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AIOps is more than just another tech buzzword. It’s a practical solution to a real problem: IT operations are becoming increasingly complex, IT infrastructure is massively growing, and simple methods aren’t cutting it anymore.

And that’s where AIOps comes in.

AIOps tools combine machine learning (ML) and artificial intelligence (AI) to manage IT infrastructure effectively. For example, AI Ops can automate incident management by continuously monitoring and detecting anomalies, generating and assessing alerts based on severity, and categorizing and assigning incidents for swift resolution.

In this guide, we’ll skip the hype and show you what AIOps really means for your business. You’ll learn:

• How AIOps compares to traditional ITOps;
• The top 15 AIOps tools that are really worth your time;
• Several concrete AIOps solutions examples (something you’ll rarely find in other articles!);
• Free templates for getting started with AIOps using n8n.

Whether you’re a seasoned IT pro or a business leader trying to make sense of AI buzz, this article will give you the straight talk on AIOps.

Let’s dive in!

What is AIOps?

Let’s take a look at what ITOps do.

ITOps covers a wide range of activities, from managing networks to monitoring virtual and physical components in a company’s IT environment.

The Disciplined Agile Toolkit identifies six key areas of IT operations. The following table explains how AIOps transforms each of these areas:

Decision Point	Traditional ITOps	AIOps
Run solutions	Manual monitoring and reactive problem-solving	Proactive monitoring with predictive analytics and automated problem detection
Manage infrastructure	Manual tracking and updates, often siloed	Holistic view of infrastructure with automated updates and intelligent resource allocation
Manage configurations	Manual maintenance of configuration and dependency metadata	Automated discovery and mapping of configurations and dependencies, with real-time updates
Evolve infrastructure	Planned, periodic upgrades with potential for disruption	Continuous, adaptive evolution with minimal disruption, guided by AI-driven insights
Mitigate disasters	Predefined disaster recovery plans, periodic testing	Dynamic disaster response with real-time adaptation, continuous scenario testing through chaos engineering
Govern IT operations	Rule-based governance with manual auditing and reporting	AI-driven governance with automated compliance checking and intelligent decision support

Essentially, AIOps shifts IT operations from reactive to proactive, from manual to automated, and from rule-based to intelligence-driven.

By leveraging AI and ML, AIOps tools can process large amounts of data from various sources, identify patterns, predict issues before they occur and even automatically suggest or implement solutions.

Why is AIOps important?

Now that we understand what AIOps are, let’s explore the tangible benefits of using AIOps across key areas of an organization:

Measurable KPIs

• AIOps significantly improves MTTD and MTTR, reducing the mean time to detect and resolve issues.
• It boosts system availability, helping businesses achieve higher uptime.
• AIOps enhances anomaly prediction, identifying potential issues before they impact the business.
• It improves compliance rates through automated monitoring and reporting.

Business impact

• AIOps reduces IT operational costs through optimized resource utilization.
• It provides real-time, actionable insights for better strategic planning and decision-making.
• AIOps supports business expansion by adapting to growing IT environments.
• It helps prioritize IT initiatives that drive business value by linking IT performance to business outcomes.
• AIOps enhances security with advanced threat detection capabilities.

Internal operations

• It automates routine IT tasks, freeing up staff for more strategic work.
• AIOps breaks down silos, fostering a unified approach to IT management.
• It continuously refines processes based on historical data through ML algorithms.
• AIOps simplifies the management of complex multi-cloud and hybrid environments.
• It improves resource allocation through improved capacity planning.

User experience

• Users experience faster problem resolution, often before they’re aware of the issues.
• AIOps prevents many issues from impacting users through predictive analytics.
• It ensures consistent service quality across the organization through automated processes.

How do AIOps tools work?

AIOps (Artificial Intelligence for IT Operations) tools leverage AI and machine learning to enhance and automate various IT operations tasks. These tools help organizations manage complex IT environments more effectively by analyzing vast amounts of data from various sources.

