One storage delay can trigger application alerts, infrastructure alarms, service tickets, and several dashboards. Your team receives plenty of signals, yet few clear answers.
AIOps tools help connect those signals. They group related events, detect unusual behavior, identify likely causes, and support the next response.
However, these products approach the work differently. Some analyze application and infrastructure data. Others begin with alerts, service records, or on-call workflows.
The starting data determines what each platform can explain. It also affects how much investigation remains manual.
This guide compares eight AIOps tools across their strongest use cases, notable capabilities, and practical considerations. You will also learn how to test each option against a real incident.
In This Article
- AIOps Tools at a Glance
- 8 Best AIOps Tools in 2026
- What to Look for in an AIOps Platform
- Turn Your AIOps Shortlist Into a Real Test
- Frequently Asked Questions
AIOps Tools at a Glance
We selected these AIOps tools for their current availability, enterprise relevance, verifiable capabilities, and distinct approaches to IT operations.
The list also reflects vendors that appear regularly in current AIOps research and comparison results. Independent review coverage helped inform the practical considerations.
Each product starts from different data and workflows. Some collect operational data directly. Others correlate events, enrich service records, or coordinate response.
The order supports a useful comparison and does not represent a universal score. Your strongest option depends on your environment, existing tools, and hardest incidents.
We also favored products with clear boundaries. Buyers should know whether each option collects operational data or works above existing tools.
Before we take a closer look at each tool, here is a quick at-a-glance look.
| Platform | Best Fit | Primary AIOps Strength | Key Area to Evaluate |
|---|---|---|---|
| Virtana | Complex hybrid and AI environments | Cross-layer dependency context and governed action | Priority integrations and incident workflows |
| Dynatrace | Application-focused enterprises | Topology-informed analysis | Infrastructure depth and licensing |
| Datadog | Cloud-first engineering teams | Broad SaaS observability and integrations | Data volumes, modules, and cost controls |
| Splunk ITSI | Established Splunk environments | Service-aware event intelligence | Data onboarding and service modeling |
| ServiceNow ITOM | ServiceNow-centered operations | Service records and workflow automation | Data quality and implementation effort |
| BigPanda | Operations centers using many monitoring tools | Cross-tool event correlation | Integration quality and event coverage |
| IBM Concert | Large hybrid organizations | Cross-domain operations and optimization | Packaging, migration, and entitlements |
| PagerDuty AIOps | On-call and incident-response teams | Alert grouping and response orchestration | Event volumes, add-ons, and safeguards |
8 Best AIOps Tools in 2026
1. Virtana
Best for: Complex environments where incidents cross applications, infrastructure, networks, storage, cloud, Kubernetes, or AI systems.
The Virtana Agentic Observability Platform continuously models your environment as one connected system. It builds dependency context before incidents begin.
When performance changes, teams can follow the affected service to a constrained resource. They can investigate with shared evidence instead of reconciling separate dashboards.
This shared model gives each team the same starting point while preserving domain-specific detail.
Notable features:
- The System Dependency Graph maps relationships across applications, services, cloud, Kubernetes, compute, networks, storage, and AI infrastructure.
- AI Event Intelligence groups related signals and separates downstream symptoms from the likely operational constraint.
- Diagnostic AI Agents investigate probable causes using evidence from the connected environment.
- Remediation AI Agents recommend or coordinate governed actions under established policies and approvals.
- Application, Service, Infrastructure, and AI Factory Observability share a common data and dependency foundation.
- SaaS, on-premises, customer-controlled, and air-gapped options support different security and operational requirements.
Suppose storage latency slows a Kubernetes service and affects a customer application. Virtana connects storage behavior with service performance and business impact.
This context supports infrastructure observability and service observability through the same platform.
Considerations: Virtana fits environments where incidents regularly cross technology and team boundaries. Test your priority integrations and most expensive incident workflows during evaluation.
G2 reviewers praise Virtana’s cross-layer visibility across applications, Kubernetes, and infrastructure. They also highlight straightforward deployment, faster troubleshooting, and practical recommendations for reducing cloud waste.
Virtana was recognized as a Notable Vendor in Forrester’s The AIOps Platforms Landscape, Q2 2026.
2. Dynatrace
Known for: Application-focused observability, automated discovery, and topology-informed analysis.
