When talking about IT, terms like tools and processes are quite common. Another important term is “platform.” However, unlike a tool which is a piece of software, a platform is a comprehensive suite of tools. These tools are compatible and interact with each other. Some of the tools in a platform act like services, while others act like interfaces.
An AIOps platform consists of a set of AI processes. An AI-driven interface connects with AI-based services to give a solution. Some AIOps platforms also offer APIs and plugins to connect to other systems.
Some of the characteristics of AIOps platforms include –
- A set of AI-based tools
- Ability to process large chunks of data
- Machine learning capabilities
- Decision trees
- Stored solutions
- Data access interface
- Data visualization capabilities
With all these qualities, AIOps platforms have become especially important for IT operations, regarding increased performance and efficiency. As their importance continues to grow, there is an increase in the delivery options available for AIOps platforms. We will discuss the top 10 AIOps platforms available.
The primary goal of AIOps platforms is to provide useful data insights so the IT and operations teams can make faster decisions, resolve issues continuously, and keep applications running.
Apart from eliminating data siloes, AIOps platforms use monitoring and observability capabilities to offer accurate context and also provide necessary engagement.
For this, AIOps platforms have certain key components, including data sources, discovery and monitoring tools, engagement and context capabilities, and automation. Using all these components, an AIOps platform collects and runs the data over ML and analytic tools to automate it. The final output is enhanced IT operations.
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Below is a list of the top AIOps platforms that leverage the power of artificial intelligence and machine learning to analyze huge volumes of data and serve as a centralized platform for teams to be able to access it –
1. AppDynamics
This website monitoring service uses a series of specialized modules to fulfill its job. AppDynamics’ modules include Application Performance Monitoring, Infrastructure Monitoring, Business Performance Monitoring, End User Monitoring, and Database Monitoring. Using these modules, AppDynamics helps track and visualize customer journeys.
Key features
- Real-user monitoring
- Powerful alerting
- Code-level visibility
- Root cause analysis
2. Big Panda
This is an AIOps powered event correlation and automation platform. Big Panda collects from diverse sources, such as tools and technologies, change management, and orchestration, and then integrates the data into a single Open Hub.
Key features
- Open Box Machine Learning for event monitoring and alerts
- Root-cause analysis
- Automated incident response
3. Datadog
This cloud-driven data management and monitoring platform cater to IT teams, developers, and security engineers. Datadog comes with a Watchdog module that duels as a root cause analysis tool as well as a system assistant. Along with application performance management (APM), Datadog also provides security, log management, network, and real-user monitoring.
Datadog helps monitor a host of resources, including containers, databases, network devices, applications, servers, cloud resources, etc. What makes it better is that Datadog can integrate with 250 built-in services and tools.
Key features
- Automates infrastructure monitoring
- Real-time observability
- Built-in ML for environment management and monitoring
- Platform integration
4. DynaTrance
Dynatrace is an AIOps-powered intelligent APM platform that offers a host of modern IT services. These services include automation, infrastructure, cloud monitoring, and digital experience monitoring. Its AI causation engine Davis offers insights on the underlying infrastructure, application performance, and also on user experience.
It allows monitoring of the entire IT infrastructure, including the network, processes, and hosts. It can be used for log monitoring, information viewing, and deep data insights. Along with the insights, DynaTrance also suggests “recommended” actions to solve problems.
Key features
- AI-powered digital business analytics
- AI-driven APM and microservices monitoring
- Digital Experience Monitoring (DEM)
- Real User Monitoring (RUM)
- Cloud Automation
5. IBM Instana Enterprise Observability
Instana, which is now a core component of IBM’s AIOps portfolio, is a full-stack observability platform. The platform offers enterprise observability to enhance application performance monitoring (APM). It is designed specifically for cloud-native and microservices applications. Instana automates and accelerates CI/CD platforms for these applications irrespective of the application’s location.
It collects observability-driven performance data from different cloud environments and offers AI-driven root cause analysis. It then uses automation to provide actionable insights.
Key features
- Automated data discovery and monitoring
- Automated root cause analysis
- Optimizes data availability and performance
- Delivery of actionable intelligence
6. LogicMonitor
This is a SaaS-based network monitoring and APM platform that comes with over 1000 built-in automation monitoring and alerting tools. This platform operates from the cloud and can monitor all types of cloud-based infrastructure. By deploying AI in its IT operation management, LM provides maximum process automation. This is the key feature that makes it a top AIOps platform.
It comes with an AIOps early warning system for anomaly detection. It uses the ML process to adjust its behavior baseline. Additionally, it also provides root cause analysis and AI-based baselining.
Key features
- Incidence response capabilities
- Allows for capacity planning
- Facilitates demand forecasting
- Advanced Cloud monitoring capabilities
- Forecasting and trend prediction
7. Moogsoft
Headquartered in San Francisco, Moogsoft is a data management and alert management platform. This is an enterprise cloud-native AIOps platform that also provides observability.
Moogsoft helps IT Ops and DevOps teams reduce noise, prioritize events, decrease escalations, and assure availability by combining visibility and control of monitoring tools. For this, it uses noise-reduction algorithms. Additionally, Moogsoft uses deduplication to bring context to data.
Key features
- Real-time data monitoring and analysis
- Early incident and anomaly detection
- Cleans and enriches for faster incident detection
- Pattern identification
- Out-of-the-box integrations
8. New Relic One
This is a popular cloud-driven Observability platform that facilitates easy, centralized visualization, optimization, and troubleshooting for the entire software stack. The AIOps platform helps keep track of all the distributed applications/ services, irrespective of where they are deployed.
Additionally, its Telemetry Data Platform offers a centralized, real-time view of the operational data.
Key features
- Data analysis and alerts
- Uses ‘Applied Intelligence’ to help detect issues and their cause in real-time
- ML-based root cause analysis
- Incident management
9. Splunk Enterprise
Splunk Enterprise is designed to provide better access, automation, end-to-end service monitoring, and powerful analytics across multiple cloud infrastructures with a strong belief that ‘data is everything.’ This platform facilitates data definition so that the data can be collected automatically from specified sources. Upon data collection, it offers data visualization and indexing so that the data can be accessed with ease.
Key features
- Visualized metrics
- Real-time alerting
10. Zenoss Cloud
Zenoss Cloud is a full-stack monitoring + AIOps platform that provides deep insights on data health and performance. The SaaS-based intelligent application offers robust AIOps analytics capabilities. It also offers full-stack monitoring capabilities for all IT environments.
Zenoss is agentless and uses a collector tool to collect system data fed to a central server for analysis.
Key features
- Full-stack monitoring
- Root-cause analysis using ML
- Real-time alerts
- Solution recommendations
- Use real-time models
Conclusion
Remember that not all AIOps platforms are made equal. You will have to select the one that suits your organizational needs the most.
If you plan to get started with AIOps platforms, you can choose from the above list. Do you have more AIOps platforms in mind? Let us know.
You might also like:
- The Role of AIOps in Digital Transformation – AIOPs have become inevitable for the success of B2B SaaS businesses. In this blog, we look at how AIOPs can help in digital transformation.
- To understand how SmartKarrot can helps SaaS companies keep and grow loyal customers, Request a Demo.
Shivani is a talented CS manager with the skillsets to elicit, scope and manage end-to-end B2B SaaS project delivery. She has a keen interest in depicting her learnings in customer success by writing resourceful blogs and articles.
Published April 14, 2022, Updated August 27, 2024
