Best AIOps platforms for proactive IT management in 2025

Modern IT environments generate thousands of alerts daily, many of which are false positives. With leaner teams and complex systems, managing this noise efficiently is critical. AIOps helps streamline operations by filtering alerts, prioritizing incidents, and improving response times through automation.

AIOps tools use intelligent automation to filter noise, detect issues early, and even resolve them before users notice. In 2025, they’re faster, sharper, and more business-aware than ever. This guide covers the 15 best AIOps tools right now, rated for how well they cut through complexity and deliver real, usable insights when it matters most.

What are AIOps tools?

AIOps (Artificial Intelligence for IT Operations) tools use machine learning, big data, and automation to streamline and improve IT operations. Instead of relying on manual monitoring, they analyze logs, metrics, and events in real time to detect issues, prevent outages, and resolve problems faster.

Unlike traditional monitoring tools, AIOps platforms don’t just alert you, they predict incidents, offer root cause insights, and even trigger automated fixes. It’s not just smarter IT, it’s proactive, self-healing, and built for scale.

What AIOps tools actually do (when they work well)

When used right, the best AIOps platforms can lead to fewer late-night emergencies, quicker fixes, and more time for your team to focus on real work. The best tools don’t just tell you what broke; they connect the dots, show you what needs attention, and often fix things automatically.

From surfacing the root cause of a cascading failure to preventing incidents with early signals, AIOps tools step in quietly, handle the grunt work, and help your team stay ahead of the curve. In this list, we’ve focused on tools that actually deliver on that promise.

Top AIOps tools comparison at a glance

Take a look at the top AIOps tools here:

15 best AIOps tools in 2025

Here's a detailed rundown on the top AIOps tools so you can pick the one that suits you best.

1. Freshservice

Freshservice is built for the modern IT team that wants speed without sacrificing control. It’s not just an ITOM tool with some AI sprinkled on top. It’s a fully AI-native platform that helps teams move from reactive firefighting to proactive, intelligent operations. From automated incident resolution to GenAI-powered ticket management, Freshservice brings together smart workflows and a user-friendly interface that makes IT ops hassle-free.

Freshservice GenAI intelligently automates IT support workflows, enhancing agent efficiency and user experience. With GenAI applications, you can:

• Summarize and categorize incoming incidents

• Get AI-driven recommendations for root cause analysis

• Generate and improve knowledge articles in no time

• Navigate easily using conversational interfaces

Pros

• Provides an intuitive user interface with a minimal learning curve

• Offers seamless integration with existing business tools and third-party platforms

• Enables seamless configuration of the system and gets it up and running in no time

• Boasts strong automation capabilities to minimize manual intervention and errors

• Provides prompt support and active community resources

Cons

• Initial setup may take some time, particularly for teams new to ITSM or AIOps but the flexibility it offers can be well worth the effort.

• Advanced reporting requires some customization to unlock its full value, giving teams the ability to shape analytics around their specific goals.

Success metrics

Freshservice delivers measurable impact:

• Increased first contact resolution time by 92% while handling a 60% spike in tickets per agent for OdontoCompany (healthcare and pharma)

• Achieved a 23% self-service deflection rate with a 96% customer satisfaction score" for Databricks (higher education)

• Cut resolution time by 50% with 100% core workflow automation for TaylorMade (retail)

Datadog

Datadog is a cloud-native observability platform built to give you deep visibility into everything, from servers and databases to applications and services.

With powerful AIOps capabilities baked in, it helps teams stay ahead of outages, prioritize what matters, and cut through the noise with smart alerting and predictive insights.

Pros

• Offers a consolidated view of efficient incident management

• Deduplicates and prioritizes alerts automatically

• Has predictive analytics that help you prevent problems before they start

• Boasts centralized log management and correlation for faster root cause analysis

• Integrates smoothly with tools like AWS, Azure, and Kubernetes

Cons

• Alert fatigue needs fine-tuning to avoid overload

• Log management is decent, but not the most advanced in the market

Splunk AppDynamics

Backed by Cisco, Splunk AppDynamics blends full-stack observability with AIOps smarts, so you can connect performance issues directly to business impact. From app response times to customer journeys to AI model performance, AppDynamics brings all your signals under one roof. And with its business lens, it’s easier to spot what needs attention.

