Alternatives to OpsWorker

Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.

NeuBird AI is a Production Ops Platform designed for ITOps, SRE, and DevOps teams running production cloud environments. It uses agentic AI to move operations from reactive incident response to proactive, autonomous production management. Despite significant investment in monitoring and observability tools, teams still face alert noise, slow root cause analysis, and costly incidents. NeuBird AI solves this by continuously analyzing telemetry across cloud services, applications, and infrastructure to prevent issues, resolve incidents faster, and optimize operations. Prevent incidents before they happen NeuBird AI detects early signals of degradation, configuration drift, and anomaly patterns across metrics, logs, traces, and change events. Teams can identify and address issues 30 to 60 minutes before user impact while reducing alert noise by more than 78 percent. Resolve incidents in minutes When incidents occur, NeuBird AI automatically investigates across Azure Monitor, Amazon CloudWatch, logs, metrics, traces, and recent changes to identify root cause in minutes. AI driven triage, correlation, and runbook generation reduce mean time to resolution by up to 60 percent while minimizing the need for large war room responses or bridge calls. Optimize cost, performance, and operations NeuBird AI continuously analyzes cloud environments to uncover cost savings, performance issues, and gaps in observability. It identifies right sizing opportunities, missing telemetry, and repetitive operational tasks, helping teams reclaim more than 200 engineering hours per month. Built for production cloud operations NeuBird AI integrates with AWS services including CloudWatch, as well as Kubernetes and Azure Monitor, and tools like Datadog, Splunk, and PagerDuty.

Datadog is the cloud-age monitoring, security, and analytics platform for developers, IT operation teams, security engineers, and business users. Our SaaS platform integrates monitoring of infrastructure, application performance monitoring, and log management to provide unified and real-time monitoring of all our customers' technology stacks. Datadog is used by companies of all sizes and in many industries to enable digital transformation, cloud migration, collaboration among development, operations and security teams, accelerate time-to-market for applications, reduce the time it takes to solve problems, secure applications and infrastructure and understand user behavior to track key business metrics.

All data sources, including topology, monitoring, change, and observation tools, are aggregated. BigPanda's Open Box Machine Learning will combine the data into a limited number of actionable insights. This allows incidents to be detected as they occur, before they become outages. Automatically identifying the root cause of problems can speed up incident and outage resolution. BigPanda identifies both root cause changes and infrastructure-related root causes. Rapidly resolve outages and incidents. BigPanda automates the incident response process, including ticketing, notification, tickets, incident triage, and war room creation. Integrating BigPanda and enterprise runbook automation tools will accelerate remediation. Every company's lifeblood is its applications and cloud services. Everyone is affected when there is an outage. BigPanda consolidates AIOps market leadership with $190M in funding and a $1.2B valuation

Hyground serves as an AI-enhanced co-pilot for DevOps and Site Reliability Engineering (SRE), functioning as a comprehensive operational intelligence platform that integrates seamlessly within the client's Kubernetes environment without any data leaving the premises. This sophisticated agent interfaces with over 21 enterprise systems to analyze incidents through various sources such as logs, metrics, traces, and Kubernetes events. Engineers can pose questions in everyday language and receive insights tailored to their specific datasets, eliminating the need to master new query languages. The AutoRCA feature transforms alert webhooks into self-sufficient root-cause analyses, providing updates directly to platforms like Slack or Teams. The investigation process initiates immediately upon alert, rather than waiting for an engineer to respond, leading customers to experience reductions in mean time to resolution (MTTR) of up to 85%. Leveraging Google's Agent Development Kit, Hyground employs a multi-agent framework that evolves by learning from the customer's infrastructure over time. Each resolved incident enhances the knowledge base, ensuring that operational runbooks remain up to date and relevant for future challenges. By facilitating real-time insights and continuous learning, Hyground empowers teams to operate more efficiently and effectively.

Do you struggle to manage all those alerts and tickets that come in? Dell APEX AIOps can reduce noise, detect incidents sooner, and fix issues faster. Do not let a flood alerts slow you. We remove these annoying alerts automatically so that you can enjoy your day without distraction. Never look at a ticket again. We send you "Situations" instead of tickets so you can fix problems faster before your customers complain. Stop wasting your time switching between tools. We bring all the tools together in one place, so you can manage any incident regardless of its origin. Use AI and ML to identify patterns and prevent them from happening again. Continuous delivery means continuous changes. Dell APEX AIOps automates the incident management workflow to provide continuous improvement. This gives you more time for other important and enjoyable tasks.

Metoro serves as an AI Site Reliability Engineer tailored for Kubernetes environments, assisting Site Reliability Engineers, DevOps professionals, and software developers in managing production effectively. This innovative tool autonomously oversees both services and infrastructure to identify any issues as they emerge, subsequently diagnosing the root causes and implementing solutions by creating pull requests. Utilizing eBPF, Metoro gathers all necessary telemetry without requiring modifications to the codebase, ensuring that every container, service, and host is monitored at the kernel level in real-time. Users can effortlessly deploy Metoro into their clusters with a single helm install command, leading to a fully operational setup in approximately five minutes. Its seamless integration and rapid deployment make it an invaluable asset for teams looking to enhance their operational efficiency.

