Observability IQ

Revolutionizing troubleshooting with targeted AI, automating complex root cause analysis and enabling users to interact directly with their data—de-risking CI/CD to empower innovation.
Proactive Problem Resolution

“[Logz.io’s] AI-powered insights and anomaly detection reduce noise and help identify issues faster, enabling proactive problem resolution. This benefits me by improving system reliability, reducing troubleshooting time, and optimizing monitoring costs, all of which are crucial for maintaining efficient cloud-native applications.”

– Arik S., Cybersecurity Manager

AI-powered Analysis and Investigation

Converse Directly with Your Data
Using the Logz.io AI Agent

  • Leverage an automated copilot to analyze your data using natural language terms
  • Automate manual querying to empower in-depth analysis and enact intuitive, targeted response
  • Generate detailed insights into a particular service being investigated and guidance on what users should analyze next
Converse Directly with Your Data 
Using the Logz.io AI Agent

Transform Investigation and Reduce MTTR with AI Agent-powered RCA

  • Trigger automated root cause analysis to eliminate manual steps and launch investigations
  • Move from use of multiple dashboards and queries to immediately pinpoint issues
  • Quickly generate conclusive response steps to enable rapid resolution of emerging issues

 

Build an Automated Knowledge Base to Increase Maturity

  • Provide detailed technical guidance on the system and observability best practices to optimize numerous workflows
  • Empower the system to adapt to specific context and evolve by learning from new data, enhancing its capabilities over time
  • Enable less experienced analysts to immediately benefit from deep system insights and the inputs of experienced teammates

 

Intelligent Detection and Oversight

Catch Issues Early with AI-Driven Anomaly Detection

  • Apply advanced Anomaly Detection for dynamic alerting and investigation of issues occurring within specific application services
  • Automatically generate real-time insights into performance of key services, operations, metrics, and endpoints to enable response
  • Launch proactive investigation into previously undetected issues based on historical data and proven remediation steps
Catch Issues Early with AI-Driven Anomaly Detection

Speed Up Resolution with Smart Alert Recommendations

  • Prioritize investigation and speed response through automated recommendations based on previous investigations
  • Model successful actions taken by platform users to advise subsequent analysis into similar issues
  • Immediately identify the fastest, most-effective resolution path to cut MTTR based on contextual analysis
Speed Up Resolution with Smart Alert Recommendations

Optimize Costs and Data Usage with AI-Driven Insights

  • Inventory all incoming data to intelligently classify telemetry, filter out unneeded data and focus on what really matters
  • Automatically reduce cost and complexity – most customers realize 30-50% cost reductions by filtering unnecessary data
  • Continuously optimize data management and retention, ensuring the most efficient classification and storage, by use case
Optimize Costs and Data Usage with AI-Driven Insights

Logz.io Observability IQ FAQs

What is AI-powered observability?

AI-powered observability is the practice of integrating artificial intelligence technologies to enhance the observability of IT infrastructures. This includes the introduction of both vendor-produced proprietary AI and generative AI driven by the ever-expanding world of large language models (LLMs). AI-powered observability enhances the traditional monitoring tools by adding layers of intelligence, prediction, guidance and automation, which are crucial for managing complex and dynamic IT environments effectively.

How does AI-powered observability differ from traditional observability?

Traditional observability focuses on collecting and visualizing telemetry data (including the “three pillars” of logs, metrics and traces) to diagnose issues impacting system availability and performance — allowing you to troubleshoot and improve mean time to recovery (MTTR). AI-powered observability goes further by integrating both proprietary and generative AI to analyze data, detect anomalies, predict issues before they occur, and automate responses.

What is AIOps?

AIOps, or Artificial Intelligence for IT Operations, is the application of artificial intelligence (AI) technologies to enhance and automate IT operations processes. In its simplest terms, AIOps is a service that captures event data and synthesizes it with machine learning capabilities. The goal of AIOps is to streamline and improve IT operations through automation and data-driven insights, leading to more efficient operations, reduced downtime, and better performance.

What is the difference between AIOps and observability?

Observability provides the data foundation that AIOps needs. Without comprehensive observability, AIOps cannot function effectively because it lacks the detailed data required to make predictions and automate processes. Conversely, AIOps extends the value of observability by adding layers of intelligence, automation, and predictive capabilities to help manage systems more proactively.

While observability concentrates on data collection and providing insight and actionability into critical systems, AIOps uses event data to automate processes and predict future actions, enhancing the efficiency of IT operations. Together, they create a more resilient and responsive IT infrastructure.

Is AI-powered observability suitable for any size of business?

Businesses of any size can benefit from AI-powered observability, especially as they scale and their IT systems become more complex. While large organizations immediately benefit from the increased ability to understand their sprawling infrastructure, smaller organizations gain the ability to cover more ground with their existing teams, and budgets.

Can Logz.io Observability IQ really help reduce observability costs? How?

Observability IQ extends the power and expertise of your existing team, reducing hours previously spent on manual tasks so your team can focus on bigger, revenue-impacting projects and help narrow knowledge gaps. You gain the ability to move faster and with greater accuracy, massively increasing return on investment.

Our AI capabilities can also help you separate needed telemetry data from the massive volumes of noisy, unneeded data. Utilizing our AI-driven Data Optimization Hub, you can automatically reduce your cost and complexity; most customers realize 30-50% cost reductions by filtering our unnecessary data.

How does Logz.io’s AI Agent-powered RCA help with troubleshooting application issues?

Logz.io’s AI Agent allows engineers and QA teams to accelerate and improve RCA by:

  • Enlisting AI to immediately accelerate their work, leveraging the system to investigate and move to resolve any emerging issues
  • Moving from using multiple dashboards and drill-downs to slowly carry out investigation to using AI to immediately isolate what went wrong
  • Employing every aspect of the agent to automate numerous day-to-day activities, covering more ground, and reducing MTTR
How does Logz.io Observability IQ automation help with debugging application issues?

Logz.io Observability IQ enables engineers and QA teams to:

  • Understand how new deployments have impacted my applications even faster, automatically performing in-depth contextual analysis
  • Quickly focus and refine analysis to concentrate on specific issues or timeframes, without immediate need for added querying or dashboards
  • Immediately access proven steps to address any errors discovered, moving directly from analysis to response activities
How can Logz.io’s AI Agent help with application performance monitoring?

Developers and SREs can leverage Logz.io’s AI Agent to:

  • Enlist AI to immediately accelerate their work, leveraging the system to find potential issues that they weren’t otherwise aware of
  • Move from chasing alerts and composing queries to asking the system to do this work, automatically pinpointing the biggest issues
  • Utilize progressively more informed analysis steps from a single interface, before drilling down into the results to perform hands-on work

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