AI Ops

AI Ops: 20 Best Platforms to Automate IT Operations in 2025

AI Ops

Explore the top AI Ops (AIOps) platforms of 2025. This in-depth guide covers 20 leading AI for IT operations solutions, pricing models, key features like AI-driven incident management and root cause analysis, deployment best practices, and future trends in autonomous ITsplunk.combigpanda.io.

AI Ops (short for AI for IT Operations or AIOps) is transforming how enterprises manage complex IT environments. By applying machine learning and big data analytics to monitoring and management data, AIOps platforms automate tasks like event correlation, anomaly detection, and root cause analysissplunk.comsplunk.com. Gartner has estimated the AIOps market at about $1.5 billion in 2020 and growing around 15% annuallyibm.com. In practice, an AIOps solution can dramatically reduce alert noise, streamline incident management, and improve operational efficiencybigpanda.iomoveworks.com. The chart below illustrates how AIOps integrates data across servers, networks, and applications to predict and resolve issues automatically.

Best AIOps Platforms in 2025

Top AIOps platforms of 2025 span established monitoring vendors and newer AI-centric tools. Many are recognized by analysts like Forrester and Gartner. Notable examples include:

  • Dynatrace – A Forrester-recognized leader in AIOpsdynatrace.com. Dynatrace’s SaaS platform uses its Davis AI engine for causal analysis, automatically detecting anomalies and pinpointing root causes across full-stack environmentscloudchipr.comdynatrace.com. It provides per-usage billing for monitoring resourcescloudchipr.com.
  • Splunk IT Service Intelligence (ITSI) – A mature AIOps and analytics platform. Splunk’s ITSI uses ML-driven anomaly detection and predictive alerting to help teams “predict incidents before they impact customers”splunk.com. It centralizes logs and metrics so operations teams can resolve issues faster with intelligent incident management.
  • Datadog – A cloud-native observability platform now extended with AIOps. Datadog was named a Leader in the Forrester AIOps Wave (Q2 2025)datadoghq.com. It uses usage-based pricing per host and per data ingestedcloudchipr.com. Datadog applies ML to trace data and metrics to reduce noise and accelerate troubleshooting.
  • New Relic – A full-stack monitoring suite with AIOps capabilities. New Relic has shifted to usage-based pricing with a free tier (e.g. up to 100 GB/month)cloudchipr.com. It uses AI to highlight significant events across applications, though some users find its interface complex.
  • Moogsoft AIOps – An AI-driven incident management tool. Moogsoft ingests alerts from multiple sources and uses AI to cluster related events into actionable incidents, helping teams focus on real issues. (Gartner Peer Insights and customers often cite Moogsoft for reducing alert overload.)
  • IBM Cloud Pak for Watson AIOps – A hybrid-cloud solution from IBM. It automatically groups related alerts (“event grouping”) to expose root causesibm.com, applies NLP-powered anomaly detection on logsibm.com, and provides AI-driven recommendations. IBM’s platform integrates with existing toolchains and can be deployed on-premises or on any cloudibm.comibm.com.
  • ScienceLogic SL1 – An AIOps-focused observability platform. ScienceLogic was named a Forrester AIOps leader for its vision of “Autonomic IT” and strong automationsciencelogic.comsciencelogic.com. It offers broad telemetry across hybrid, multi-cloud environments and advanced AI analytics (Skylar AI) for anomaly detection and root cause analysissciencelogic.comsciencelogic.com.
  • ServiceNow ITOM with AIOps – The IT operations module of ServiceNow incorporates AI to correlate events and automate incident response. It emphasizes integration with ITIL processes and uses machine learning to filter events.
  • PagerDuty OpsCast (formerly Stackstorm) – A digital operations platform focusing on alerting and incident response. PagerDuty embeds AI for automated event enrichment and has been listed in AIOps reports (Contender category).
  • BigPanda – An incident-intelligence platform that applies AI to consolidate alerts into high-quality incidents. In one case, BigPanda helped Comcast’s FreeWheel reduce alert noise by 90% and cut MTTR by 78% (from 25 hours to 5.5 hours)bigpanda.io. BigPanda uses ML to prioritize and automate incident workflows.
  • BMC Helix AIOps – The BMC Helix platform (formerly BMC TrueSight) uses AI for anomaly detection and remediation. AIOps now includes “HelixGPT” generative agents for tasks like post-incident analysisbmc.com, showcasing how generative AI is entering operations.
  • PagerDuty – Renowned for on-call and alert management, PagerDuty has extended into AIOps by adding AI-driven event intelligence and orchestration for incident response, helping teams automate triage.
  • Other notable platforms – Additional AI monitoring tools include open-source and cloud-native options: Elastic Observability (Elasticsearch with ML), Grafana (with plugins), Prometheus/Grafana+Loki, AWS CloudWatch/Azure Monitor (built-in AI features), and newer pure AIOps startups like Coralogix, Moogsoft, and LogicMonitor’s XD component. Many of these offer AI-powered anomaly detection and alert correlation in hybrid environments.