Functions independently to manage regular alerts and actions, thereby minimizing escalations and mitigating burnout. It intelligently modifies thresholds and dashboards to proactively avert incidents and updates runbooks with each new occurrence. This efficiency can save on-call engineers as much as 20 hours weekly, allowing them to focus on development tasks. It manages all alerts, conducts root cause analysis, resolves incidents, and ensures that the on-call experience is stress-free. By automating root cause analysis and incident response, it can reduce Mean Time to Resolution (MTTR) by up to 80%. With comprehensive incident summaries and hypotheses accessible prior to logging in, users will enjoy quicker response times and significantly enhanced uptime. Getting started is quick and easy with production-ready AI that is secure and adept in utilizing all necessary production tools just like a seasoned software engineer. Additionally, it automatically maps your production environment, comprehends code, and tracks modifications seamlessly without requiring any prior training. This innovative approach not only streamlines operations but also enhances overall productivity and efficiency within the team.

Adps AI represents a groundbreaking autonomous AI-SRE platform that revolutionizes the management, troubleshooting, and security of cloud infrastructure for businesses. Rather than depending on cumbersome, manual processes for incident management, Adps AI employs continuous monitoring of various signals from logs, metrics, traces, deployments, Kubernetes, CI/CD pipelines, and cloud services to swiftly identify anomalies, pinpoint root causes, and generate accurate recovery actions within seconds. With the capability to decrease mean time to recovery (MTTR) by as much as 99% and achieve reliability levels exceeding 99.99%, Adps AI effectively alleviates on-call fatigue, prevents service disruptions, and guarantees seamless operations across diverse cloud environments. This innovative approach not only enhances operational efficiency but also empowers teams to focus on strategic initiatives rather than reactive problem-solving.

Safeguard business service-level agreements by utilizing dashboards that enable monitoring of service health, troubleshooting alerts, and conducting root cause analyses. Enhance mean time to resolution (MTTR) through real-time event correlation, automated incident prioritization, and seamless integrations with IT service management (ITSM) and orchestration tools. Leverage advanced analytics, including anomaly detection, adaptive thresholding, and predictive health scoring, to keep an eye on key performance indicators (KPIs) and proactively avert potential issues up to 30 minutes ahead of time. Track performance in alignment with business operations through ready-made dashboards that not only display service health but also visually link services to their underlying infrastructure. Employ side-by-side comparisons of various services while correlating metrics over time to uncover root causes effectively. Utilize machine learning algorithms alongside historical service health scores to forecast future incidents accurately. Implement adaptive thresholding and anomaly detection techniques that automatically refine rules based on previously observed behaviors, ensuring that your alerts remain relevant and timely. This continuous monitoring and adjustment of thresholds can significantly enhance operational efficiency.

Embark on your AIOps journey and revolutionize your IT operations using IBM Cloud Pak for Watson AIOps. This advanced platform integrates sophisticated, explainable AI throughout the ITOps toolchain, enabling you to effectively evaluate, diagnose, and address incidents affecting critical workloads. For those seeking IBM Netcool Operations Insight or earlier IBM IT management solutions, IBM Cloud Pak for Watson AIOps represents the next step in your current entitlements. It allows you to correlate data from all pertinent sources, uncover hidden anomalies, predict potential issues, and expedite resolutions. By proactively mitigating risks and automating runbooks, workflows become significantly more efficient. AIOps tools facilitate the real-time correlation of extensive unstructured and structured data, ensuring that teams can remain focused while gaining valuable insights and recommendations integrated into their existing processes. Additionally, you can create policies at the microservice level, allowing for seamless automation across various application components, ultimately enhancing overall operational efficiency even further. This comprehensive approach ensures that your IT operations are not just reactive but also strategically proactive.

Ciroos is a platform designed to enhance Site Reliability Engineering (SRE) teams through AI integration, revolutionizing the approach to incident management by employing multi-agent AI to minimize repetitive tasks, identify anomalies promptly, and speed up both investigations and resolutions in intricate, multi-domain scenarios. This innovative AI SRE Teammate seamlessly connects with various telemetry and observability tools, ticketing systems, collaboration platforms, and cloud service providers, functioning effectively in both automated and manually initiated modes to diligently investigate alerts, link data from diverse sources, pinpoint root causes, and offer practical recommendations often prior to escalation. The AI agents within Ciroos create dynamic investigation strategies, evaluate evidence at a scale akin to human experts, and produce reports post-incident for ongoing enhancement. Additionally, the platform’s ability to correlate across different domains allows it to detect problems that affect a range of areas, including infrastructure, networking, applications, and security, thus providing a comprehensive solution for modern operational challenges. By bridging gaps in these domains, Ciroos not only streamlines workflows but also empowers teams to focus on strategic initiatives.