Pros

• Maps app performance directly to business KPIs for clearer impact analysis

• Monitors AI and LLM-based applications (yes, even your GenAI stack)

• Tracks user journeys to spot friction points across the app experience

• Provides full-stack observability, not just apps, but infrastructure too

• Offers dynamic baselining that adjusts to shifting traffic and usage patterns automatically

Cons

• Works best within the Cisco ecosystem — can feel a bit limiting outside it

• Initial setup and navigation may require a learning curve for some teams

BigPanda

BigPanda is a rapid incident detection and automation platform. Its smarter alert management feature auto-correlates alerts and adds rich context to help teams investigate faster. Powered by its AI engine BiggyAI, BigPanda doesn’t just show you what’s happening. It gives you the context to fix it fast. It acts like the bridge between your monitoring tools and your ops team, built for speed and scale.

Pros

• Offers customizable summaries that give you more control over incident visibility

• Provides high situational awareness that helps teams prioritize what matters

• Enhances business impact visualization to show how incidents affect customers and revenue

Cons

• Support response time can be hit or miss

• Filtering options aren’t as flexible as some teams may need

• A heavily code-focused setup might not suit all user levels

PagerDuty

If real-time incident response is a top priority for your team, PagerDuty is built to deliver. It’s an AIOps-powered platform designed to help IT and DevOps teams catch, assess, and resolve critical issues before they become major disruptions. Whether it's alert escalation, on-call management, or streamlined collaboration, PagerDuty brings clarity and speed to chaos, so you can stay focused on uptime, not firefighting.

Pros

• Offers auto-escalation that keeps incidents moving

• Enables seamless integration with a wide range of DevOps and monitoring tools

• Boasts a clean, intuitive interface that doesn’t require a steep learning curve

Cons

• Multilingual support is limited

• Reporting and analytics aren’t as deep as some competitors

• Setting up event workflows can be tricky at first

Dynatrace

If you're managing a sprawling, modern cloud environment and want complete visibility with AI-driven precision, Dynatrace is a serious contender. It’s an all-in-one observability platform built for enterprises that need more than just dashboards; it's ideal for those who need answers, fast. At the heart of it is Davis AI, Dynatrace’s powerhouse engine that connects the dots, finds root causes, and even fixes problems before your team gets involved.

Pros

• Pinpoints root cause analysis that cuts through layers of complexity

• Auto-discovers applications and maps dependencies, no manual setup required

• Enables proactive issue detection to prevent downtime before it hits users

Cons

• The pricing model is complex and can be hard to navigate

• There’s a learning curve, especially for teams new to AIOps or large feature sets

Moogsoft

When your ops team is overwhelmed by alerts and constant firefighting, Moogsoft brings calm to the storm. This AIOps platform uses machine learning to filter noise, pinpoint root causes, and help teams resolve incidents faster, together. It’s especially handy for complex environments where traditional monitoring tools fall short and where cross-team collaboration is key to staying ahead.

Pros

• Boasts adaptive thresholds and alert deduplication to reduce notification overload

• Offers machine learning that evolves with your environment to catch emerging issues

• Enables advanced correlation and classification to simplify incident triage and management

• Extends strong situational awareness features that provide context around incidents

Cons

• Documentation is limited, which can slow down onboarding

• Heavy reliance on open-source resources for support

LogicMonitor

LogicMonitor is built for teams that need to monitor everything, on-prem, cloud, containers, and beyond. It’s a full-stack infrastructure monitoring platform that brings real-time visibility and AI-driven automation to even the most complex IT environments.

With its AI assistant Edwin AI and a conversational GenAI interface, LogicMonitor helps teams detect problems faster, streamline workflows, and manage it all from one powerful dashboard.