Dynatrace collects supported data through OneAgent, OpenTelemetry, cloud integrations, and monitoring extensions. Smartscape maps relationships across services, infrastructure, Kubernetes, and cloud resources.
The platform links observability data with a current dependency map. Teams can see where a transaction depends on shared services.
Notable features:
- Dynatrace Intelligence applies causal, predictive, generative, and agentic techniques across platform data.
- Distributed tracing follows transactions through connected services and infrastructure.
- Application, infrastructure, cloud, log, security, and user-experience capabilities share a common platform.
- Automation capabilities can route findings into defined workflows under established controls.
Considerations: Evaluate agent deployment, required modules, licensing, and coverage across networks and storage. Test an incident that begins below the application layer.
TrustRadius reviewers highlight service-flow tracing, root-cause correlation, and contextualized telemetry. Several describe DQL, navigation, and onboarding as learning-intensive, especially during platform transitions.
3. Datadog
Known for: Cloud-first SaaS observability with a large integration catalog.
Datadog brings metrics, traces, logs, events, networks, security signals, and user experiences into one SaaS environment.
Teams can move from an unusual metric to related traces, logs, deployments, and service ownership.
Notable features:
- Watchdog detects unusual behavior and surfaces relationships across supported data.
- Bits AI supports investigations using data and context available within Datadog.
- Event Management and Workflow Automation connect signal analysis with response processes.
- Broad cloud and Kubernetes integrations support engineering teams using a wide range of services.
- Network monitoring adds traffic and dependency context for cloud and hybrid connections.
Considerations: Model hosts, data volume, indexed events, retention, modules, and AI usage together. Confirm that a SaaS service meets your data-control requirements.
TrustRadius reviewers highlight cross-signal correlation, dashboards, log analysis, and broad integrations. Frequent concerns include opaque usage costs, complex queries, dashboard customization, and alert fatigue without careful tuning.
4. Splunk IT Service Intelligence
Known for: Service intelligence and AIOps built around operational data already available within Splunk.
Splunk IT Service Intelligence connects events with service and key performance indicator health. It helps operations teams prioritize issues by business impact.
Service Analyzer gives teams a shared view of service health, KPI changes, and notable events.
Notable features:
- Event iQ Detect uses machine learning to group related events into episodes.
- Event iQ Diagnose adds summaries with suspected causes, key events, and effects on related services.
- Change context from systems such as ServiceNow and Jira supports incident investigation.
- Episode Review brings grouped events, supporting details, and suggested causes into one workspace.
Considerations: ITSI is especially relevant for established Splunk customers. New buyers should assess data onboarding, service modeling, platform requirements, and licensing.
TrustRadius reviewers highlight service health scores, KPI alerting, event consolidation, and business-impact views. They describe service modeling, data normalization, threshold validation, and complex-environment rollouts as time-consuming. Much of the detailed feedback predates Event iQ, so validate its current workflows directly.
5. ServiceNow ITOM
Known for: Connecting AIOps with service management, a configuration management database (CMDB), service mapping, and enterprise workflows.
ServiceNow ITOM combines operational events with configuration and service records. This approach ties detected issues to established incident, problem, and change processes.
The workflow focus suits organizations where operations already revolve around ServiceNow records, approvals, and ownership.
Notable features:
- Discovery and Service Mapping maintain relationships among infrastructure, applications, and business services.
- Event Management correlates and deduplicates alerts while tracking service health.
- Policy-driven workflows can trigger approved remediation and create follow-up records.
- Service health views connect operational changes with business services recorded in the CMDB.
Considerations: Results depend on accurate configuration and service data. Assess implementation effort, CMDB maintenance, licensing, and data from connected monitoring tools. Confirm whether AI Agent Topology Mapping, Now Assist for ITOM, and AI Specialists are included in the proposed tier.
TrustRadius reviewers value discovery, service mapping, CMDB-connected incident handling, and workflow automation. They warn that poor source data weakens results and cite steep learning curves, reporting limitations, upkeep, and cost.
6. BigPanda
Known for: Correlating events from existing monitoring, change, and topology systems.
BigPanda sits above connected operational tools and turns their alerts into enriched incidents. It serves large operations centers managing many event sources.
Teams can review a consolidated incident timeline instead of opening every source during triage.
Notable features:
- Event normalization and enrichment create consistent data across monitoring sources.