Autointelli Inc, a leader in AIOps, delivers innovative solutions that revolutionize modern IT operations through a combination of automation and advanced machine learning techniques. Our focus on providing solutions has led us to create an AIOps platform designed to streamline data center automation. By utilizing the Autointelli AIOps platform, you can effectively minimize alert noise, pinpoint root issues, and reallocate your team to focus on more critical IT responsibilities. Partner with us to enhance your digital workplace experience. The Autointelli AIOps platform accelerates event correlation and seamlessly escalates complex incidents to the appropriate engineers. Furthermore, it includes a robust self-service automation feature, enabling users to design countless workflows for automation purposes. The platform's root cause analysis capability allows for the identification of core issues affecting both hardware and software. Additionally, our analytics tools are engineered to boost your business performance by gleaning valuable insights from all significant data sources, ensuring you remain competitive in a rapidly changing landscape. As technology evolves, having an intelligent AIOps solution becomes essential for sustained operational success.

Deductive AI is an innovative platform that transforms the way organizations address intricate system failures. By seamlessly integrating your entire codebase with telemetry data, which includes metrics, events, logs, and traces, it enables teams to identify the root causes of problems with remarkable speed and accuracy. This platform simplifies the debugging process, significantly minimizing downtime and enhancing overall system dependability. With its ability to integrate with your codebase and existing observability tools, Deductive AI constructs a comprehensive knowledge graph that is driven by a code-aware reasoning engine, effectively diagnosing root issues similar to a seasoned engineer. It rapidly generates a knowledge graph containing millions of nodes, revealing intricate connections between the codebase and telemetry data. Furthermore, it orchestrates numerous specialized AI agents to meticulously search for, uncover, and analyze the subtle indicators of root causes dispersed across all linked sources, ensuring a thorough investigative process. This level of automation not only accelerates troubleshooting but also empowers teams to maintain higher system performance and reliability.

Rootly redefines incident management with a fully integrated, AI-powered platform designed to simplify and accelerate the entire reliability workflow. From intelligent on-call management to automated incident response and retrospectives, it eliminates repetitive tasks so engineers can focus on problem-solving. The platform’s AI SRE module performs real-time root cause analysis, suggests fixes, and predicts resolution steps based on millions of real-world incidents. Through seamless integrations with Slack, Microsoft Teams, Jira, and Zoom, Rootly embeds reliability directly into team workflows. Its automation engine streamlines communication, tracking, and reporting, cutting resolution times by up to 50%. Built for scalability, Rootly adapts to teams of any size—from startups to Fortune 500 enterprises—without sacrificing simplicity. Users can also publish automated status pages to keep customers informed and reduce inbound support. With award-winning support and reliability baked in, Rootly enables organizations to strengthen uptime, operational efficiency, and engineering wellness.

Improving business outcomes involves making it easier to spot and address high-priority incidents. The WatchTower Platform serves as a comprehensive observability tool that streamlines incident resolution specifically within mainframe environments by effectively integrating and correlating events, data flows, and metrics across various IT silos. It provides a cohesive and intuitive interface for operations teams, allowing them to optimize their workflows. Leveraging established AIOps solutions, WatchTower is adept at detecting potential problems at an early stage, which aids in proactive mitigation. Additionally, it utilizes OpenTelemetry to transmit mainframe data and insights to observability tools, allowing enterprise SREs to pinpoint bottlenecks and improve operational effectiveness. By enhancing alerts with relevant context, WatchTower eliminates the necessity for logging into multiple tools to gather essential information. Its workflows expedite the processes of problem identification, investigation, and incident resolution, while also simplifying the handover and escalation of issues. With such capabilities, WatchTower not only enhances incident management but also empowers teams to proactively maintain high service availability.

Sherlocks.ai operates as an autonomous AI Site Reliability Engineering (SRE) agent, tirelessly functioning around the clock to avert incidents, streamline root cause analysis, and hasten recovery processes without necessitating additional personnel. Distinct from conventional monitoring tools, Sherlocks integrates seamlessly as a cognitive ally within your Slack channels, promptly addressing alerts, and synthesizing logs, metrics, and traces from your entire infrastructure, providing context-sensitive root cause analysis in mere seconds instead of hours. Organizations utilizing Sherlocks experience a threefold increase in the speed of incident resolution, a 50% decrease in manual work, and achieve 20-30% savings on cloud expenses due to intelligent predictive scaling. The system requires no agent installation, as it effortlessly connects to your existing observability stack—such as OpenTelemetry, Prometheus, and Datadog—through a secure API. Additionally, it boasts SOC2 Type 2 certification and offers a self-hosted deployment option, ensuring comprehensive control over data management. Furthermore, the integration of Sherlocks enhances team collaboration, allowing for a more efficient response to incidents and improved operational insights.

