AIOps Vendors: An Expert Guide to the Top Platforms of 2025

The vendors in the table below are listed alphabetically for easy reference.

How We Choose the Best AIOps Platforms: Our Ranking Methodology

To provide a credible and unbiased list, we conducted a comprehensive evaluation of the top AIOps companies in the market. Our goal is to provide you with a clear understanding of each platform’s strengths, based on a consistent set of criteria.

Core AIOps Capabilities: The power and sophistication of each platform’s AI and machine learning engine, including the ability to perform real-time anomaly detection, predictive analytics, intelligent alert correlation, and accurate root cause analysis. We also assessed the use of advanced machine learning algorithms for anomaly detection and incident resolution, as these are essential for processing large datasets and improving operational efficiency.

Integration & Data Management: A platform’s value depends on its ability to connect with your existing IT ecosystem. We prioritized companies that offer extensive, easy-to-configure integrations with a wide range of monitoring tools, data sources, and next-gen ITSM platforms. Effective data collection from diverse sources, such as logs, servers, and networks, is a key evaluation criterion, as centralized data gathering and processing are crucial for operational efficiency and informed decision-making.

Automation & Remediation: Evaluation of the platform’s ability to move beyond simple monitoring by automating workflows and triggering remediation actions, helping IT teams resolve issues faster and reduce manual effort.

User Experience (UX) & Implementation: Consideration of the platform’s ease of use, the intuitiveness of its dashboard, and the complexity of the initial setup and onboarding process. A powerful tool is only effective if the team can use it efficiently.

Market Presence & Customer Feedback: Analysis of real-world data, including customer reviews from sites like Gartner Peer Insights and G2, case studies, and industry analyst reports to gauge customer satisfaction and proven enterprise readiness.

Leveraging Advanced Technology: Evaluation of how platforms are adopting agentic AI. Unlike traditional AIOps that depend on static rules, agentic AI introduces goal-driven agents that go beyond surfacing insights.

By leveraging AI in IT, AI agents can autonomously detect, diagnose, and resolve IT issues by making real-time decisions and executing actions in complex environments. Acting proactively and adaptively, they automate low-value tasks, accelerate troubleshooting, and reduce manual effort – freeing IT support teams to focus on higher-value, strategic work.

When discussing predictive analytics, we considered how platforms leverage historical data for forecasting and proactive incident management. Predictive analysis is a critical factor in assessing platforms’ ability to anticipate and prevent incidents before they escalate.

A step-by-step guide to implementing AIOps at a glance

Agentic AI-led AIOps

1- Aisera

Aisera is a leading AIOps vendor, providing an enterprise-grade AI agent platform for IT operations that proactively detects issues, automates root-cause analysis, orchestrates remediation, and reduces operational costs by enabling IT teams to resolve incidents faster and with greater accuracy.

Aisera AIOps Key Features:

Proactive incident detection and prevention: Aisera AIOps platform uses advanced correlation methods to analyze signals from monitoring data and incident management systems, detecting patterns or anomalies that indicate potential issues.

Automated impact & root-cause detection: Automates impact analysis and root cause detection by correlating data from incident tickets, monitoring tools, and telemetry data collected from infrastructure, applications, and services.

Incident clustering: Reduce alert noise by up to 90% and minimize alert fatigue by analyzing logs, tickets, alerts, change requests, and more to cluster related incidents automatically.

Advanced features: Offers sophisticated automation, predictive analytics, and integration capabilities that go beyond basic offerings.

Integration ecosystem: Integrates with the best ITSM software, monitoring, and alerting tools like ServiceNow, Jira, Datadog, and Splunk to streamline incident management and enhance operational responsiveness.

Agentic AI capabilities: Aisera’s AI agents can autonomously remediate recurring issues, execute remediation workflows, and orchestrate multi-agent system responses across agentic ITSM and monitoring platforms, ensuring incidents are resolved without human intervention. By automating routine processes, Aisera enables IT teams to focus on critical tasks.

Configuration changes automation: Automates configuration changes across IT environments, supporting real-time updates, scheduled backups, and compliance adherence.

Team collaboration: Supports team collaboration through integrated workflows and intelligent dashboards, facilitating communication and coordination during incident response.

Best for: Enterprises looking for an Agentic IT operations automation to reduce downtime, accelerate ticket resolution, and boost IT team productivity.

2- BigPanda

BigPanda’s AIOps platform focuses on robust data collection from various monitoring sources, enabling enterprises to streamline incident management by turning noisy IT alerts into actionable insights with AI-driven analysis of millions of data points.

BigPanda AIOps Key Features:

Open Box Machine Learning (standout feature): Provides transparent, customizable event correlation so IT teams can understand and fine-tune how incidents are linked, setting BigPanda apart from traditional AIOps tools.

Root cause analysis: Surfaces probable root causes by analyzing large volumes of alerts, changes, and topology data points, accelerating resolution times.

