12 Best AIOps Platforms of 2024
I’ve used various tools that made my job as an IT administrator much easier. Here’s what I think are the best artificial intelligence for IT operations (AIOps) platforms.
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Best AIOps Shortlist
Here’s my shortlist of the top AIOps platforms that I’ll cover in this article, with my full review for each below:
- IBM Instana Observability - Best for automated full-stack observability
- LogicMonitor - Best for root cause analysis and anomaly detection
- New Relic - Best for system alerts and notifications using machine logic
- Digitate - Best closed-loop automation system
- Splunk - Best for multi-cloud environments
- Elastic Observability - Best for accelerating root cause analysis
- Coralogix - Best for ensuring data security compliance
- PagerDuty - Best for building AI-enabled workflows with integrations
- Dynatrace - Best for enterprises to scale their operations with AI and ML
- BigPanda - Best for gaining operational insights across your infrastructure
- Moogsoft - Best for leveraging AI and ML to automate incident response
- ServiceNow - Best for a range of AI-powered capabilities
As an IT professional, you’re under immense pressure to keep your company’s operations running smoothly. You have to monitor network performance, respond to incidents, and conduct root cause analysis to identify solutions. How can you streamline these processes and resolve issues faster? The right AIOps platform can help.
Below, I’ve evaluated the best AIOps platforms. I’ll cover the key features of each tool and why I think they deserve a spot on this list. I’ll also answer a few questions about AIOps and explain the selection criteria I followed.
What Are AIOps Platforms?
AIOps platforms are software solutions that use artificial intelligence (AI) and machine learning (ML) to automate IT processes. Advanced algorithms enable these platforms to detect issues, conduct root cause analysis, and propose solutions faster than humanly possible. This allows IT teams to quickly respond to incidents and reduce the mean time to resolve (MTTR).
Overviews of the 12 Best AIOps Platforms
Here’s a brief description of the best AIOps platforms on the market. I’ve highlighted noteworthy features and provided screenshots to give you a feel of what each is like.
1. IBM Instana Observability - Best for automated full-stack observability
IBM Instana Observability is an AIOps platform that provides comprehensive application and infrastructure monitoring. Its AI-powered solution proactively identifies and resolves issues before they affect end users.
Why I picked IBM Instana Observability: I picked IBM Instana Observability because it gives you full visibility of your entire IT infrastructure. It automatically consolidates and aggregates data from sources like applications, servers, and components. Customizable dashboards and interactive visualizations provide a clear picture of your environment.
IBM Instana Observability Standout Features and Integrations:
Features that I believe differentiate IBM Instana Observability from its competitors include its AI-powered root cause analysis, which automatically discovers and maps application dependencies if it detects an issue. This makes getting to the root cause of an issue more efficient than manually trudging through your data. You can also set up Smart Alerts based on predefined blueprints to receive alerts of issues like application slowness, JavaScript errors, and HTTP status codes.
Integrations are available natively with over 300 systems. These include Amazon Corretto, Apache CXF, ClickHouse, OpenSearch, Oracle, Redis, Solaris, and PagerDuty.
Pricing: From $75/host/month (12-month minimum service term)
Trial: 14-day free trial
Pros
- Automates root cause analysis and anomaly detection
- Dashboards and visualizations provide actionable insights
- Supports over 300 network and monitoring tools
Cons
- Steep initial learning curve
- Some users report high CPU usage
2. LogicMonitor - Best for root cause analysis and anomaly detection
LogicMonitor is a cloud-based infrastructure monitoring platform with AIOps capabilities that help organizations prevent outages and streamline their operations.
Why I picked LogicMonitor: I put LogicMonitor on this list for its anomaly detection. The system applies machine learning to historical data to detect anomalies outside normal patterns. Dynamic thresholds ensure that you only receive alerts for critical issues.
LogicMonitor Standout Features and Integrations:
Features that I think make LogicMonitor stand out from other AIOps platforms include its data forecasting tool, which uses historical data to make predictions about your infrastructure. For example, you can use it to anticipate when the disk space on a server will run out. These insights can help you take a more proactive approach to preventing outages.
Integrations are available natively for over 2,000 platforms. Notable integrations include Microsoft Azure, Google Cloud Platform, VMware, AWS, ServiceNow, Citrix, Fortinet, Java, MySQL, and ConnectWise.
Pricing: Pricing upon request
Trial: 14-day free trial
Pros
- Offers an intuitive and simple user interface
- Enables real-time monitoring of network devices, servers, and applications
- Native integrations for 2,000 platforms and services
Cons
- Lack of customization for the monitoring templates
- Some users report additional development work for certain integrations
3. New Relic - Best for system alerts and notifications using machine logic
New Relic is a network observability platform that allows you to monitor the health of your infrastructure. It automatically detects anomalies and correlates incidents to streamline troubleshooting.