BMC Helix Operations Management serves as a comprehensive, cloud-native solution for observability and AIOps, specifically engineered to address the complexities of hybrid-cloud environments. Adopting a service-oriented perspective towards observability data is crucial for achieving effective AIOps results. It facilitates the integration of third-party observability inputs, including metrics, events, logs, incidents, changes, and topologies, into a unified IT data repository. This enables users to monitor service health and enhances the capacity for pinpointing root causes through automatically generated dynamic business service models. The AI-driven features improve the signal-to-noise ratio by employing event suppression, de-duplication, and correlation, all aimed at generating actionable insights. Users can quickly identify root causes with AI probability assignments to key causal nodes based on comprehensive data and service models. Additionally, the platform aids in preventing future incidents through proactive Business Service Health monitoring and AI-driven outage predictions. Troubleshooting is expedited via enriched logs and advanced analytics, while users can conveniently request and implement automations through BMC or other third-party tools, making management seamless and efficient. Ultimately, this solution empowers organizations to enhance their operational resilience and streamline management processes.

Traversal is an innovative AI-driven Site Reliability Engineering (SRE) solution that functions round the clock, autonomously identifying, addressing, and even preventing production issues. It meticulously analyzes logs, metrics, traces, and your codebase to pinpoint the root causes of errors or delays, quickly highlighting the impacted areas, critical bottleneck services, and potential root causes with relevant evidence in a matter of minutes. Leveraging advancements in causal machine learning, reasoning from large language models, and intelligent AI agents, Traversal proactively resolves problems before alerts are triggered, ensuring seamless operations. Tailored for complex organizations and vital infrastructure, it accommodates diverse data types, supports bring-your-own models, and offers optional on-premises deployment for added flexibility. With its straightforward integration into existing systems requiring only read-only access—without the need for agents, sidecars, or any write operations to production—Traversal guarantees data privacy and control. By effortlessly fitting into your observability framework, it not only accelerates the resolution process but also significantly reduces downtime, further enhancing operational efficiency and reliability. Furthermore, its ability to adapt to various environments makes it a versatile asset for businesses striving for uninterrupted service delivery.

OpenText AI Operations Management (Operations Bridge) is a comprehensive AIOps platform designed to provide enterprises with full-stack visibility and automated management of IT operations across cloud, on-premises, and XaaS environments. The solution dynamically discovers services and dependent resources, consolidating performance and event data from multiple sources to improve IT observability and accelerate incident resolution. Its AI-powered event correlation intelligently groups symptomatic alerts, reducing event noise and speeding up root cause identification. Deployment options include flexible SaaS and on-premises models, enabling organizations to balance control, speed, and scalability according to their strategic priorities. Embedded automation workflows enable rapid remedial actions through thousands of pre-built operations, minimizing manual intervention. The platform also delivers detailed service performance insights to pinpoint resource bottlenecks affecting user experience. OpenText AI Operations Management integrates seamlessly with existing toolchains to provide actionable intelligence and faster mean time to repair. It helps IT teams proactively manage service health and enhance operational efficiency.

The Azure SRE Agent functions as an intelligent reliability assistant, aimed at streamlining site reliability engineering tasks to ensure optimal health and performance within cloud environments. It operates by continuously observing Azure resources, identifying irregularities, and leveraging AI to suggest or implement actions that minimize downtime and reduce operational burdens. By integrating seamlessly with Azure services and other external systems, it facilitates comprehensive automation of operational processes, thereby enhancing system reliability and consistency. Using a user-friendly natural-language chat interface, engineers are able to probe into incidents, receive guidance for troubleshooting, and authorize automated remediation processes prior to their implementation. Additionally, the agent scrutinizes logs, metrics, and telemetry data to expedite root cause analysis and is capable of executing preset solutions such as scaling resources or restarting services, further increasing operational efficiency. This smart assistant not only streamlines workflows but also empowers teams to focus on more strategic initiatives.

A centralized approach driven by AI and machine learning is designed to handle vast quantities of IT-related data sourced from various platforms. This approach enhances the responsiveness of multiple teams to outages and performance issues while ensuring seamless interaction with IT service management technologies. By employing AIOps, organizations can effectively address daily IT operational challenges on a large scale, utilizing a range of advanced techniques such as machine learning, network science, combinatorial optimization, and additional computational methods. AIOps equips enterprises to manage an extensive array of IT management tasks, which includes intelligent alerting, correlating alerts, escalating alerts, automating remediation, investigating root causes, and optimizing capacity. Implementing a structured framework enables the proactive refinement of processes, resources, personnel, information, and communication channels. Continuous oversight and optimization of operations are essential, allowing for 24/7 management of IT functions. Additionally, establishing effective processes helps minimize the disruptive noise that often accompanies incident occurrences, ultimately leading to a more streamlined IT environment. This comprehensive strategy can significantly enhance overall operational efficiency and reliability.