The companies above illustrate the diverse AIOps landscape. Analysts consistently cite Dynatrace, Datadog, and ScienceLogic as Leadersdynatrace.comsciencelogic.com, with strong performances from Splunk, BMC (Helix), and ServiceNowbmc.com. Together, these 20+ platforms use AI to automate monitoring and incident management across cloud and on-prem IT.

AIOps Pricing & Subscription Models

AIOps solutions offer various pricing and delivery models:

  • Consumption-based (SaaS) Pricing: Many modern AIOps tools are cloud-based and bill by usage. For example, Dynatrace charges per hour or per GiB monitoredcloudchipr.com, and New Relic bills per GB of data ingestedcloudchipr.com. These usage models can include free tiers (e.g. New Relic’s free 100 GB/month). Usage pricing is flexible but can become costly at scale if data volume grows.
  • Per-Host or Per-Device Pricing: Traditional approaches (used by Datadog and others) bill per monitored host or device, often tiered by agent type. For instance, Datadog’s classic plan bills per host (per OS instance)cloudchipr.com. This model is simple but can “skyrocket” as you add more serverscloudchipr.com.
  • On-Premises Licensing: Some vendors (e.g. Splunk, older BMC/CA tools, private editions of New Relic) offer on-premises deployments under perpetual or annual licenses. These often depend on the number of CPU cores or agents. On-prem licenses avoid recurring data fees but require managing your own infrastructure.
  • Subscription Tiers: Enterprise suites may bundle AIOps features into broader ITOM or observability products. For example, ServiceNow’s AIOps is part of its ITOM licensing. Vendors often require paid support and higher plans to access advanced AI capabilities.
  • Hybrid Deployment Options: Platforms like IBM Cloud Pak for AIOps or ScienceLogic allow either SaaS or on-prem deployment, giving organizations choice. A pure SaaS model reduces infrastructure overhead and simplifies updates, while on-premises suits strict data governance.
  • Cloud vs. On-Premises: The trend is towards SaaS delivery. Global SaaS spending is projected to exceed $715 billion by 2028sciencelogic.com, reflecting enterprises’ preference for cloud models. SaaS AIOps platforms can be up and running quickly with minimal setup. On-prem AIOps is typically chosen for compliance or integration reasons, at the cost of more upfront investment and maintenance.
  • Total Cost Considerations: Analysts note that AIOps pricing can be complex. Open-source alternatives (e.g. Prometheus/Grafana, ELK) avoid vendor fees entirelycloudchipr.com, whereas commercial tools bundle data, events, and AI in their pricing. Teams should consider data retention needs and plan accordingly.

In summary, most AIOps tools follow cloud pricing (pay-as-you-go) with options for annual commitment. Key factors are whether pricing is tied to hosts, data volume, or queries, and whether the software is consumed as a service or installed locally. Clear comparison often requires estimating monthly event/data rates and expected coverage.