Why I picked New Relic: I picked New Relic because it does an excellent job at cutting through the “noise” and minimizing false positives. The platform uses an AI-powered correlation engine that reviews incidents and groups related issues to create a single alert. You can easily configure workflows to notify and provide relevant context to the right people so they can get straight to work.
New Relic Standout Features and Integrations:
Features that make New Relic stand apart include its issues feed, which provides a clear overview of the issues the system detected. I could drill down into each issue and get specifics like issue duration, entity type, and user actions. I also found its “postmortem” feature useful as it provided insights into what worked and what didn’t when responding to an incident.
Integrations include native options for various platforms and systems, including Amazon ECS, Elasticsearch, Synk, Kubernetes, Azure Batch, Google BigQuery, Kamon, and Comet.
Pricing: From $49/user (extra $0.30/GB beyond 100GB)
Trial: Free plan available
Pros
- Provides full-stack observability across your infrastructure
- Offers simple and transparent pricing plans
- Easy to set up and configure
Cons
- Some users report inadequate documentation for advanced features
- Can be costly for small businesses
4. ignio AIOps - Best closed-loop automation system
ignio AIOps is a cloud-based AIOps platform that uses AI and machine learning (ML) to help enterprises automate IT processes across hybrid environments.
Why I picked ignio AIOps: I chose ignio AIOps for its closed-loop capabilities, which detect network anomalies, resolve incidents, and implement resolutions based on future states. This helps address issues before they can escalate.
Ignio AIOps Standout Features and Integrations:
Features that stood out to me about ignio AIOps include its intelligent alert management; it uses AI-based logic to prioritize alerts based on their business impact. The system continuously learns from your actions to better manage alerts. I also liked that ignio AIOps recommends ways to optimize your infrastructure and improve capacity planning to meet future demands.
Integrations are available natively for platforms like AppDynamics, Microsoft Azure, Sumo Logic, Enterprise Bot, Beroe, and NetSpyGlass.
Pricing: From $15,000 (one-time payment)
Trial: Free trial + Free demo
Pros
- Uses AI-based behavior profiling to identify outliers
- Provides recommendations to optimize your infrastructure
- Can query data from SAP and non-SAP systems
Cons
- Lack of pre-built automations
- Doesn’t easily integrate with DevOps pipelines
5. Splunk - Best for multi-cloud environments
Splunk is an observability platform that uses AI and ML to detect anomalies and automate incident response across hybrid environments.
Why I picked Splunk: I chose Splunk because it offers a robust ecosystem of apps and integrations that enable multi-cloud enterprises to gain full visibility of their infrastructure. Its service-level dashboards allow you to drill down to the code level to make troubleshooting issues more effective.
Splunk Standout Features and Integrations:
Features that impressed me about Splunk include its predictive analytics tool, which uses ML algorithms to predict what the health score of a service may look like in 30 minutes. With these models, I could determine which services required immediate attention before an outage occurs. I also like that Splunk lets you create and add templates to standardize incident responses.
Integrations include pre-built and native applications that are available via Splunkbase — a marketplace of apps and add-ons for Splunk.
Pricing: Pricing upon request
Trial: 14-day free trial
Pros
- Powerful search and analytics capabilities
- Has a range of pre-built and native integrations
- Offers flexible deployment options
Cons
- Uses a resource-intensive architecture
- Requires training for users to utilize all features
6. Elastic Observability - Best for accelerating root cause analysis
Elastic Observability unifies log data, application traces, and infrastructure metrics in one location. It uses AI to automate anomaly detection and reduce manual troubleshooting.
Why I picked Elastic Observability: Pinpointing the root cause of an issue isn’t easy unless you’re willing to scour through large volumes of data. I picked Elastic Observability because it offers an array of preconfigured ML models that can detect anomalies and automate root cause analysis. You can apply these models to all types of application and infrastructure data.
Elastic Observability Standout Features and Integrations:
Features that I want to highlight about Elastic Observability include its library of out-of-the-box integrations that enable you to ingest data from any source, like applications, endpoints, and servers. Another noteworthy feature is the machine learning wizard. The tool walks you through each step, so you can “train” the system and create your own anomaly detection models even if you have little technical expertise.
Integrations are available natively for various platforms, including AWS S3, Azure Logs, GitHub, PagerDuty, Slack, ServiceNow, and Fortinet.
Pricing: From $95/month
Trial: 14-day free trial
Pros
- Supports a range of use cases, from cloud migrations to DevOps
- Enables real-time analysis of log, trace, and event data
- Offers developer-friendly APIs
Cons
- Slow query times for large volumes of log data
- Can be expensive to retain data for longer periods
7. Coralogix - Best for ensuring data security compliance
Coralogix is a log management platform that helps organizations analyze their data at scale and ensure end-to-end security with automated incident response.
