real-time performance alerts based on models built from historical telemetry data, reducing the manual tasks that slow down and complicate remediation.
While Logz.io has been offering ML and AI-based Anomaly Detection across the Open 360™ platform since February of 2023, Anomaly Detection for App 360 extends this capability to the new App 360 solution addressing the specific requirements of today's application observability users.
Anomaly Detection for App 360 is the kind of AI-driven automation that customers are asking for to help them optimize user experience while increasing efficiency and driving down costs.
With Anomaly Detection for App 360, Open 360 users can now enlist targeted automation to do more of the work for them — automatically monitoring and alerting any issues occurring within the specific services and microservices they identify as being most critical, which are often those that immediately impact business or SLO-related requirements.
Anomaly Detection for App 360 utilizes powerful automation to make it simple for users to set up and begin monitoring and alerting against their critical services. Whether users prefer a list-based approach using Logz.io Service Overview or topology-based approach using Logz.io Service Map, the new capability also supports these varied use cases oriented to different audiences, including software engineers, SREs, platforming engineering and beyond. Anomaly Detection for App 360 takes users beyond traditional monitoring of critical services by locating and scoring the severity of unusual activity for a more proactive response.
Key Benefits of Anomaly Detection for App 360:
- Faster troubleshooting: Increases app performance through automated detection. This new capability automatically surfaces emerging problems in the most critical services as designated by the user. Troubleshooting is accelerated by enabling the user to focus on those alerts that matter most to application optimization.
- Proactive and real-time: Proactively identifies issues that may otherwise go unnoticed. Advanced automation uncovers hard-to-predict issues before they impact end users. In contrast to traditional point-in-time, threshold-based application monitoring, Anomaly Detection enlists full-stack application observability that is more relevant and real-time.
- Automated insights: Moves away from traditional APM to full-scope application observability. Traditional APM solutions based on threshold-based detection typically require users to manually analyze available data, leaving users chasing high-volume alerts that may or may not be high priority. In contrast, Anomaly Detection for App 360 automatically generates real-time insights into the performance of user-prioritized services, operations, metrics and endpoints. This helps engineering teams accelerate and simplify their work in optimizing application performance, cutting through noise and reducing manual tasks.
"We continue to rapidly expand upon and deepen the capabilities of App 360, our groundbreaking application observability solution," said Asaf Yigal, co-founder and CTO at Logz.io. "Anomaly Detection for App 360 is the kind of AI-driven automation that customers are asking for to help them optimize user experience while increasing efficiency and driving down costs. This added capability helps our customers find the 'unknown unknowns' lurking in their complex microservices architectures, cutting through the mountains of available data to focus on priority issues and troubleshoot faster."
Engineering and ops teams — or anyone responsible for oversight of specific applications services — can use Anomaly Detection for App 360 to ensure they are automatically alerted whenever their services and microservices fall outside expected parameters.
Further, this new capability has been designed to give software engineers, site reliability engineers (SREs) and platform engineering teams the precise manner of utilization they prefer or that best tracks with their roles. Read more about these role-based customization options here.
For existing Logz.io customers, Anomaly Detection for App 360 is already available at no additional cost; it's simply enabled as a new element of the platform.
The Latest
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...
Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...
Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...
As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...
For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...
I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...
Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...