AIOps Features & Capabilities

AIOps platforms bundle a range of AI-driven IT operations features:

  • Intelligent Alerting & Incident Management: A core benefit of AIOps is drastically reducing alert noise. AI engines ingest events from monitoring tools and use clustering or correlation to group related alerts. By focusing on root causes, teams see “actionable incidents” instead of thousands of raw alertsbigpanda.iobigpanda.io. For example, BigPanda’s case study shows 90% noise reduction and a 78% drop in MTTR after applying AIOpsbigpanda.io. ScienceLogic and Moogsoft similarly emphasize automated incident triage with AI.
  • Automated Root Cause Analysis (RCA): Traditional incident troubleshooting is slow. AIOps platforms automatically link symptoms to causes. For instance, ScienceLogic’s Skylar Automated RCA scans logs and metrics with ML to “pinpoint root causes” in vast datasciencelogic.com, providing concise insights and next-step recommendations. Splunk also highlights event correlation and RCA as key taskssplunk.com. Automated RCA can make diagnosis up to ten times fastersciencelogic.com by surfacing the most relevant signals.
  • Anomaly Detection & Predictive Maintenance: AI Ops uses machine learning to detect unusual patterns in metrics or logs before they become critical. Generative and predictive AI models analyze historical performance to forecast issues. As one analysis notes, AIOps “predicts potential issues and automates responses to optimize performance,” thereby reducing downtimeaisera.com. For example, generative AI in AIOps can identify trends indicating imminent hardware failures or service degradation, enabling proactive fixes.
  • Automation & Remediation: Beyond detection, AIOps can auto-remediate known issues. When the system identifies a well-understood problem, it can trigger scripts or workflows (auto-scaling, patching, or restarting services). ScienceLogic advises starting with small automated actions and scaling up: “By automating workflows with AI and ML, IT teams can eliminate repetitive manual operations, reducing errors and increasing efficiency”sciencelogic.com. Many platforms integrate with ticketing systems (e.g. auto-creating Jira or ServiceNow incidents) and chatops tools to push fixes in real time.
  • Data Integration & Observability: Successful AIOps requires consolidating telemetry. Platforms ingest metrics, logs, traces, events, and even tickets from diverse sources. IBM recommends integrating “both structured and unstructured sources across your entire stack, regardless of vendor”ibm.com to feed the AI engine. Comprehensive observability is “non-negotiable” – without full visibility, ML models lack contextsciencelogic.com. Thus modern AIOps suites often include built-in data collectors and connectors to cloud services, apps, and legacy systems.
  • Business Context & Prioritization: Advanced AIOps adds context like service dependencies or business impact. It can highlight which alerts risk critical SLAs. AI incident management can prioritize by blast radius (affected users or revenue), so IT teams focus on what matters most. For instance, ScienceLogic’s analytics can “predict outages or service degradations before they occur” in key business servicessciencelogic.com.
  • AI-Driven Monitoring Tools: In effect, AIOps platforms serve as next-gen AI monitoring tools. They continuously learn from the environment, adjusting alert baselines and refining models over time. Splunk summarizes that AIOps “is the use of big data and analytics (and increasingly AI itself) to enhance IT operations,” continuously ingesting and analyzing data for instant insightssplunk.com. In practice, Dynatrace’s AI engine (Davis) and New Relic’s Applied Intelligence both exemplify this trend of embedding ML into monitoring.

In short, AIOps features go well beyond traditional monitoring. They combine AI-powered incident management, automated RCA, anomaly detection for predictive maintenance, and workflow automation into unified platforms. This cross-cutting AI capability turns monitoring tools into proactive “AI incident management” and “AI monitoring tools,” aligning operations with business priorities and keeping systems healthy with minimal human interventionbigpanda.iosciencelogic.com.