The AWS DevOps Agent is a solution provided by Amazon Web Services (AWS) that functions as a self-sufficient, continuously operating operations engineer, tasked with identifying and preventing issues within your infrastructure, applications, and deployment processes. This tool autonomously analyzes your application assets and their interconnections, encompassing infrastructure, code repositories, deployment workflows, monitoring tools, and telemetry data, to synthesize information from logs, metrics, traces, deployment activities, and recent code modifications. In the event of an alert, unexpected error surge, or a help request, the DevOps Agent promptly initiates an automated analysis; it conducts incident triage around the clock, performs root-cause examinations, and offers detailed remediation strategies that can seamlessly integrate into team workflows (for instance, through Slack, ServiceNow, or PagerDuty) or directly generate support tickets with AWS. Moreover, this proactive approach ensures that potential issues are addressed before they escalate, enhancing the overall reliability of your systems.

TrueSight Operations Management provides comprehensive performance monitoring and event management solutions. By leveraging AIOps, it continuously learns from behaviors, correlates, analyzes, and prioritizes event data, enabling IT operations teams to identify, locate, and resolve issues more rapidly. It also detects data anomalies and issues proactive alerts to address potential problems before they affect services. TrueSight Infrastructure Management is designed to identify and rectify performance issues before they disrupt business operations, as it autonomously learns the typical behavior of your infrastructure and triggers alerts only when attention is required. This focus allows IT teams to concentrate on the most critical events that affect both their operations and the overall business. Additionally, TrueSight IT Data Analytics employs machine-assisted techniques to analyze log data, metrics, events, changes, and incidents, allowing users to efficiently navigate through vast amounts of information with just one click, thus enhancing problem-solving speed. Ultimately, the integration of these solutions streamlines IT operations and improves overall service reliability.

Cleric serves as an independent AI Site Reliability Engineer (SRE) that autonomously oversees, optimizes, and repairs software infrastructure without the need for human oversight. Acting as a collaborative AI partner, it seamlessly integrates with various existing tools, such as Kubernetes, Datadog, Prometheus, and Slack, to explore and diagnose production issues. By automatically managing alerts, Cleric enables engineers to dedicate more time to development rather than routine tasks. It efficiently evaluates systems simultaneously, providing insights in mere minutes, which would typically take hours to resolve manually. When faced with unfamiliar problems, Cleric formulates hypotheses and executes real-time queries with its integrated tools, only presenting conclusions once it is confident in its findings. With each investigation, Cleric enhances its capabilities by learning from actual outcomes and incidents. By the end of the first month, Cleric is equipped to manage approximately 20–30% of on-call responsibilities, empowering your team to prioritize problem-solving over monotonous alert triage. As a result, the overall efficiency and productivity of the engineering team can significantly improve.

Synergy serves as an AI-driven command center designed for enterprise IT operations, consolidating fragmented monitoring, ticketing, logging, and documentation into a cohesive interface. By continuously integrating data from tools such as Splunk, New Relic, Jira, ServiceNow, and Confluence, it transforms overwhelming alert storms into well-organized, prioritized insights. Its Smart Incident Workflows streamline routine processes, recommend subsequent actions, identify ownership gaps, and expedite resolutions, thereby reducing the average time for detection and repair. Additionally, Synergy’s proactive monitoring capabilities identify potential risks ahead of conventional alerts, highlight error surges and missed escalations, detect emerging trends, and respond to investigative inquiries using natural language. Furthermore, its integrated root cause analysis tracks incidents comprehensively across timelines, logs, metrics, tickets, and post-mortem evaluations, connecting to related events for immediate context and producing succinct summaries to aid in understanding. Overall, Synergy enhances operational efficiency and effectiveness for IT teams, ensuring they remain ahead of potential issues.

You can innovate faster in the cloud, improve user experience and future-proof applications. Splunk is designed for cloud-native enterprises and helps you solve current problems. Splunk helps you detect any problem before it becomes a customer problem. Our AI-driven Directed Problemshooting reduces MTTR. Flexible, open-source instrumentation eliminates lock-in. Optimize performance by seeing all of your application and using AI-driven analytics. You must observe everything in order to deliver an excellent end-user experience. NoSample™, full-fidelity trace ingestion allows you to leverage all your trace data and identify any anomalies. Directed Troubleshooting reduces MTTR to quickly identify service dependencies, correlations with the underlying infrastructure, and root-cause errors mapping. You can break down and examine any transaction by any dimension or metric. You can quickly and easily see how your application behaves in different regions, hosts or versions.