Pros

• Spots anomalies and triggers rapid response actions with Edwin AI

• Enables unified monitoring across networks, servers, apps, and multi-cloud services

• Offers a conversational GenAI interface that makes navigation feel natural and efficient

• Automates entire workflows, from alerting to resolution

Cons

• Doesn’t offer deep application performance monitoring (APM)

• Some reporting tasks lack automation out of the box

Splunk IT Service Intelligence (ITSI)

For teams overseeing complex digital environments, Splunk ITSI brings clarity and control. It offers a real-time, unified view of systems and services, helping teams understand not just where issues occur, but how they affect the business.

Pros

• Offers intuitive, service-based dashboards that make it easier to spot issues and track what matters

• Provides support for hybrid and multi-cloud environments, plus synthetic monitoring that enables teams to stay ahead of performance dips

• Health scores and KPIs offer a quick snapshot of system performance and service status

Cons

• Setting up integrations and tuning data sources can take time, especially in larger environments

• Without fine-tuning, alerts can be noisy and distract from real issues

IBM Watson AIOps

For enterprise teams juggling complex systems, IBM Cloud Pak for AIOps offers a way to bring everything into focus. Built for scale, it connects tools, data, and teams, turning scattered signals into a clear picture of your IT health.

Pros

• Gives full visibility across apps, infrastructure, and networks in one place

• Builds real-time maps that show how systems connect and where issues could spread

• Makes adoption easier with a low-code platform designed for speed and flexibility

Cons

• Setup can be technically involved, especially in highly customized environments

• Pricing may be a hurdle for mid-sized teams or those just starting their automation journey

ServiceNow ITOM

For teams managing hybrid or cloud-first infrastructures, ServiceNow ITOM acts as a central command center, bringing together visibility, automation, and intelligence to keep operations running smoothly. With Now Assist, its built-in generative AI engine, teams can analyze alerts, correlate events, and automate resolutions, reducing manual effort and accelerating incident response.

Pros

• Creates a single source of truth for all IT operations

• Offers built-in firewall auditing and compliance reporting

• Speeds up cloud adoption and streamlines performance tracking

Cons

• Requires technical expertise to fully implement and maintain

• Discovery tools may miss components in more diverse or legacy environments; also, some third-party integrations could be more seamless

New Relic

New Relic is like a mission dashboard for engineering teams, offering deep, real-time visibility into your apps, infrastructure, and user experience. With GenAI capabilities and a powerful query engine, it helps you move from “what went wrong?” to “let’s fix this fast.”

From startups to large SaaS platforms, it’s built for teams that want data-backed decisions and smarter incident handling at speed and scale.

Pros

• Defines custom Red-Amber-Green (RAG) statuses with historical views for clear performance tracking

• Offers GenAI-driven recommendations that help speed up root cause analysis and scale smoothly across complex, high-volume environments

• Supports forecasting in NRQL queries to spot trends before they become incidents

Cons

• Offers limited network-level observability

• Lacks out-of-the-box support for Azure Web Apps

ScienceLogic SL1

For IT teams managing large, distributed environments, ScienceLogic SL1 brings together visibility, automation, and proactive insights across hybrid and on-premises setups. The 2025 update (v12.3.3), DoDIN-certified for enhanced security, adds native Python 3.11 support and unlocks Skylar AI-powered analytics and anomaly detection.

Pros

• Adapts easily to complex environments with deep customization options

• Excels in network data collection, analysis, and monitoring

• Automates detection and resolution to speed up incident handling

• Tracks configuration changes to reduce risk and prevent surprises

Cons

• Offers limited documentation, which can slow down onboarding

• Requires time and expertise to fully unlock its more advanced features

Zluri

For teams juggling dozens of cloud apps, Zluri shines a light on every corner of your SaaS ecosystem, discovering even unsanctioned and AI-powered tools, while automating governance and cost controls.