AIOps tools work in a continuous cycle of Observe, Engage, and Act:

Observe

• Ingest data from various IT systems and monitoring tools
• Use ML algorithms for event suppression, deduplication and correlation
• Perform anomaly detection and predictive analysis
• Visualize data for easier interpretation

Engage

• Automate incident creation in ITSM systems
• Intelligently assign tasks based on skills and availability
• Analyze tasks, agent performance, changes and processes
• Facilitate collaboration among IT teams

Act

• Recommend and execute automated solutions
• Resolve incidents and fulfill service requests
• Orchestrate changes across IT infrastructure
• Provide feedback to improve future actions

This cycle enables AIOps tools to learn from past events continuously, predict future issues and take proactive measures to maintain optimal IT operations.

AIOps achieves these improvements through the use of various ML and AI technologies. In the next section, we’ll explore the specific types of ML and AI that power AIOps tools.

What kinds of ML and AI do AIOps tools use?

Most commercial AIOps vendors provide only vague information about their proprietary algorithms and often use buzzwords like “advanced ML” or “AI-powered analytics” without specifying the actual techniques. This lack of transparency can make it difficult for organizations to effectively evaluate and compare different AIOps solutions.

To gain more concrete insights, we’ve borrowed three practical examples from the “Hands-on AIOps” and open-source AIOps platforms that provide more insight into the underlying technologies

The book “Hands-on AIOps” highlights three key use cases:

• Event deduplication: basic techniques often don’t require complex ML, Simple data management and filtering is often enough to reduce alert noise.
• Automated baselining: this more advanced use case employs time-series ML techniques like ARIMA and SARIMA to handle the shifting baselines (i.e. CPU utilization) in dynamic IT environments.
• Anomaly detection: several techniques including K-means clustering, NLP techniques such as stopword removal and TF-IDF for log messages are used to identify outliers in large datasets.

While these examples provide a good starting point, they don’t cover the full range of techniques used in AIOps. To broaden our understanding, we also examined the open-sourced AIOps tool loglizer.

Loglizer implements a variety of supervised and unsupervised learning models:

• Supervised models include Logistic Regression (LR), Decision Tree and Support Vector Machine (SVM). These are used when labeled data is available, allowing the system to learn from known anomalies.
• Unsupervised models are particularly interesting for real-world scenarios where labeled data is scarce. These include Local Outlier Factor (LOF), One-Class SVM, Isolation Forest, Principal Component Analysis (PCA), Invariants Mining and Clustering. These methods can detect anomalies without prior examples.
• The developers of the tool are also planning to implement deep learning models such as DeepLog and AutoEncoder.

When evaluating AIOps tools, it’s crucial to look beyond the marketing claims and try to understand the underlying technologies. While vendors may not disclose their exact algorithms, they should be able to explain their general approach and how it addresses specific IT operations challenges.

15 top AIOps platforms

Here’s a shortlist of the 15 AIOps tools that we’ll review in this section:

• Full-stack observability platforms:
  • Dynatrace
  • New Relic
  • AppDynamics (Cisco)
  • Datadog
  • Dell APEX AIOPs
  • OpenText IT Operations Cloud
• Event correlation and incident management:
  • BigPanda
  • PagerDuty
  • IBM Cloud Pak for AIOps (formerly IBM Watson AIOps)
• IT service management (ITSM) integration:
  • Splunk IT Service Intelligence
  • ServiceNow ITSM & ITOM
  • BMC Helix
• Infrastructure monitoring and management:
  • LogicMonitor
  • ScienceLogic
  • Zenoss

These tools represent a wide range of AIOps solutions, each with its own strengths and focus.

Full-stack observability platforms:

These tools offer comprehensive monitoring and observability across the entire IT stack, including infrastructure, applications and user experience.