Streamlined and effective incident management made effortless. Featuring a beautifully intuitive interface, robust workflow automation, and seamless integrations with your current tools, prepare to experience incident management in a whole new way. We ensure a smooth transition by allowing your teams to utilize Slack and integrate effortlessly with familiar tools like Jira, Statuspage, and PagerDuty. Our system supports your teams during their most challenging moments, empowering anyone to manage incidents with assurance, facilitating organizational growth without interruption. Instantly establish consistency with our user-friendly workflow creation tools. You can automate repetitive tasks such as sending update emails to executives and compiling post-mortems, allowing you to concentrate on developing and improving exceptional products. Minimize redundancy and mitigate distractions by conducting more transparent incidents, where you can assign roles and actions, give real-time updates, and access a comprehensive overview of all ongoing incidents, ensuring everyone stays informed and engaged throughout the process. This approach not only enhances communication but also fosters a culture of accountability and efficiency within your organization.

FortiAIOps enhances IT operations by providing proactive visibility through the power of artificial intelligence, facilitating a more efficient network management system. This AI/ML solution is specifically designed for Fortinet networks, enabling rapid data acquisition and the detection of anomalies within the network. The various Fortinet devices, including FortiAPs, FortiSwitches, FortiGates, SD-WAN, and FortiExtender, contribute to the FortiAIOps dataset, which aids in generating insights and correlating events crucial for the network operations center (NOC). This system allows for comprehensive visibility across the entire OSI model, offering detailed Layer 1 data such as RF spectrum analysis to identify potential Wi-Fi interference. Additionally, it provides Layer 7 application insights, revealing the applications that flow through both Ethernet and SD-WAN links. To further assist in network management, users can leverage an array of troubleshooting tools, including VLAN probing, cable verification, spectrum analysis, and service assurance, to effectively diagnose and resolve issues. By employing these tools, organizations can ensure their networks operate smoothly and efficiently.

Enhancing incident management for active SRE and DevOps teams, this solution integrates your team's expertise with the capabilities of AI and machine learning. It features a correlation engine designed to streamline DevOps and Site Reliability Engineering processes. Through automatic correlation, aggregation, and prioritization of alerts, it ensures that you concentrate on the most critical matters. Swiftly address problems with predictive insights and suggested resolutions that are generated automatically. Additionally, issues are enriched automatically with all pertinent logs, events, and metrics required, no matter the timeframe, allowing for a more comprehensive understanding of incidents. This innovative approach ultimately empowers teams to maintain better operational efficiency and responsiveness in a fast-paced environment.

The Riverbed Aternity platform harnesses the power of AI-driven analytics and self-healing mechanisms to enhance both employee efficiency and customer satisfaction while enabling swift market entry with high-quality applications, reducing IT operational expenses, and managing the complexities of IT transformation. By providing AI-powered insights derived from authentic end-user experience data and precise telemetry across various endpoints, applications, infrastructure, and networks, Riverbed Aternity equips Digital Workplace teams with essential tools such as DXI for benchmarking, an Intelligent Service Desk, and AI-enhanced troubleshooting. These features facilitate ongoing service enhancement and proactive incident prevention throughout the organization. Explore how Aternity can empower enterprises to achieve comprehensive visibility across their environments, lower IT asset expenditures, promote sustainable IT practices, and elevate the satisfaction of both employees and customers, ultimately driving organizational success.

Utilize AIOps to foresee problems, minimize the impact on users, and streamline resolution processes. Transition from a reactive approach in IT operations to one that leverages insights and automation for better efficiency. Detect unusual patterns and address potential issues proactively through collaborative automation workflows. Enhance digital operations with AIOps by focusing on proactive measures rather than merely responding to incidents. Eliminate the burden of chasing after false positives as you pinpoint anomalies with greater accuracy. Gather and scrutinize telemetry data to achieve improved visibility while minimizing unnecessary distractions. Identify the underlying causes of incidents and provide teams with actionable insights for better collaboration. Take preemptive steps to reduce outages by following guided recommendations, ensuring a more resilient infrastructure. Accelerate recovery efforts by swiftly implementing solutions derived from analytical insights. Streamline repetitive processes using pre-crafted playbooks and resources from your knowledge base. Foster a culture centered on performance across all teams involved. Equip DevOps and Site Reliability Engineers (SREs) with the necessary visibility into microservices to enhance observability and expedite responses to incidents. Expand your focus beyond just IT operations to effectively oversee the entire digital lifecycle and ensure seamless digital experiences. Ultimately, adopting AIOps empowers your organization to stay ahead of challenges and maintain operational excellence.

StackPulse streamlines and enhances the processes of incident response and management, fostering a seamless commitment to the reliability of software services. It equips Site Reliability Engineers, developers, and on-call personnel with the essential context and authority to effectively analyze, address, and resolve incidents throughout the entire stack, regardless of scale. By revolutionizing how engineering and operations teams handle software and infrastructure services, StackPulse introduces a collaborative platform filled with various incident management tools. Users can effortlessly initiate teamwork through automated war room setups, efficient data collection, and auto-generated postmortem reports. The insights gathered during incidents pave the way for tailored recommendations on playbooks and triggers, leading to remarkable decreases in Mean Time to Recovery (MTTR) and enhanced adherence to Service Level Objectives (SLOs). Additionally, StackPulse identifies risks by analyzing unique patterns within an organization’s monitoring, infrastructure, and operational data, offering customized automated playbooks that suit specific organizational needs. This approach not only mitigates risks but also empowers teams to better manage their operational challenges.