Pros

• Uncovers every SaaS app in use, including shadow IT and rogue AI tools

• Automates onboarding, approvals, and access changes across the employee lifecycle

• Tracks usage and spend to help you optimize your SaaS budget

• Integrates smoothly with major SSO providers and leading SaaS platforms

Cons

• Offers fewer native integrations compared to larger SaaS management or AIOps providers

Fabrix.ai

For IT teams navigating complex, hybrid environments, Fabrix.ai stands out with its Robotic Data Automation Fabric (RDAF) — a unified “data mesh” that automatically pulls together logs, metrics, and events into a single pane of glass, so you get context-rich alerts and actionable insights without stitching multiple tools together.

Pros

• Offers a low-code interface for faster setup and customization

• Provides ready-to-use dashboards and reporting to get insights quickly

• Maps services and dependencies in detail so you always know how systems connect

Cons

• The interface and overall usability could be smoother in some areas

Pricing

This breakdown highlights the varied pricing models for different tools, including per-host or per-agent charges, as well as custom or contact-based pricing for some tools.

How to choose the right AIOps tool for your organization

You’ve narrowed your choices. Now you're evaluating AIOps platforms to find the best fit for your IT operations. But which features matter most, and which are just surface-level selling points?

Use this checklist to evaluate with clarity:

• Fast time-to-value

Select a platform that offers quick implementation and minimal setup. Solutions like Freshservice stand out with low-code onboarding and native connectivity with popular monitoring and observability tools. This helps you resolve real issues without spending weeks on setup configurations.

• Explainable AI

Does the platform provide insight into why the AI flagged an alert? Top AIOps platforms offer complete transparency on how they analyze data, detect patterns, and reach specific conclusions.

• Smart noise reduction

While each AIOps software promises alert noise reduction, the effectiveness of this feature varies. It’s recommended to go with platforms that offer adaptive suppression and intelligent alert grouping, especially the ones that are trained on datasets from companies similar to yours in size and complexity.

• User-friendly interface

Your AIOps platform should be easy to use for everyone in your company. So, look for a modern, intuitive interface with clearly defined workflows for triaging and resolving incidents.

• Built-in collaboration capabilities

Time is a useful resource when it comes to handling incidents. Your team must be able to coordinate responses without switching between systems. Platforms like Freshservice make collaboration faster by integrating natively with communication tools such as Slack, Teams, etc. 

• Total cost of ownership

Think about what it’ll cost to deploy, integrate, and maintain over time beyond just the license fee. A platform that’s “cheap” upfront might cost more in the long run. However, one that delivers value early and continues to scale with your needs is worth the investment.   

Long story short, choose an AIOps tool that works with your stack, is easy for your team and takes work off your platform. A solution like Freshservice, powered by Freddy AI, helps your IT operations team resolve incidents faster and focus on what really matters.

Matching AIOps tools to your IT maturity

Not every AIOps platform fits every team, and that’s totally okay. The right tool depends on where you are in your IT journey and what you’re aiming for. Here’s how to find your match:

• Organizational size and complexity: Choose tools that align with the scale of your operations. If you have a small team, opt for lightweight tools that are quick to deploy and easy to manage. If you are running a large enterprise, go for feature-rich platforms that can handle complex workflows and scale with your infrastructure.

• Infrastructure diversity: Look for tools that support your environment — on-prem, cloud, hybrid, or multi-cloud — with native connectors and integration breadth.

• Operational requirements: Prioritize specific capabilities based on your IT use cases, like capacity forecasting or predictive incident management.

• Team skill sets and staffing: Consider the learning curve and automation flexibility to match your team’s expertise. If you have a lean IT team with limited AI experience, work with platforms that need low-code automation and offer intuitive dashboards. If you have data scientists on board, explore those that offer advanced customization.

• Strategic alignment: Select platforms that support goals unique to your business, whether that’s reducing MTTR or improving service reliability.

Key features to look for in an AIOps tool

Choosing the right AIOps platform goes beyond flashy dashboards. To truly support your IT operations, here are the core features that matter most:

• Real-time analytics: The ability to process and analyze data as it’s generated is critical. A good AIOps tool offers instant insights into system health, performance issues, and anomalies so your team can act before users even notice.