Dynatrace

Dynatrace is a full-stack observability and application performance monitoring (APM) platform. It provides comprehensive monitoring of cloud environments, infrastructure, applications and user experience.

Davis is Dynatrace’s AIOps engine that enables automatic problem detection and root cause analysis. It uses causal AI to understand dependencies and relationships within the monitored environment.

• Full-stack monitoring;
• Flexible deployment options: SaaS, Managed, ActiveGate (proxy between monitored environments and Dynatrace);
• Application performance monitoring capabilities;
• AI-powered analysis with Davis: seasonal baseline model, forecast analysis, anomaly detection models, causal AI for root cause analysis and problem clustering;
• Automatic discovery and instrumentation;
• API Integration with various platforms.

Most of the concerns about Dynatrace were related to pricing.

• On the one hand, it can be quite expensive for smaller companies;
• On the other hand, ROI for smaller deployments could be higher due to usage-based pricing. The running costs for large deployments increase due to the volume, while the positive effect of running the platform could stay the same.

• Pricing is quite complex and is based on resource usage with additional costs for various features.

New Relic

New Relic is another full-stack cloud-based observability platform.

The platform offers more than 30 observability capabilities, with a focus on application performance monitoring (APM), infrastructure monitoring, log management and digital experience monitoring.

New Relic’s AIOps features include anomaly detection, predictive analytics and automated root cause analysis, enabling IT teams to proactively identify and resolve issues before they impact end-users.

• Powerful AIOps capabilities for automated insights and issue resolution;
• Flexible, usage-based pricing model;
• Extensive integration ecosystem (750+ integrations);
• Robust data query (via NRQL language) and custom dashboard capabilities;
• Supports modern technologies like Kubernetes and serverless.

• The learning curve can be steep for new users;
• No on-premises deployment.

While pricing for AIOps platforms can be complex, New Relic has a breakdown comparison of total ownership cost.

• Free tier: 100 GB/month data ingest, 1 full platform user, unlimited basic users;
• Usage-based pricing beyond the free quota;
• Enterprise tier: Custom pricing, additional security and support features.

AppDynamics (Cisco)

Cisco AppDynamics is an application performance monitoring (APM) and AIOps platform. It offers full-stack observability, combining infrastructure monitoring, application performance monitoring and business performance monitoring.

The platform’s AIOps capabilities are built on the Cisco Observability Platform, providing end-to-end visualization of applications and infrastructure along with incident correlation and event monitoring.

AppDynamics uses machine learning for anomaly detection and automated root cause analysis, which can reduce Mean Time To Resolution (MTTR) for application performance issues. Its dynamic baselines feature automatically calculates baseline performance for applications, enabling the detection of anomalous conditions without manual configuration.

• Advanced AIOps capabilities with ML-driven anomaly detection;
• Automated root cause analysis for faster problem resolution;
• Dynamic baselining without manual configuration;
• Strong integration with Cisco’s broader technology ecosystem.

• Can be complex to set up and configure for large, distributed environments;
• Pricing may be higher compared to some competitors;
• Some advanced features are only available in higher-tier editions.

AppDynamics offers several pricing tiers based on the CPU cores of the system:

• Infrastructure Monitoring Edition: starts at $7/month per CPU Core;
• Customized quotes are available for enterprise-scale deployments;
• Additional modules like Real User Monitoring and Cisco Secure Application are priced separately.

Datadog

Datadog is a monitoring and analytics platform that provides full-stack observability for cloud environments, on-premises infrastructure and applications.

The AIOps capabilities are powered by Watchdog, Datadog’s AI engine, which provides automated alerting, anomaly detection and root cause analysis across the entire IT stack.

• Comprehensive insight into complex, distributed systems;
• Strong AI/ML capabilities for proactive issue detection;
• Extensive integration ecosystem with over 750 services;
• Highly customizable dashboards.

• Setup and configuration for full utilization can be complex;
• Pricing can get expensive as we found in multiple discussions;
• Certain users complained about non-convenient user roles permissions.