- Cross-tool correlation groups related events and adds topology, change, and incident context.
- The L1 Agent and automation features support investigation and selected response workflows.
- Change data helps identify problems associated with recent deployments.
Considerations: BigPanda generally depends on existing monitoring tools for underlying data. Review integration quality, event coverage, automation scope, contract structure, and reporting needs.
G2 reviewers praise its ability to turn alerts from many tools into fewer, explainable incidents. They also say effective correlation requires clean source data, upfront tuning, and continued rule maintenance.
7. IBM Concert
Known for: A modular IBM operations platform spanning observability, incident operations, and resource optimization across hybrid environments.
IBM is bringing several operations products together under Concert. The most relevant components for AIOps buyers are Concert Observe, Concert Operate, and Concert Optimize.
Concert Operate is the new name for IBM Cloud Pak for AIOps. It remains available separately while IBM expands the broader Concert platform.
Notable features:
- Concert Observe uses IBM Instana and SevOne for application, infrastructure, network, and AI visibility.
- Concert Operate connects alerts across domains and adds dependency and topology context.
- Ops Agent Diagnostics supports AI-assisted root-cause investigation.
- Concert Optimize uses IBM Turbonomic for performance, capacity, cost, and GPU optimization.
- Concert Operate supports an AWS bring-your-own-cloud option for customers needing direct data control.
Considerations: Confirm which Concert products, entitlements, and deployment models support your intended workflows. Existing Cloud Pak customers should also review migration and upgrade requirements.
G2 reviewers highlight cross-cloud integration, event grouping, and broad operational visibility under the earlier Cloud Pak name. Common concerns include deployment effort, training requirements, platform performance, and cost. These reviews predate Concert packaging, so use them as implementation context and verify current workflows directly.
8. PagerDuty AIOps
Known for: On-call operations, alert grouping, escalation, and incident-response orchestration.
PagerDuty AIOps receives events from monitoring systems and helps response teams manage what follows. Its workflows center on routing, coordination, and action.
Teams can trigger runbooks, collaboration steps, or escalations from established incident rules.
Notable features:
- Intelligent Alert Grouping combines related events before they create separate incidents.
- Event Orchestration routes and enriches events using rules and operational context.
- Change correlation adds recent deployment information to an active incident.
- AI Orchestrations reviews historical incidents and recommends Event Orchestration rules for approval.
- Runbook Automation executes approved diagnostic and response workflows through PagerDuty Automation.
- Response Analytics helps leaders review incident patterns and process performance after resolution.
Considerations: PagerDuty starts with events from connected monitoring systems instead of collecting raw telemetry directly. AIOps is available as an add-on, while Signal Intelligence provides similar capabilities within PagerDuty Reliability plans. Runbook Automation may require a separate PagerDuty Automation add-on. Review event volumes, plan limits, orchestration features, automation safeguards, and total cost.
TrustRadius reviewwers praise reliable alert delivery, flexible on-call scheduling, and extensive integrations. Common concerns include interface complexity, mobile usability, reporting limitations, and pricing.
What to Look for in an AIOps Platform
Document where investigations slow down before comparing feature lists. Your current bottleneck should shape the capabilities and tests you prioritize.
For example, alert volume may be the main problem. Another team may already group alerts well but still struggle to confirm root cause.
Use the following questions to compare each platform against the same operational needs:
| Evaluation Area | Question to Ask | What to Test |
|---|---|---|
| Operational coverage | Which applications, infrastructure, networks, cloud services, Kubernetes systems, and AI workloads can it analyze? | Use an incident crossing at least three technology layers. |
| Dependency context | Does the platform understand how components and services depend on each other? | Review a recent infrastructure change and confirm that the dependency model captured it. |
| Signal correlation | Can it group symptoms and reduce alert noise while preserving important evidence? | Replay an alert storm from a previous incident. |
| Root-cause evidence | Which signals support the platform’s conclusion? | Verify the diagnosis without opening several other tools. |
| Service impact | Can it identify affected services, customers, or service commitments? | Trace one infrastructure constraint to a customer-facing service. |
| Prediction | Can it identify capacity, performance, or availability risks early? | Use historical data from a period of gradual degradation. |
| Automation and governance | Can the system automate diagnosis and remediation while still enabling your team to limit, approve, pause, audit, and recover from automated actions? | Run one low-risk action through the approval process. |
| Deployment and data control | Does it meet your hosting, security, and isolation requirements? | Confirm where operational data and platform models reside. |
| Integrations | Does it fit your observability, service management, automation, and collaboration tools? | Test every integration required during a real incident. |
| Total operating cost | What will data, modules, infrastructure, services, and administration cost? | Model current usage and three years of expected growth. |
Feature coverage alone cannot show how well a product supports an investigation. Give each finalist a historical incident that crossed several technology layers.