Alert noise reduction: Consolidates thousands of alerts into high-level incidents to explicitly reduce noise and minimize alert fatigue for operators.

AI agents incident management: Automate Level 1 incident detection and response, augmenting higher-level incident teams, and proactively preventing outages.

Integration ecosystem: Connects different systems across the IT environment and works with leading monitoring, observability, and ITSM platforms such as Datadog, New Relic, ServiceNow, and Jira.

Best for: Enterprises dealing with a high volume of IT alerts and looking to improve their incident management process through correlation, automation, and faster resolution.

Observability & monitoring led AIOps

3- Dynatrace

Dynatrace (Davis AI) is a comprehensive observability platform with a strong AIOps engine, delivering full-stack visibility, AI-powered root-cause analysis, and business analytics to help organizations optimize application performance and infrastructure monitoring.

The Davis AI engine leverages causal AI for precise root cause analysis, delivering accurate answers and enabling intelligent automation during IT operations and performance troubleshooting. Dynatrace also provides insights into cloud costs, helping organizations optimize spending and prevent unexpected expenses.

Dynatrace AIOps Key Features:

Automatic and intelligent observability: Dynatrace automatically discovers and maps applications, microservices, and infrastructure in real time, providing end-to-end visibility without manual configuration. This automatic AI observability is a standout feature that differentiates Dynatrace from traditional monitoring tools.

Root-cause analysis: Uses its Davis AI engine, which incorporates causal AI, to continuously analyze billions of dependencies and pinpoint the precise root cause of issues across complex, hybrid environments.

Business analytics: Goes beyond IT metrics to correlate performance data with business KPIs, enabling teams to understand how technical issues impact revenue and customer experience.

Cloud-native focus: Purpose-built to support Kubernetes, multicloud, and dynamic microservices architectures with automatic instrumentation and monitoring at scale. Dynatrace also offers AIOps for cloud monitoring, enhancing performance, security, and operational insights across cloud environments.

Integration ecosystem: Connects with leading DevOps, cloud, and ITSM tools, including AWS, Azure, Google Cloud, ServiceNow, and Atlassian.

Predictive AI: Dynatrace uses predictive AI to forecast potential issues, analyze root causes, and solutions for IT operations automation and performance monitoring.

Best for: Organizations that need deep visibility into their applications and infrastructure.

4- New Relic

New Relic is a well-known observability platform with a strong focus on application performance, enhanced by AIOps capabilities through its Applied Intelligence suite. It provides monitoring and optimization for cloud applications and offers insights into cloud costs to help manage and optimize expenses.

New Relic AIOps Key Features:

Applied Intelligence: Provides automatic anomaly detection, incident correlation, and AI-driven root cause analysis. New Relic leverages generative AI in AIOps to enhance incident detection and response, automating workflows and improving operational efficiency.

End-to-end visibility: Offers full-stack observability for applications, infrastructure, logs, and user experience. This full-stack observability is a standout feature that differentiates New Relic from traditional monitoring tools.

Proactive incident response: Surfaces critical issues before they impact customers through predictive insights.

Developer and DevOps focus: Tailored for engineering and DevOps teams with real-time debugging and telemetry insights.

Broad integration ecosystem: Connects with cloud services, collaboration tools, and ITSM systems like Slack, AWS, and ServiceNow.

New Relic also offers a flexible hourly pricing model, allowing organizations to pay based on actual usage.

Best for: DevOps teams and organizations focused on application performance monitoring with built-in AI-driven intelligence.

5- Datadog

Datadog is a popular monitoring and analytics platform that has expanded into AIOps. It combines unified observability with AI-driven insights to help teams detect, troubleshoot, and resolve issues faster.

Datadog AIOps Key Features:

Unified monitoring: Provides a single pane of glass for infrastructure monitoring, applications, logs, and security data, with deep visibility into cloud applications. Datadog also offers tools to monitor and optimize cloud costs, helping organizations manage expenses across cloud environments.

AI-powered anomaly detection: Uses machine learning to detect abnormal behavior in metrics, logs, and traces. A standout feature is Datadog’s Watchdog AI, which automatically identifies and highlights potential issues, setting Datadog apart from traditional monitoring tools.

Intelligent alerting: Reduces alert fatigue by automatically grouping related alerts and surfacing the most critical incidents.

Cloud-native focus: Purpose-built for modern cloud environments with strong support for Kubernetes, multicloud, and microservices.

Extensive integrations: Connects with 700+ tools across DevOps, cloud, and ITSM ecosystems, including leading alerting tools for seamless incident management and workflow integration.

User-friendly: Datadog features a user-friendly interface and intuitive setup, making it easy for IT teams to deploy, configure, and monitor their environments.

Best for: Organizations already using Datadog for monitoring and looking to extend into AIOps capabilities without adding a separate platform.