Datadog’s pricing model is highly granular, combining both resource-based and usage-based pricing across its various products and features.

This granularity can quickly become difficult to understand. For a more comprehensive understanding of the total ownership costs, you may want to check the comparison with New Relic provided earlier in this article.

Dell APEX AIOPs

Dell APEX AIOps is an AI-driven observability and incident management suite designed to simplify IT operations, increase agility and provide greater control over digital infrastructure. It’s the result of Dell’s acquisition of Moogsoft, integrating Moogsoft’s powerful AIOps capabilities into the existing Dell APEX offering.

The suite consists of three main components:

• Infrastructure observability
• Application observability
• Incident management (formerly Moogsoft)

The Incident Management component is particularly noteworthy, as it leverages advanced AI to reduce alert noise, identify root causes, improve collaboration across IT teams and automate remediation processes.

• Covers infrastructure, applications and incident management;
• Significant event noise reduction;
• AI-driven root cause analysis and remediation recommendations;
• Integration with 100+ third-party IT tools;
• Customizable workflows for events, alerts and incidents;
• Historical comparison of incidents for faster resolution.

• May require extensive setup and configuration for optimal performance;
• Potential learning curve for teams transitioning from traditional ITOps tools;
• As a relatively new integrated offering, it may still be evolving and refining its features.

Dell APEX AIOps is offered as a Software-as-a-Service (SaaS) solution. For specific pricing information, customers need to contact Dell sales representatives, as pricing can vary based on the scale of deployment and specific features required.

OpenText IT Operations Cloud

OpenText IT Operations Cloud is a platform designed to modernize and optimize IT operations across hybrid and multi-cloud environments. Within this platform, OpenText provides three key products that focus specifically on AIOps and observability:

• Operations bridge
• Infrastructure observability
• Application observability

These solutions work together to provide full-stack visibility, AI-driven insights and automated remediation capabilities.

• AI-based event and performance management;
• Embedded automation for remediation;
• End-to-end monitoring for cloud and on-premises infrastructure;
• AI-powered anomaly detection;
• OpenTelemetry-based application performance monitoring.

Based on G2 user reviews we found two most common complains:

• Some users report that the user interface could be more intuitive;
• Integration with third-party tools may be limited or require additional effort.

Exact pricing is not publicly disclosed and varies depending on the products selected, deployment model and scope of implementation. OpenText offers flexible licensing options, including SaaS subscriptions. Multi-year commitments may provide better pricing. Organizations should contact OpenText sales for a customized quote tailored to their specific requirements and scope.

Event correlation and incident management:

These tools specialize in noise reduction, event correlation and streamlining incident management processes.

BigPanda

BigPanda is an AIOps platform with a strong focus on event correlation and incident management.

The platform offers a comprehensive suite of features including alert correlation, root cause analysis and automated incident triage. Its AI-driven approach helps to reduce alert fatigue and improve mean time to resolution (MTTR).

BigPanda also offers integration capabilities, allowing it to work seamlessly with a wide range of monitoring tools and IT service management platforms.

• Powerful event correlation engine that significantly reduces alert noise;
• Comprehensive incident management features including automated triage;
• Integration capabilities with various monitoring and ITSM tools;
• User-friendly interface with customizable dashboards.

Several reviews on G2 indicate potential weak points of the platform:

• May require significant fine-tuning to optimize for specific environments;
• Limited customization options for certain features;
• Some users have reported integration issues with certain tools.

BigPanda’s pricing is not publicly disclosed on the website. Some user reports on Capterra suggest that pricing can start around $6,000 per year.

PagerDuty

PagerDuty is one of the leading incident management and AIOps platforms that helps organizations reduce operational noise and automate incident resolutions.

The platform’s key strengths lie in its robust feature set, which includes intelligent alert grouping, noise reduction and accelerated triage capabilities. The system also offers advanced event orchestration, allowing teams to create sophisticated routing and enrichment rules across their entire IT ecosystem.