Why I picked Coralogix: I put Coralogix on this list because it automates posture assessments to ensure compliance with data security standards like SOC, ISO, and HIPAA. The TCO Optimizer feature lets you designate certain types of logs as compliance data.
Coralogix Standout Features and Integrations:
Features that differentiate Coralogix from other AIOps tools, in my opinion, are its automated vulnerability assessments. This feature uses AI to continuously monitor your log data and detect known security vulnerabilities; if it detects a vulnerability, it’ll display a description of the issue and estimate its severity so you can prioritize and remediate appropriately. I also like that it has 24/7 support built right into the app.
Integrations include over 100 native options to platforms and services like AWS, CircleCI, Jenkins, Microsoft Azure, PagerDuty, Perimeter 81, Cloudflare, and NXLog. You can use its REST APIs to connect to more applications.
Pricing: Pay for what you use
Trial: 14-day free trial
Pros
- Easy to set up pipelines to ingest data from multiple sources
- Full range of features available with every plan
- Offers 24/7 in-app technical support
Cons
- Some users report slow speeds for log searches
- Can be difficult to set up alerts and notifications
8. PagerDuty - Best for building AI-enabled workflows with integrations
PagerDuty is an infrastructure monitoring platform that leverages AI and ML to help companies stay ahead of critical issues and reduce manual processes.
Why I picked PagerDuty: Aggregating data across all the platforms and services you use isn’t easy, which is why I put PagerDuty on this list. It includes native integrations with over 700 tools and step-by-step instructions for popular platforms like AWS. You can also use its Events API v2 to ingest and process different types of event data.
PagerDuty Standout Features and Integrations:
Features that I feel make PagerDuty stand out from other AIOps tools include its intelligent alert grouping, which uses ML to group related alerts into a single incident. This helps reduce alert noise and speed up resolution times. Automation Actions is another feature I found helpful as it allows you to create automated incident response workflows to common issues; however, I should point out that this feature is only available as a paid-on.
Integrations are available natively for over 700 platforms, including AWS, ServiceNow, Salesforce, Zendesk, Atlassian, and Datadog.
Pricing: From $399/month
Trial: Free plan available
Pros
- Allows for on-call scheduling to ensure incidents reach the right people
- Pre-built ML models help reduce incident noise
- Offers an extensive resource library of guides and documentation
Cons
- Free plan has limited functionality
- Automation Actions is charged separately as an add-on
9. Dynatrace - Best for enterprises to scale their operations with AI and ML
Dynatrace is an application monitoring tool that provides full infrastructure observability. Is AI engine monitors your infrastructure in real-time and instantly surfaces anomalies.
Why I picked Dynatrace: I chose Dynatrace because it offers a highly scalable platform that continuously maps your digital environment as your organization grows. Its cloud-native architecture automatically adds new nodes to scale horizontally as needed.
Dynatrace Standout Features and Integrations:
Features that I think can help organizations automate their IT operations include its OneAgent software, which automatically maps your entire environment with a single agent; you won’t have to install multiple agents or manually configure plugins. Another feature worth mentioning is Smartscape, a topological model that creates an interactive map of your infrastructure. This allows you to see all the dependencies between your applications.
Integrations include over 600 native and pre-built options, such as Amazon S3, Ansible, CloudFlare, Databricks, Google Big Query, jQuery, NeoLoad, and Redis.
Pricing: From $0.08/hour (for 8GB host)
Trial: 15-day free trial
Pros
- Automates incident response at scale
- Supports on-premise and cloud deployments
- Offers customizable dashboards and reporting options
Cons
- Not cost-effective for small businesses to implement
- Interface can be difficult to navigate
10. BigPanda - Best for gaining operational insights across your infrastructure
BigPanda’s AIOps tool uses intelligent automation to help organizations detect and resolve IT incidents to ensure continuous service availability.
Why I picked BigPanda: BigPanda has all the features you’d expect from an AIOps platform, like data aggregation, incident analysis, and alert correlation. But the reason I listed BigPanda here is because of its robust analytics and reporting. Its dashboard makes it easy to visualize key trends and understand the operational health of your infrastructure.
BigPanda Standout Features and Integrations:
Features that stood out to me about BigPanda during my testing are its generative AI that automatically creates titles and descriptions for incidents. You can give these descriptions a thumbs or down, which improves the AI-generated summary. I also like that the system conducts an incident analysis and surfaces probable root causes in real time.
Integrations are available natively with various platforms and services, such as Splunk, Nagios, Jenkins, Jira, ServiceNow, Slack, and Asana.
Pricing: From $9/node/month (billed annually via AWS)
Trial: Free demo available
Pros
- Correlates and organizes IT alerts from various monitoring tools
- Performs automated incident analysis in real time
- Offers a user-friendly and intuitive interface
Cons
- Some users report slow response times from customer support
- Documentation isn’t as detailed for certain features
11. Moogsoft - Best for leveraging AI and ML to automate incident response
Moogsoft is a real-time observability and monitoring platform that uses AI and ML to help organizations streamline incident management.
