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The 8 Leading AIOps Tools To Transform Your IT Landscape

Artificial intelligence operations (AIOps) tools are game changers for ensuring effective IT operations, offering faster alert response and little to no downtime. 

The AIOps market is growing rapidly, standing at $29.97 billion in 2023. With this growth comes the emergence of dozens of new AIOps solutions that help maximize businesses’ operational efficiency. 

Let’s explore the basics of AIOps tools and tips for implementing them within your business to maximize efficiency. We’ll also cover eight powerful AIOps solutions that can help transform your IT operations. 

What do AIOps tools do?

AIOps tools use advanced technologies like Big Data analytics and machine learning to automate IT functions and deliver quality IT services to the end user. The average AIOps tool offers the following benefits: 

Data collection and aggregation

IT teams collect data from logs, servers, and networks, which enables them to manage the IT infrastructure, resolve employee and customer-related issues, and ensure fewer system outages. 

AIOps tools help analyze, categorize, and present the aggregated data in a repository known as a data lake. The result? Easier access, faster analysis, and understanding of data insights.

Reduction in irrelevant data

IT teams juggling information from multiple sources often have to filter out the “noise” manually—the irrelevant or less important data crowds the high-priority alerts, leading to alert fatigue. 

AIOps tools solve this problem by automatically analyzing alerts (using Big Data analytics and machine learning models) and presenting only relevant ones for further action and analysis. This frees up capacity for the IT team to handle more pressing issues.

Anomaly detection

AIOps tools can identify unusual behavior and resolve issues before they escalate into a crisis. This is courtesy of machine learning algorithms that detect strange patterns within a dataset, which could be the onset of a security breach or hardware problems.

Event correlation and analysis

Beyond collecting large volumes of data and aggregating them, AIOps tools conduct a deeper analysis of events (i.e., actions) by picking out their attributes and tracing them to a similar event. The software uses this approach to get to the root cause of incidents in the IT environment. 

Performance and predictive analysis

AIOps tools conduct predictive analysis by working with historical data that provides insights for forecasting. These insights are also helpful for evaluating metrics and setting up measures that proactively fight malicious agents. 

The top 8 AIOps tools to consider

Unlike traditional IT management solutions that struggle to work with large volumes of data and are more reactive when handling ITOps incidents, AIOps tools adopt a different approach, thereby replacing traditional ITOps systems. 

Below, we’ll break down eight AIOps software recommendations for transforming your IT landscape—plus their key features and use cases. 

1. Moogsoft

Moogsoft is a cloud AIOps platform focusing on incident resolution through ML-enabled anomaly detection and IT noise reduction. It stands out because of its patented technologies, including its machine learning algorithms. It focuses on offering immediate return on investment (ROI) to its users by focusing on incident remediation

Moogsoft’s goal is continuous AIOps availability for its users—meaning zero downtime for ITOps teams. It provides:

  • Integration with multiple tools for easier collaboration
  • Automated incident response, which boosts your mean time to restore (MTTR) score
  • Early incident detection, which reduces your mean time to detect (MTTD) score 

2. Splunk

Splunk is an IT service intelligence (ITSI) platform that simplifies AIOps for enterprise IT teams. It’s compatible with multiple applications and cloud solutions, allowing it to collate and analyze data from these data sources. This gives users complete visibility into their KPIs, allowing them to monitor their service health. 

Splunk also uses its historical data for predictive analysis, helping IT teams detect and prevent upcoming incidents.

3. BigPanda

BigPanda’s open integration hub works with alerts from your tech stack, change, and topology data. Then, its proprietary machine learning searches through these notifications to diagnose the root cause of IT incidents, present findings on the impact of such incidents, and determine the best approach for resolving them. 

Its automatic incident-finding reports use generative AI that works with available IT alert data, greatly reducing time spent on incident resolution.

BigPanda also automates workflows in IT centers by allocating tasks to different teammates and integrating with other tools to streamline collaboration. 

4. Datadog

Datadog is a comprehensive monitoring solution for cloud applications, network devices, logs, and databases. With it, you can display data from different sources and set up metrics. Plus, you can explore the available dashboard templates, collaborate with teammates on a dashboard, or customize them according to your team’s needs. 

Datadog’s distributed tracing feature uses AI, which works across different data points and is excellent for root cause analysis. It’s also valuable for generating insights into your IT environment’s security and filtering alerts that keep anomalies at bay.

5. LogicMonitor

LogicMonitor is an observability powerhouse that promises in-depth visibility into your IT infrastructure. With it, you can simultaneously monitor multiple servers, databases, and websites, or even apply it to other areas of ITOps, like automating client onboarding. 

Many of LogicMonitor’s users are managed service providers (MSPs) that need a structured way to handle ITOps for their clients. 