How to Deploy AIOps (Step-by-Step Guide)

Implementing AIOps in your environment involves people, process, and technology steps. A phased approach is best:

  1. Assess Needs and Define Goals: Identify pain points (e.g. alert overload, slow RCA, too much manual toil). This sets objectives for AIOps (faster MTTR, fewer missed incidents, etc.). Also determine data sources (logs, metrics, tickets) and stakeholders (IT Ops, DevOps, SRE).
  2. Ensure Strong Observability Foundation: As experts note, “complete observability across the entire IT infrastructure is non-negotiable”sciencelogic.com. Begin by consolidating monitoring tools into a central view. Deploy or configure agents to collect metrics, logs, traces, and events from all critical systems (cloud, on-prem servers, containers, network devices, etc.). The goal is to feed the AIOps engine with rich, clean data.
  3. Integrate Data Sources and Tools: Hook the AIOps platform into existing tools. For example, connect network monitors, application APM tools, public cloud APIs, and ITSM systems. IBM’s AIOps portal advises: “Connect application data from both structured and unstructured sources across your entire stack, regardless of vendor”ibm.com. Integration may involve REST APIs, message queues, or proprietary connectors. Proper integration ensures the AI has context (e.g. which service a log event relates to, or which on-call schedule to use).
  4. Configure Initial AI/ML Models: Use historical incident and alert data to train the platform’s ML models. Define correlation rules or let the AI learn patterns (e.g. event spikes preceding outages). Many vendors provide out-of-the-box classifiers or use unsupervised learning. Start with a pilot: select a subset of systems or alert types and turn on the AI analysis. Adjust sensitivity to avoid false positives. At this stage, you are essentially “bootstrapping” the AIOps brain.
  5. Validate and Tune: Monitor how well the AI groups alerts and identifies anomalies. Review a set of AI-generated incident groups: are they accurate? Early on, keep human oversight as recommended: continuously “monitor and refine automated processes” and “keep a human in the loop”sciencelogic.com. Tune thresholds, noise filters, and event enrichment rules. Good AIOps adoption requires iteration – use feedback from IT staff to improve the models.
  6. Automate Remediation Workflows: Once the platform reliably identifies incidents, automate response actions for common problems. This might mean scripting auto-resolves (restart a service, scale a cluster) or auto-generating tickets. Many tools have “one-click” remediation templates. As ScienceLogic notes, “by automating workflows… IT teams can eliminate repetitive manual operations”sciencelogic.com. Integrate with ticketing (e.g. automatically create a Jira or ServiceNow ticket when an incident is flagged) and chatops (notify teams on Slack/Teams).
  7. Train Your Teams: AIOps changes processes, so train operators and Dev teams. Update runbooks to incorporate AI alerts, and show staff how to interpret AI-driven insights. Encourage collaboration between Dev and Ops (DevOps) so that AI findings lead to fixes. Providing clear dashboards and notifications helps adoption.
  8. Scale Gradually: Expand AIOps coverage step by step. After success on a pilot domain, add more applications, services, or data feeds. Ensure the platform can handle the load (Scalability is key: “start small, but ensure the software is scalable”sciencelogic.com). Periodically revisit models and include new types of events as the IT environment changes.
  9. Measure and Iterate: Track KPIs like mean time to detect (MTTD), mean time to repair (MTTR), and incident counts. AIOps deployment is not a one-off – as the infrastructure evolves, update the AI. Leverage platform analytics (many provide metrics on noise reduction or RCA speed).
  10. Governance and Best Practices: Implement data governance (who can view AI insights), and ensure AI decisions comply with policies. Keep human oversight especially on critical actions. Document the deployment so future team members understand the setup.

By following these steps – strong data foundation, integration, careful tuning, and ongoing training – organizations can successfully roll out AIOps. The ScienceLogic team emphasizes that strong monitoring and analytics is “the foundation for everything else” in AIOpssciencelogic.com. With patience and executive support, AIOps can become a reliable tool that your teams trust.