Why I picked Moogsoft: I picked Moogsoft for its automated incident response capabilities. It allows you to build automated workflows with the third-party systems you use. For example, you can build a workflow that automatically routes incidents to a Slack channel with relevant context for IT teams to quickly resolve.
Moogsoft Standout Features and Integrations:
Features that left me with a favorable impression of Moogsoft include its early anomaly detection system, which can detect system changes outside normal parameters as soon as it ingests source data. It automatically assigns color-coded tags to anomalies based on severity level, so you can remediate incidents before they lead to outages.
Integrations include native options for AWS, AppDynamics, New Relic, SolarWinds, PagerDuty, and Slack. You can also create your own custom integrations with Moogsoft’s REST APIs.
Pricing: From $10,000/year
Trial: Free plan available
Pros
- Built-in AI capabilities help identify and resolve incidents faster
- Offers a reliable and scalable platform for processing data
- Receives constant product updates with new features
Cons
- Free plan is limited to 100 incidents per month
- No options to retest or rerun an alert
12. ServiceNow - Best for a range of AI-powered capabilities
ServiceNow is a cloud-based IT operations management platform. It leverages AI to simplify data ingestion and automate incident resolution.
Why I picked ServiceNow: I put ServiceNow here because it offers an array of features that can help organizations manage and optimize their IT infrastructure. It can automatically discover applications and map their dependencies, detect and remediate anomalies, and even optimize resources to reduce cloud spend.
ServiceNow Standout Features and Integrations:
Features that I think make ServiceNow worth considering include its predictive AIOps capabilities, which can instantly identify anomalies as they occur. The platform also offers pre-built actions that you can apply to alerts and speed up remediation. I also found its service health dashboards helpful for understanding which applications were at risk.
Integrations are available natively for platforms like AWS, Google Cloud Platform, Microsoft Azure, Citrix, Okta, Jira, and SAP. You can also use ServiceNow’s REST APIs to integrate with more applications.
Pricing: Pricing upon request
Trial: Free demo available
Pros
- Platform can be customized to fit different use cases
- Facilitates collaboration and information sharing
- Offers apps for iOS and Android
Cons
- Some users report performance issues with the platform
- Setting up integrations may require some technical expertise
Other AIOps Software Options
Throughout my research, I evaluated a wide variety of tools. While these didn’t make my list of the top AIOps, I still think they’re worth checking out:
- Netreo - Best for ease of deploying an AIOps solution
- Datadog - Best for growing startups to leverage AI
- Micro Focus - Best for network health and performance monitoring
- Zenoss - Best for full-stacking monitoring
- OpsRamp - Best for managed service providers (MSP)
- StackState - Best for Kubernetes-based applications
- CloudFabrix - Best generative AI for troubleshooting issues
- ProphetStor - Best for optimizing cloud spend
Selection Criteria For AIOps Platforms
Below, I’ve put together a short summary of the criteria I followed to put together my list of the best AIOps tools:
Core Functionality
I looked for AIOps platforms with the following core functionalities that allow you to:
- Aggregate and consolidate data from any source
- Detect anomalies and send alerts based on monitoring thresholds
- Conduct root cause analysis to improve response times
Key Features
To deliver the core functionalities I highlighted above, I prioritized AIOps tools with the following features:
- Data ingestion: Your data resides across various sources. Any AIOps tool you choose must be able to ingest data from any source, whether they’re on-premise or in the cloud.
- Anomaly detection: Anomaly detection is a key component of AIOps platforms. It uses ML models to detect anomalies that deviate from normal patterns.
- Automated incident resolution: Manually resolving every incident is an impossible task. I looked for tools that include pre-built and custom automations to resolve common incidents at scale.
- Predictive analytics and forecasting: This feature uses historical data to predict future trends. It can help you proactively address future issues before they even appear.
Usability
An important feature of any AIOps tool is ease of use. You don’t want your IT team spending weeks just to become proficient with basic features. I chose AIOps tools with intuitive interfaces that help you quickly get to the root cause of issues. I also picked solutions that made it easy to integrate with various platforms.
People Also Ask
Here are some answers to frequently asked questions about AIOps:
What are the benefits of AIOps platforms?
Is AIOps part of DevOps?
What is the difference between AIOps and RPA?
Final Thoughts
On average, organizations use over 1,000 applications across hybrid cloud environments. Manually monitoring these services for performance issues or security vulnerabilities just isn’t feasible.
Fortunately, AIOps platforms can gather data from multiple sources and detect anomalies in real time, allowing you to remediate issues before they impact your users. Use this list of the best AIOps platforms to find the right solution for your company.
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