Event correlation led AIOps

6- ServiceNow

ServiceNow AIOps is delivered through the ServiceNow IT Operations Management (ITOM) suite, combining AI/ML, observability, and deep ITSM integration, a standout feature that sets ServiceNow apart, to cut noise, detect anomalies, and automate remediation across hybrid and multi-cloud environments, including monitoring and managing cloud applications.

ServiceNow AIOps Key Features:

Event Management & Noise Reduction: Consolidates alerts from multiple monitoring tools, deduplicates, and applies machine learning to reduce event noise and highlight high-priority incidents.

Service Mapping & Contextual Insights: Dynamically maps infrastructure, applications, cloud applications, and dependencies so issues are understood in the context of business services.

Anomaly Detection & RCA: Uses machine learning to detect deviations from normal patterns and correlates related events to accelerate RCA.

Automated Remediation & Workflows: Leverages ServiceNow’s orchestration engine to trigger playbooks, workflows, and automated fixes—including configuration changes—without manual intervention.

Integration with ITSM & ITIL Processes: Seamlessly ties incidents, changes, and problems to ITSM processes, ensuring consistency and governance across the enterprise.

ServiceNow AI Agents: Includes prebuilt and custom AI agents within the ServiceNow platform that autonomously resolve routine IT tasks, triage and route incidents, recommend next-best actions, and execute workflows across ITSM and ITOM.

Team Collaboration: Supports collaboration through integrated workflows and intelligent dashboards, enabling teams to coordinate incident response and streamline communication.

Best For: Enterprises that need end-to-end ITSM and AIOps integration to unify service visibility, automate incident resolution, and enhance team collaboration.

7- PagerDuty

PagerDuty AIOps delivers a full-stack incident response and operations platform enhanced with AI/ML and automation to reduce alert noise, speed up triage and root cause analysis, and enable event-driven orchestration. PagerDuty integrates with leading alerting tools to enhance incident response and streamline workflows across IT systems.

PageDuty AIOps Key Features:

Noise reduction & alert grouping: Uses built-in machine learning to suppress, deduplicate, or group alerts (intelligent, content-based, time-based, unified, and global alert grouping) to dramatically cut down on unnecessary incident noise, helping IT teams focus on critical tasks by filtering out irrelevant alerts.

Triage & Root Cause Analysis (RCA): This method automatically surfaces key incident context through features like probable origin, past incidents, related incidents, outlier incidents, and correlating recent changes to speed up identifying where the problem likely started.

Event Orchestration & Automation: Enables event-driven workflow rules to automatically enrich, route, or alter alerts. This lets teams automate repetitive incident response work, reduce human intervention, and enforce consistent service handling.

Operations Console and Visibility: Provides a centralized dashboard to see your active incidents, filter and search by service, priority etc., get situational awareness, and quickly take action. PagerDuty also facilitates team collaboration through virtual war rooms and shared dashboards, improving communication and coordinated response during incidents.

PagerDuty AI Agents: Includes the Agentic Site Reliability Engineer (for classification, context, and guided resolution), Agentic Operations Analyst (for cross-tool data analysis and strategic insights), and Agentic Scheduler (for dynamic on-call shift management). A standout feature is PagerDuty’s Agentic AI capabilities, which set it apart by delivering advanced automation and intelligent incident management.

Best for: Teams that need fast, noise-reduced incident response with automation and real-time visibility.

8- Splunk

Splunk is a leader in data analytics and security. With its AIOps platform, Splunk ITSI, it offers a comprehensive solution for IT operations and security. This platform delivers predictive insights and service-level intelligence for complex enterprise environments by analyzing millions of data points collected from across your IT infrastructure.

Splunk AIOps Key Features:

IT Service Intelligence (ITSI): A standout feature, ITSI provides service-level monitoring, event and incident management to track health and performance across distributed systems, including deep monitoring of servers.

Predictive analytics: Uses machine learning to forecast potential issues and prevent outages before they impact users by leveraging vast data points.

Event correlation: Consolidates and analyzes events from diverse data sources to accelerate root-cause identification.

Security and IT synergy: Combines AIOps with Splunk’s core strengths in SIEM and security analytics.

Enterprise-grade integrations: Works seamlessly with ServiceNow, AWS, Azure, Google Cloud, and more.

Best for: Large enterprises and managed service providers with complex IT environments and a need for scalable monitoring, deep data analysis, and robust support for servers that bridges operations and security.

AIOps is reshaping how enterprises manage IT – bringing automation, intelligence, and efficiency to the heart of operations. From reducing alert fatigue to accelerating root-cause analysis, the benefits are clear: faster resolution times, lower costs, and more resilient systems.

In today’s complex, hybrid IT environments, AIOps is no longer optional – it’s becoming essential. The organizations that embrace it now will be the ones best positioned to innovate, scale, and deliver superior digital experiences.

Now is the time to take action. Start evaluating AIOps vendors to find the solution that aligns with your goals, integrates with your ecosystem, and empowers your IT teams to thrive in the era of intelligent automation.