Ask the vendor to reconstruct the incident from detection through response. Record how many tools, manual steps, and team handoffs the process requires.
Check whether the platform reaches a likely root cause and shows the supporting evidence. Then test one low-risk action with your normal approval controls.
Test daily operating work alongside major outages. Routine alert review can expose tuning, ownership, and reporting gaps.
If monitoring consolidation is a goal, confirm that broader coverage preserves the technical depth your teams use today.
Turn Your AIOps Shortlist Into a Real Test
Choose two or three platforms that match your required approach. Then give every finalist the same historical incident and success criteria.
Use the test to answer six practical questions:
- Which evidence did the platform collect and connect?
- How many manual steps were required to reach a likely root cause?
- How many teams and tools were involved?
- Did the platform show the affected service and supporting dependencies?
- Could one approved action run within your governance controls?
- What will the complete deployment cost be as your environment grows?
Record results while each test runs. A polished demonstration can feel convincing, yet your own incident reveals daily operational fit.
Invite security, service owners, and platform teams to review the results before signing a contract.
If your hardest incidents cross applications, infrastructure, networks, storage, Kubernetes, and AI systems, include Virtana in the final test.
See how the System Dependency Graph and AI agents build context, diagnose, control and govern a real environment.
Frequently Asked Questions
What Is an AIOps Tool?
An AIOps tool uses machine learning and AI to analyze operational data. Common inputs include metrics, logs, traces, events, alerts, topology, and service tickets.
The tool may group related events, suggest likely causes, identify affected services, or support selected actions. Exact capabilities depend on its role and the data it uses.
What Capabilities Should an AIOps Platform Include?
An AIOps platform should collect or connect relevant operational data. It should correlate related signals while preserving the evidence needed for verification.
Strong platforms also understand dependencies, identify unusual behavior, estimate service impact, and support diagnosis. Automation should include clear permissions, approvals, records, and recovery options.
How Is AIOps Different From Observability?
Observability helps teams understand system behavior through metrics, logs, traces, events, and dependency context. AIOps analyzes this evidence to identify patterns and guide response.
Many observability platforms include AIOps capabilities. Some AIOps products instead analyze alerts or service records from already deployed tools.
How Do AIOps Tools Reduce Alert Noise?
AIOps tools can deduplicate repeated alerts and group events that share timing, topology, or service context. This process turns many symptoms into fewer incidents.
Effective correlation keeps the original evidence available. Teams can review why events were grouped and confirm whether an important signal was excluded.
Can AIOps Platforms Automate Remediation?
Yes, although the scope varies considerably. Some platforms recommend an action, while others start scripts, workflows, or changes through connected automation tools.
Use policies and approvals to limit which actions can run. Audit records, testing, and recovery procedures help teams introduce automation safely.
How Is AIOps Different From Agentic Observability?
AIOps covers a broad set of analysis and automation techniques for IT operations. Agentic observability uses specialized AI agents to investigate issues and support action across connected system context.
An event-correlation rule can be part of AIOps without using an AI agent. Agentic workflows usually pursue a defined operational goal across several steps.
What Is the Best AIOps Tool?
The best AIOps tool fits your environment, existing stack, deployment needs, and most costly incident patterns.
Compare finalists with the same historical incident. Review their evidence, manual steps, integrations, automation controls, and complete operating costs before deciding.
Is Virtana an AIOps Platform?
Yes. Virtana provides AIOps capabilities within a system-aware Agentic Observability Platform.
The System Dependency Graph maintains connected context across applications and infrastructure. Event Intelligence correlates signals, while Diagnostic AI Agents investigate likely causes.
Remediation AI Agents can recommend or coordinate governed actions under policies and approvals. This approach helps teams move from observation to evidence-backed response.
Virtana Insight