• Noise reduction and root cause analysis: Effective tools cut through alert fatigue by correlating signals and identifying what really needs attention. Built-in RCA (Root Cause Analysis) helps pinpoint the origin of issues faster, reducing mean time to resolution (MTTR).

• Automated remediation: Beyond just identifying problems, top AIOps platforms can trigger automated workflows to resolve common incidents without human intervention, saving time and reducing errors.

• Integration capabilities: Your AIOps tool should seamlessly integrate with your existing stack; monitoring systems, ITSM platforms, cloud services, and more. Native connectors and API support make it easier to unify operations.

• Scalability and ease of deployment: Whether you're a mid-size team or a global enterprise, the platform should scale with you. Look for tools that are easy to deploy, configure, and maintain, even as your infrastructure grows.

Implementing AIOps tools: Best practices for success

Rolling out an AIOps platform requires setting your team and infrastructure up for long-term success. Here’s how to do it right:

• Prepare your IT environment: Start by auditing your existing systems, tools, and data flows. AIOps relies on clean, connected data, so it’s important to ensure your monitoring, logging, and infrastructure layers are ready.

• Align stakeholders and define goals: Get buy-in from leadership, IT, and operations teams early. Clearly define what success looks like, whether it’s reducing incident response time, automating ticket triage, or improving service reliability.

• Focus on data integration and normalization: AIOps platforms thrive on consistent, high-quality data. Integrate logs, metrics, events, and other sources into a unified format to fuel better insights and automation.

• Start with a pilot: Test the tool in a limited, controlled environment. Use the pilot to fine-tune thresholds, validate workflows, and measure early impact before scaling across your organization.

• Commit to continuous monitoring and optimization: AIOps isn't set-and-forget. Keep refining the models, adjusting automations, and analyzing performance data to ensure the tool evolves with your infrastructure.

• Train your teams: Equip your IT staff with the knowledge to interpret AI-driven insights and manage automation workflows. A well-trained team is key to getting full value from your AIOps investment.

Challenges and limitations of AIOps tools

While AIOps tools bring speed and intelligence to IT operations, the journey to full value isn’t without roadblocks. Here are some common challenges to be aware of:

• Data silos and integration issues: AIOps platforms rely on unified data streams, but many organizations still operate in silos. Integrating data from different systems and tools can be time-consuming and may require custom connectors.

• Complexity of deployment: Implementing AIOps isn’t always plug-and-play. From setting up data pipelines to configuring automation workflows, deployment can get complex, especially in large, hybrid environments.

• Dependency on data quality: The accuracy of insights depends heavily on the quality of data fed into the system. Incomplete, noisy, or poorly labeled data can lead to false positives, or missed incidents altogether.

• Managing expectations vs. reality: Teams need to understand that AI will enhance operations over time, not replace humans overnight. Setting realistic goals is key to long-term adoption.

Why Freshservice leads in AIOps innovation

The stakes are high when it comes to IT operations, especially when every delay or overlooked issue can cause significant downtime, hurt team productivity, and impact the bottom line. But that doesn’t have to be the reality. With the right AIOps platform, your team can stop reacting to problems and start staying ahead of them.

If you ask us, we’d recommend Freshservice AIOps, a powerful solution that’s already transforming how teams work, including at Texas A&M's transportation team, where they cleared a three-month ticket backlog in just 15 minutes after implementing Freshservice. The platform offers intelligent alert routing to make sure your team only gets the most important notifications, low-code automation to reduce manual work, and Freddy AI to provide real-time recommendations that empower your team to solve problems faster.

Freshservice AIOps also helps teams stay proactive by monitoring service health and using anomaly detection to spot issues before they become full-blown problems. And with clear, transparent pricing, you’ll have access to powerful tools without worrying about hidden fees or unnecessary add-ons.

Ready to transform how your team handles IT? Try Freshservice AIOps free for 14 days and experience what thousands of teams are already discovering — fewer surprises, faster resolutions, and a smarter, more efficient way to work.