• Comprehensive incident management and AIOps features;
• Advanced ML-powered alert grouping and noise reduction;
• Integrations with other tools and services;
• Robust mobile app for incident management on the go;
• Real-time visibility console for monitoring.

Similarly to BigPange, we sourced critical reviews from G2 and reddit. Here’s what users reported:

• While the platform itself is not the most expensive, it was still considered pricey when used mostly for alerting purposes;
• Could be complex in setup and configuration for non-tech users;
• The seat-based licensing model can be limiting for larger organizations.

• PagerDuty AIOps starts at $699 per month, with pricing based on usage;
• Several add-ons are priced per user.

Contact sales team for detailed pricing information.

IBM Cloud Pak for AIOps (formerly IBM Watson AIOps)

IBM Cloud Pak for AIOps is an AI-powered platform designed to transform IT operations across hybrid cloud environments. It helps ITOps managers and Site Reliability Engineers (SREs) to make incident management and remediation more effective.

The platform offers a range of tools for proactive IT management, including anomaly detection, application topology mapping and automatic baselining.

IBM Cloud Pak for AIOps is part of IBM’s broader Cloud Pak family, built on Red Hat OpenShift, which allows for flexible deployment across on-premises, cloud or hybrid environments.

• Strong feature set, especially for anomaly detection and event correlation;
• Comprehensive data management capabilities;
• Provides valuable insights for proactive IT management;
• Integrates well with existing IBM ecosystems.

• Requires significant time investment to fully leverage all features;
• May be overwhelming for teams without prior AIOps experience;
• Can be expensive.

IBM Cloud Pak for AIOps uses a modular pricing model, allowing customers to pay only for the components they need. Contact the sales team for more details.

AWS Marketplace page suggests a starting price of $12 000/year for a small initial deployment (100 resource units).

IT service management (ITSM) integration

These platforms offer strong integration with ITSM processes and tools, focusing on aligning IT operations with service management.

Splunk IT Service Intelligence

Splunk IT Service Intelligence (ITSI) is a service monitoring and analytics solution built on the Splunk Enterprise platform. It provides insight into the health and performance of IT services.

ITSI calculates health scores for services based on key performance indicators (KPIs), offering service-oriented dashboards and analytics that go beyond individual metrics. The platform leverages ML for predictive analytics and anomaly detection to help organizations predict and prevent service disruptions.

ITSI also integrates with event management and IT Service Management (ITSM) tools, streamlining incident response and resolution processes.

• Flexible KPI and service modeling;
• ML capabilities for predictive insights;
• Integrates data from different sources and tools;
• Customizable dashboards and glass tables;
• Automated event correlation and incident prioritization.

• Requires significant setup and configuration;
• Can be complex to implement and maintain;
• Needs careful tuning of KPIs and thresholds;
• Only suitable for very mature companies with complex IT environments.

Splunk offers several flexible pricing options, these include:

• Workload-based pricing
• Ingest-based pricing
• Other customized models

Splunk ITSI requires both a Splunk Enterprise license and an additional ITSI-specific license. Contact Splunk sales directly for a customized quote.

ServiceNow ITSM & ITOM

ServiceNow offers a comprehensive suite of IT management tools on its Now Platform, with IT Service Management (ITSM) and IT Operations Management (ITOM) as two key components. These solutions work together to provide a unified approach to managing IT services, infrastructure and operations.

ITSM focuses on the delivery and support of IT services, while ITOM provides insight into the IT infrastructure and helps to prevent service outages. Together, they create a powerful ecosystem for modern IT departments:

• Comprehensive solution that covers both service management and operations;
• Integration between ITSM and ITOM components;
• Strong AIOps capabilities for predictive issue resolution;
• Automated service mapping and discovery;
• Single platform for traditional IT, DevOps and SRE practices;
• AI-powered self-service options and virtual agents.