Selector’s software-as-a-service leverages machine learning and natural language processing to deliver self-service analytics that facilitate immediate access to actionable insights, significantly decreasing mean time to resolution (MTTR) by as much as 90%. This innovative Selector Analytics platform harnesses artificial intelligence and machine learning to perform three critical functions, equipping network, cloud, and application operators with valuable insights. It gathers a wide array of data—including configurations, alerts, metrics, events, and logs—from diverse and disparate data sources. For instance, Selector Analytics can extract data from router logs, device performance metrics, or configurations of devices within the network. Upon gathering this information, the system normalizes, filters, clusters, and correlates the data using predefined workflows to generate actionable insights. Subsequently, Selector Analytics employs machine learning-driven data analytics to evaluate metrics and events, enabling automated detection of anomalies. In doing so, it ensures that operators can swiftly identify and address issues, enhancing overall operational efficiency. This comprehensive approach not only streamlines data processing but also empowers organizations to make informed decisions based on real-time analytics.

Avaron specializes in creating autonomous infrastructure tailored for both enterprise and mission-critical settings. Central to their offering is AIM (Avaron Infrastructure Manager), a sophisticated system that perpetually tracks infrastructure performance, scrutinizes operational metrics, and implements policy-driven remediation workflows. By integrating monitoring, automation, simulation, and orchestration within a single platform, AIM not only simplifies operational complexities but also enhances the resilience and efficiency of infrastructure. Unlike conventional tools that merely focus on monitoring and alerting, AIM seamlessly blends observability, AI-powered decision-making, automation, and remediation, thereby eliminating tedious manual tasks and refining incident response protocols. Designed for various sectors, including data centers, managed service providers, telecom, healthcare, financial services, and manufacturing, AIM caters to any organization operating distributed infrastructure and aims to transform the way enterprises manage their critical systems.

Dash0 serves as a comprehensive observability platform rooted in OpenTelemetry, amalgamating metrics, logs, traces, and resources into a single, user-friendly interface that facilitates swift and context-aware monitoring while avoiding vendor lock-in. It consolidates metrics from Prometheus and OpenTelemetry, offering robust filtering options for high-cardinality attributes, alongside heatmap drilldowns and intricate trace visualizations to help identify errors and bottlenecks immediately. Users can take advantage of fully customizable dashboards powered by Perses, featuring code-based configuration and the ability to import from Grafana, in addition to smooth integration with pre-established alerts, checks, and PromQL queries. The platform's AI-driven tools, including Log AI for automated severity inference and pattern extraction, enhance telemetry data seamlessly, allowing users to benefit from sophisticated analytics without noticing the underlying AI processes. These artificial intelligence features facilitate log classification, grouping, inferred severity tagging, and efficient triage workflows using the SIFT framework, ultimately improving the overall monitoring experience. Additionally, Dash0 empowers teams to respond proactively to system issues, ensuring optimal performance and reliability across their applications.

Service assurance is a key goal for digital-first businesses. It has become the lifeblood of their business applications. These applications are becoming more complex due to the advent of 5G, edge, and containerized cloud-native infrastructures. RDAF consolidates disparate data sources and converges on the root cause using dynamic AI/ML pipelines. Then, intelligent automation is used to remediate. Data-driven companies should evaluate, assess, and implement RDAF to speed innovation, reduce time to value, meet SLAs, and provide exceptional customer experiences.

StackState's Topology & Relationship-Based Observability platform allows you to manage your dynamic IT environment more effectively. It unifies performance data from existing monitoring tools and creates a single topology. This platform allows you to: 1. 80% Reduced MTTR by identifying the root cause of the problem and alerting the appropriate teams with the correct information. 2. 65% Less Outages: Through real-time unified observation and more planned planning. 3. 3.3.2. 3x faster releases: Developers are given more time to implement the software. Get started today with our free guided demo: https://www.stackstate.com/schedule-a-demo

The Netenrich operations intelligence platform is meticulously designed to assist enterprises in addressing both immediate and long-term challenges, fostering stable and secure environments and infrastructures. By integrating the finest elements of machine and human intelligence—commonly referred to as hybrid intelligence—we enhance processes such as threat detection, incident response, and site reliability engineering (SRE), alongside various other key objectives. Our approach begins with self-learning machines that have been honed through extensive research, investigation, and remediation tactics. As a result, the need for human involvement in repetitive, automatable tasks is minimized, empowering your team and technology to focus on achieving significant outcomes like SRE, reduced mean time to resolution (MTTR), decreased dependency on subject matter experts (SMEs), and an unprecedented operational scale without the burden of routine operations. From the initial detection to final resolution, the Netenrich platform takes on the heavy lifting of analyzing and addressing alerts and threats, ensuring that your organization can operate efficiently and effectively in a constantly evolving landscape. This comprehensive strategy not only enhances operational efficiency but also positions enterprises to thrive amid future challenges.