The Future of AIOps for Enterprises

The AIOps landscape is rapidly evolving. Key future trends include:

  • Autonomous, Self-Managing IT (Autonomic IT): The long-term vision is an IT environment that “operates with minimal human involvement”sciencelogic.com. ScienceLogic and other leaders call this “Autonomic IT” or autonomous operations. Platforms will increasingly use AI not only to detect issues, but to self-heal systems. Gartner and Forrester talk about “agentic AI” that not only alerts but acts. For example, IT infrastructures may someday adjust capacity, reroute traffic, or apply patches automatically in response to predicted issues. This full autonomy is a few years out, but the trend is clear: more baked-in automation and intelligence.
  • Generative AI and AIOps Copilots: Generative models (like GPT) are entering ITOps. Vendors are already introducing AI agents. BMC’s Helix 25.2 release (Nov 2024) added “HelixGPT” agents – for instance a Post Mortem Analyzer that summarizes an incident in natural language, and an Insight Finder for chat-driven queriesbmc.com. Similarly, Google Cloud and AWS are exploring generative AI for operations. We can expect chatbots and copilots that let engineers query infrastructure (“why is latency up?”) and get AI-written answers. This will make AIOps more accessible, using conversational interfaces for incident analysis.
  • Hybrid and Multi-Cloud Integration: Enterprises continue to spread workloads across on-prem, public clouds, and edge. AIOps tools will deepen support for these hybrid environments. As noted by Splunk and ScienceLogic, dealing with “multi- and hybrid cloud” data is a driving challengesplunk.comsciencelogic.com. Future AIOps solutions will provide unified views across these domains, ingesting cloud provider telemetry and correlating it with on-prem logs. Integration with cloud-native services (Kubernetes, serverless, container platforms) will improve.
  • AI-driven Incident Management: The concept of AI incident management will mature. Rather than just clustering alerts, systems will proactively advise operations teams. For instance, ScienceLogic’s upcoming Skylar Advisor will use “agentic AI” to give persona-based recommendationssciencelogic.com. AIOps may suggest how to prevent incidents by analyzing trends. In short, IT incident workflows will embed AI copilots at every step.
  • Expanded Observability Data: Future AIOps may incorporate more data types (like observability for IoT devices, security logs, business KPIs). Some platforms are blurring lines between observability and AIOps. Expect convergence: monitoring tools will increasingly add AI features (Splunk, Dynatrace, Datadog continue to build AIOps into their stacks).
  • Convergence with MLOps: As companies embed more ML models in production, the disciplines of MLOps and AIOps may overlap. For example, models for predictive analytics might be trained in MLOps pipelines and then used by AIOps. Conversely, AIOps insights could feed back into DevOps to retrain models. This integration will help align data science with IT operations.
  • Greater Use of Predictive Analytics: AIOps will lean more on predictive maintenance. Already, some firms report that critical outages cost up to $15,000 per minutebigpanda.io. AIOps tools will use historical and real-time data to forecast and alert on issues (e.g., disk failure, security breaches) before they happen, moving from reactive to proactive ops.
  • Emphasis on “AI Explainability” and Governance: As AI handles more ops decisions, transparency will matter. Expect features that explain why an alert was flagged or why a specific remediation is suggested. Trust and compliance requirements may drive better audit trails for AIOps decisions.
  • Industry-Specific Solutions: Certain sectors (finance, healthcare, telco) may see tailored AIOps solutions addressing regulatory, latency, or scale demands. For example, 5G networks and edge computing will need specialized AIOps that handle network data and low-latency event correlation.

In summary, the future of AI Ops is one of increasing autonomy and intelligence. Enterprises can look forward to more “AI incident management” where AI not only alerts but also advises and even fixes issuesbigpanda.iosciencelogic.com. Generative AI will make these systems more conversational, while hybrid-cloud deployments will become more seamless. As ScienceLogic emphasizes, as IT demands grow, “the need for automation, insight, and scale will only increase” – and AIOps will be at the core of delivering that next level of IT resiliencesciencelogic.com.

Together, these trends suggest that by 2025 and beyond, AIOps will evolve from a niche IT tool into a strategic capability, fundamentally changing how operations teams work.

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