• Higher cost compared to individual point solutions;
• Significant investment in the broader ServiceNow ecosystem for full value;
• Steep learning curve and potentially long implementation time;
• Requires an experienced team with ITSM expertise.

ServiceNow does not publicly disclose pricing. Both services are based on a subscription model and require custom quotes.

BMC Helix

Similarly to ServiceNow, BMC Helix is an integrated suite of IT solutions designed to unify IT service and operation management, as well as workflow orchestration and solutions for mainframes.

BMC Helix aims to unify IT service management (ITSM) and IT operations management (ITOM) into a single platform. At its core, the platform provides AI-driven incident management and problem resolution, enabling faster and more accurate responses to IT issues. The service desk is enhanced with automation capabilities and virtual agents, reducing manual workload and improving response times.

• AI/ML features for automation and predictive analytics;
• Flexible deployment options (SaaS, on-premise, hybrid);
• Robust integration ecosystem;
• Scalable for large enterprises.

• Complex licensing model;
• The steep learning curve for administrators;
• UI feels dated according to the user reviews;
• High cost, especially for full feature set.

BMC Helix pricing is customized for each customer and not publicly available. Additional search on forums did not reveal many details.

Infrastructure monitoring and management

These tools have a strong focus on infrastructure monitoring, including networks, servers and cloud resources.

LogicMonitor

LogicMonitor is a SaaS-based hybrid observability platform that provides monitoring for on-premises infrastructure, cloud services and applications.

AIOps capabilities, branded as Edwin AI, enable the platform to provide real-time insights, automate alert correlation, and offer predictive analytics to help prevent potential issues before they impact business operations.

LogicMonitor is an agentless solution. A lightweight LogicMonitor Collector (a 100MB Java application) is required in each location of the infrastructure. The Collector then monitors resources within the infrastructure using standard monitoring protocols.

• Monitoring capabilities for diverse IT environments;
• Strong integration with thousands of other services and tools;
• AI-powered analytics and anomaly detection (via Edwin AI);
• Customizable dashboards and reporting.

• Complex pricing structure based on resource types and volumes;
• Several redditors reported potential pitfalls when running on AWS (cloudwatch pricing may spike).

While granular pricing gives a detailed overview of costs per resource being monitored and the log volumes, the overall costs remain unclear. Vendor page suggests an average contract value of around $36,000 annually.

ScienceLogic

ScienceLogic offers an AIOps platform called SL1 that provides monitoring, automation and AI-powered analytics for hybrid cloud environments. The platform aims to give IT teams visibility across complex infrastructures and help reduce incident response times.

After the acquisition of Zebrium, an AI-powered root cause analysis tool, ScienceLogic integrated it as “Skylar AI” within the platform. This addition enhances SL1’s ability to automatically detect anomalies and identify the root cause of issues.

• Comprehensive monitoring across hybrid cloud environments;
• Strong topology mapping and service dependency visualization;
• AI/ML capabilities for anomaly detection and root cause analysis.

• Complex UI with multiple interfaces that can be confusing;
• Several G2 reviewers reported frequent false positives/alarms;
• Latency in asset status updates due to SNMP dependency;
• According to the reviews, PowerShell/WMI collection can cause CPU spikes on monitored Windows systems.

ScienceLogic offers tiered pricing plans:

• Hybrid Cloud Monitoring (Base)
• Service Centric Operations (Standard)
• Intelligent Automated Operations (Premium)

Exact pricing is not publicly available and requires contacting sales. The Skylar Automated RCA (formerly Zebrium) component may be priced separately.

Zenoss

Zenoss is a powerful infrastructure monitoring and AIOps platform, designed to provide visibility and intelligent insights across complex IT environments.

Similarly to other platforms, it leverages machine learning to enhance infrastructure monitoring with predictive insights, anomaly detection and automated root cause analysis.