6. New Relic AI

New Relic AI combines the effectiveness of AIOps with the visibility of an observability platform. This improves its ability to detect anomalies, get to the root cause of incidents quickly, and launch a remediation plan to solve the problem. 

Its robust monitoring tools also deliver actionable insights into end users’ interactions with your product and track metrics like error rate, web vitals, and app performance. 

7. PagerDuty

PagerDuty automatically filters alerts, resolves minor issues, and flags the more pressing issues for human intervention. But with PagerDuty, you will also get the context behind the incidents—including historical data on past incidents to create a response plan. PagerDuty can also connect you to their experts with experience handling similar issues, reducing response time and keeping your IT team productive. 

8. Dynatrace

Dynatrace is a comprehensive AIOps platform for use cases like: 

  • Infrastructure monitoring
  • Advanced analytics
  • Security support
  • Digital experience
  • Incident management 

Its application performance monitoring feature uncovers insights into user behavior and tracks the performance of your applications over time. Actionable AI lets you quickly detect and respond to customer complaints, enhancing the user experience by monitoring digital service delivery across mobile, web, and IoT channels. 

Dynatrace also has a proprietary AI engine called “Davis,” which leverages predictive AI, causal AI, and generative AI to run root cause analysis and produce solutions for performance issues. 

Tips for implementing AIOps tools

Before rolling out any new tool, you need to lay the groundwork for a smooth implementation. To set your business up for the best outcome, here are our top tips for integrating AIOps tools into your workflows.

Assess and plan first

Beyond the excitement of improving your IT operations with AI, you should be able to define the value you’d like to get from AIOps. One way to achieve this is by setting clear, measurable goals that align with your business needs, like:

  • Improving system uptimes
  • Understanding user behavior
  • Reducing incident response times
  • Identifying bottlenecks in business processes
  • Optimizing user experience with your product

Evaluate the existing IT infrastructure

Assess your existing IT environment and take note of the available data, network systems, APIs, hardware, processes, and roles. Then, identify the areas that need the most support from AIOps. 

Consider any or all of the following areas:

  • Real-time monitoring of your networks and servers
  • Automating manual tasks like cost and usage report prep
  • Automating incident response

Choose the right AIOps tools

Small or mid-sized businesses may have vastly different needs (and budgets) than major enterprises, which will also play a part in tool selection. Beyond perusing the tool’s marketing material, check out review sites and forums. These are excellent resources for feedback about a tool’s ease of setup, user experience, customer support options, and value for unique use cases.

Consider your business’s size, operational goals, and workflows: Do you need something reasonably priced that simply covers the basics of alert response, incident resolution, and cybersecurity? Or do you need more robust solutions that can handle huge volumes of data?

There are a few must-have features to look for regardless of your business’s size or operational needs:

  • APIs and SDKs for integrating with your company’s existing tech stack
  • Scalable features that grow as the business grows
  • Interoperability with the other tools your organization uses for seamless data transfer

Develop a feedback loop

No AIOps solution is perfect, so you’ll need to train it to adapt to your IT environment, which is where feedback loops come in. 

Initially, you’ll need to provide feedback to the AIOps solution on how it adapts to your needs. It’s as easy as giving a thumbs up or thumbs down reaction on select features, using a dialogue box to give more context to a problem, or tweaking the rules for solving a problem.

With this feedback (whether positive or negative), your team effectively retrains the software to adjust its approach to a particular issue or double down on what it’s already doing. 

For example, you can use feedback loops to instruct the software to work with new metrics during user monitoring. Remember, even though these tools try to imitate human intelligence, you’ll still need to “teach” them how to think and act. 

Document everything

As you progress with the AIOps solution, record the journey. Keeping a record can reveal the tool’s strengths, weaknesses, and quirks during the implementation. The data you collect from thorough documentation can:

  • Improve tool usage in the IT centers.
  • Serve as the foundation for the success of future scaling of AIOps initiatives.
  • Provide an audit trail to fall back on before the management and compliance bodies.

Optimize cloud cost expenses with ProsperOps

AIOps tools are indispensable and continue to play a huge role as the AI industry evolves. Businesses that will remain the most competitive are those that learn to leverage AI wisely. Used judiciously, these tools shift the burden of manual data collection and analysis, freeing up IT teams to focus on what matters—resolving anomalies and keeping things running smoothly.

Since AIOps tools are cloud-based, it’s important to keep an eye on those expenses and ensure you don’t rack up unnecessary cloud costs. ProsperOps simplifies this process by automatically finding and applying the best AWS discounts to keep your cloud costs minimal even as your business scales.

Learn more about how ProsperOps can help your business optimize AWS cloud spend: Get a demo today.

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