Introducing the ultimate self-repairing IT solution tailored for your enterprise. With its remarkable cognitive abilities, HEAL proactively averts IT system failures before they occur, allowing you to devote your attention to other vital areas of your business. In today’s fast-moving environment, merely identifying and reporting incidents post-factum is insufficient. HEAL stands out as a revolutionary IT tool that not only addresses issues but also anticipates and mitigates them through advanced AI algorithms and machine learning techniques, ensuring seamless operations for enterprises. Utilizing an innovative approach known as 'workload-behavior correlation,' HEAL thoroughly examines all elements essential for the efficient functioning of an IT system, including volume, composition, and payload. Whenever it detects any irregular behavior, it promptly initiates either a healing response or a scaling action based on the underlying cause, making it an indispensable asset for modern businesses striving for reliability and efficiency. This proactive strategy empowers organizations to maintain optimal performance and reduce downtime significantly.

Runframe offers a solution for incident management and on-call scheduling specifically designed for engineering teams and is seamlessly integrated within Slack. By using the command /incident, teams can easily declare incidents, prompting Runframe to automatically create a dedicated channel, designate responders, and keep a comprehensive log of every action taken. The system also features on-call rotations paired with escalation policies that notify the appropriate individual if there is no response. To enhance operational efficiency, analytics monitor metrics like MTTR, MTTA, and on-call equity, while post-incident evaluations utilize timelines that are generated automatically for a detailed review. This ensures that teams can effectively learn from past incidents and continually improve their response strategies.

InsightFinder Unified Intelligence Engine platform (UIE) provides human-centered AI solutions to identify root causes of incidents and prevent them from happening. InsightFinder uses patented self-tuning, unsupervised machine learning to continuously learn from logs, traces and triage threads of DevOps Engineers and SREs to identify root causes and predict future incidents. Companies of all sizes have adopted the platform and found that they can predict business-impacting incidents hours ahead of time with clearly identified root causes. You can get a complete overview of your IT Ops environment, including trends and patterns as well as team activities. You can also view calculations that show overall downtime savings, cost-of-labor savings, and the number of incidents solved.

TraceRoot.AI serves as an open-source, AI-driven observability and debugging platform that aims to assist engineering teams in swiftly addressing production challenges. By merging telemetry data into a unified correlated execution tree, it offers essential causal insights into failures. AI agents leverage this structured representation to summarize problems, identify probable root causes, and even propose actionable solutions or generate GitHub issues and pull requests. Users can engage in interactive trace exploration, featuring zoomable log clusters and detailed views on spans and latency, complemented by insights linked to the code itself. Additionally, lightweight SDKs for Python and TypeScript facilitate effortless instrumentation via OpenTelemetry, accommodating both self-hosted and cloud-based deployments. A key aspect of the platform is its human-in-the-loop interaction, which allows developers to influence the reasoning process by selecting relevant spans or logs, enabling them to validate the agent's reasoning with traceable context. This collaborative approach not only enhances debugging efficiency but also empowers teams with greater control over the issue resolution process.

While it’s well known that unknown changes are the root cause of most stability issues, IT still struggles to know what actually changed. Until now… Using AI-based analytics, Evolven detects and prioritizes risks triggered by actual, granular changes in configuration, application, infrastructure, and data so that you can prevent and rapidly resolve stability, compliance, and security issues. Despite the higher pace of changes in agile environments, the result is a greater user experience for customers. With Evolven, DevOps, CloudOps, and IT Ops teams experience greater visibility into their environments, fewer incidents and faster MTTR.

Enhancing the efficiency of construction projects can be achieved through improved risk reporting, streamlined administration, and shortened lead times for issue resolution. Implementing GDPR-compliant and digital onboarding processes engages all personnel while alleviating the administrative workload for site management. This approach empowers every worker to report observations, near-misses, and accidents, thus fostering a culture of safety and operational efficiency on site. Users can create customized checklists and forms for various purposes, including safety inspections, quality checks, LEED/BREEAM assessments, daily records, toolbox discussions, and more. With comprehensive control over ongoing tasks, bespoke task lists are updated in real-time to ensure accountability. Automated reminders and documented actions establish a solid foundation for personal responsibility. Furthermore, investigating incidents and accidents allows for the identification of root causes and potential hazards, while offering flexibility to adapt to various investigative frameworks, such as the 5 WHY method and MTO. This holistic approach not only enhances safety but also promotes a proactive attitude towards risk management, ultimately leading to more successful project outcomes.