With the wide array of ZenPacks, Zenoss expands its capabilities and integrates with hundreds of different systems.

• Monitoring across hybrid environments;
• Strong service mapping and dependency visualization;
• AI/ML capabilities for predictive analytics and anomaly detection;
• Highly extensible through ZenPacks.

• Can be complex to set up and configure initially;
• Learning curve for utilizing all features effectively;
• Discontinued open-source community edition;
• Resource-intensive, may require significant hardware.

Zenoss does not publicly disclose pricing. Additional search did not reveal any recent information.

n8n for AIOps

While AIOps tools are beneficial, some of them face limitations such as integration difficulties, high costs, and restricted customization options, which can hinder accessibility for certain organizations. n8n addresses these challenges by offering extensive integration capabilities, a user-friendly low-code interface, and a self-hosted version for enhanced privacy.

n8n is a source-available workflow automation platform. Its flexibility and integration capabilities make it suitable for implementing several AIOps strategies.

First, n8n complements existing AIOps platforms in several ways:

• Collect, transform, and prepare data from diverse sources, feeding it into specialized AIOps tools or custom analysis pipelines.
• Automate incident response workflows: create sophisticated automation that integrates with existing IT tools, notification systems and chat platforms. This streamlines the incident management process beyond the built-in capabilities of AIOps tools.
• Easy integration with AI and ML services, including various Language Models (LLMs). This feature enables powerful Natural Language Processing (NLP) for the analysis of logs, helpdesk messages and other unstructured data sources common in IT operations.

Second, n8n is great both for DIY AIOps prototypes and fully in-house developed AIOps solutions:

• With the Execute Command, Code, SSH nodes, you can fuse n8n into every step of the AIOps pipeline: from data aggregation to calling external ML scripts (every programming language supported!) to automatic incident response.
• With our recent self-hosted AI starter kit you can create a completely air-gapped AI environment: perform advanced NLP even on the most sensitive data.

• Highly flexible and customizable, allowing for tailor-made AIOps solutions;
• Cost-effective alternative for implementing or prototyping AIOps functionalities;
• Strong data preprocessing capabilities for diverse data sources;
• Easy integration with AI/ML services, including LLMs for NLP tasks;
• Powerful automation features for incident response and management;
• Self-hosting options, providing control over data and deployment.

• Limited specialized AIOps features compared to dedicated platforms (e.g., built-in anomaly detection, pre-configured event correlation);
• While perfect for batch processing and single event processing, n8n may face challenges with real-time data processing in large-scale AIOps scenarios;
• Community support might lack deep AIOps-specific expertise;
• Requires additional configuration efforts for integration with specialized IT monitoring tools or ML platforms.

• n8n offers a free Community version for self-hosting;
• The most advanced enterprise tier comes with both managed or self-hosted deployment options. Pricing is available upon request;
• Other cloud tiers begin with 24€/mo.

Take a look at several workflow examples suitable for ITOps / AIOps:

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FAQ about AIOps

Wrap up

In this article, we covered AIOps tools and their impact on IT operations. We explained AIOps basics, reviewed 15 top platforms in four categories, and showed how to use AI to improve IT efficiency.

We also explored how n8n, a workflow automation tool, can be used for AIOps tasks. While not a dedicated platform, it’s a flexible, cost-effective option for organizations starting with AIOps or needing custom solutions.

What’s next?

To continue your AIOps journey, explore free n8n’s workflow templates in various categories like DevOps, IT, AI, SecOps, and Engineering at. These templates can help you implement AIOps concepts using n8n

For further reading on related topics:

• Learn about AI orchestration: benefits, best practices, and tools to align AI capabilities with business objectives.
• Explore top workflow automation tools for IT, SecOps, and DevOps.

Ready to start your AIOps journey with n8n? Choose the option that best fits your needs:

• Try n8n Cloud with a free trial;
• Self-host n8n with our Community or Enterprise Edition (for advanced security and scalability).