In today's complicated digital landscape, IT operations are faced with many challenges when it comes to managing vast infrastructures and ensuring system reliability. Outdated monitoring tools struggle to keep pace with the volume and velocity of data generated by modern, cloud-native applications and distributed systems.
Manual incident response and prolonged downtime cost enterprises millions of dollars each year, while overwhelming operations teams.
Artificial Intelligence for IT Operations (AIOps) changes this fact. By combining machine learning, big data analytics and automation these platforms analyze massive volumes of operational data in real time and can predict failures before they occur with remarkable precision.
This guide explores what Top AIOps companies offer, the types of solutions available and the possible considerations to pick them.
Understanding AIOps and Its Market Importance
AIOps platforms leverage artificial intelligence and machine learning to enhance IT operations through intelligent automation. Unlike traditional monitoring tools that simply collect metrics and generate alerts, AIOps systems correlate data across multiple sources, identify patterns and often resolve issues autonomously before they impact users.
The global AIOps market was valued at approximately 11.7 billion $ in 2023 and is forecast to reach around 32.4 billion $ by 2028, representing a robust compound annual growth rate (CAGR) of about 22.7% Marketshare says.
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Leading AIOps Companies
Below is a list of leading AIOps companies. We will explore what they excel at and where they might be lacking.
IBM with Watson AIOps
IBM stands among the most established AIOps providers, leveraging Watson's AI capabilities to deliver predictive insights and automated remediation across hybrid cloud environments. Watson AIOps ingests data from diverse sources, correlates incidents across domains, predicts anomalies before they cause outages, and automates responses through integration with enterprise runbooks.
What They Do Best:
Decades of enterprise IT management experience and proven reliability for mission-critical systems
Deep Watson AI integration with natural language processing for log analysis and incident understanding
Considerations:
Complex implementation requiring significant professional services engagement and expertise
Higher total cost of ownership compared to cloud-native alternatives
Cisco with ThousandEyes and AppDynamics
Cisco's AIOps capabilities span network intelligence through ThousandEyes and application performance through AppDynamics. ThousandEyes provides visibility into internet and cloud network performance with AI-powered anomaly detection for connectivity issues, while AppDynamics delivers application performance monitoring with business transaction analysis and automated root cause identification.
What They Do Best:
Unmatched network visibility leveraging Cisco's networking heritage and global infrastructure insights
End-to-end correlation from network layer through application business transactions
Considerations:
Requires separate licensing and integration for ThousandEyes and AppDynamics platforms
Pricing can escalate quickly for large-scale application monitoring deployments
Microsoft with Azure Monitor and Insights
Microsoft Azure embeds AIOps capabilities throughout its cloud platform via Azure Monitor, Application Insights, and Log Analytics. Machine learning models detect anomalies in metrics and logs, smart alerts reduce noise through dynamic thresholds and grouping, and automated recommendations suggest performance optimizations and cost savings.
What They Do Best:
Native Azure integration providing seamless experiences for Microsoft ecosystem deployments
Unified platform combining infrastructure, application, and business intelligence with AI-powered insights
Considerations:
Limited capabilities for monitoring non-Azure or multi-cloud environments
Advanced features may require Azure-specific services and architecture decisions
Splunk
Splunk transformed from a log management leader into a comprehensive observability and AIOps platform. Splunk IT Service Intelligence (ITSI) applies machine learning to IT and business data, performing predictive analytics for capacity planning and incident prediction, correlating events across infrastructure layers, and providing customizable dashboards linking IT metrics to business KPIs.
What They Do Best:
Industry-leading data ingestion and search capabilities handling massive log volumes at scale
Powerful correlation engine linking IT performance directly to business outcomes and revenue impact
Considerations:
Data ingestion-based pricing model can become expensive with high-volume log environments
Steep learning curve requiring specialized Splunk expertise for advanced configurations
Datadog
Datadog delivers unified monitoring and AIOps for cloud-scale infrastructure and applications. Its platform combines metrics, traces, and logs with anomaly detection algorithms, watchdog automated monitoring that surfaces unusual behavior without configuration, and forecasting capabilities for resource planning.
What They Do Best:
Cloud-native architecture optimized for modern containerized and serverless environments
Developer-friendly interface with extensive integration library covering 600+ technologies
Considerations:
Per-host pricing can become costly for large infrastructure footprints
Less mature for legacy on-premises infrastructure compared to cloud workloads
Dynatrace
Dynatrace pioneered automatic instrumentation and AI-powered observability through its proprietary Davis AI engine. Davis performs automatic root cause analysis pinpointing precise causes without manual correlation, predictive problem detection identifying issues before user impact, and business impact analysis quantifying the effect of technical issues on revenue and user experience.
What They Do Best:
Automatic instrumentation and topology discovery requiring minimal configuration overhead
Proprietary Davis AI engine delivering precise root cause analysis without alert correlation rules
Considerations:
Premium pricing positioning it at the higher end of the market
Proprietary agent approach may limit flexibility in highly customized environments
New Relic
New Relic offers full-stack observability with integrated AIOps capabilities for modern cloud applications. Applied Intelligence features include proactive anomaly detection across all telemetry types, incident intelligence that correlates related alerts into unified incidents, and root cause suggestions based on historical patterns and topology.
What They Do Best:
Unified telemetry platform eliminating data silos across metrics, events, logs, and traces
Consumption-based pricing model aligned with actual usage rather than fixed host counts
Considerations:
AIOps features are less advanced compared to specialized pure-play AIOps vendors
Historical focus on application monitoring means infrastructure capabilities are still maturing
Moogsoft
Moogsoft specializes in algorithmic correlation and noise reduction for enterprises overwhelmed by alert volumes. Its platform ingests alerts from hundreds of monitoring tools, applies AI to group related alerts into actionable situations, assigns collaboration workflows, and learns from operator feedback to improve accuracy continuously.
What They Do Best:
Purpose-built for alert noise reduction, achieving 90%+ alert volume reduction in complex environments
Self-learning algorithms that improve correlation accuracy through operator feedback loops
Considerations:
Requires integration with existing monitoring tools rather than providing native data collection
Focused on correlation and alerting rather than comprehensive observability capabilities
BigPanda
BigPanda focuses on event correlation and automation for IT operations and NOCs. Its Autonomous Digital Operations platform aggregates alerts from any source, correlates them using topology and timing analysis, enriches incidents with context from CMDB and other sources, and automates ticket creation and routing.
What They Do Best:
Topology-aware correlation leveraging CMDB and configuration data for accurate incident grouping
Unified incident view consolidating fragmented monitoring toolchains into single operational dashboard
Considerations:
Depends on quality and completeness of CMDB data for optimal correlation accuracy
Limited native monitoring capabilities requiring existing monitoring infrastructure
ServiceNow with Predictive AIOps
ServiceNow integrates AIOps capabilities directly into its IT Service Management platform through Predictive AIOps and Health Log Analytics. These features predict outages based on historical patterns and current signals, automate ticket categorization and routing using natural language processing, and recommend resolutions from knowledge bases and past incidents.
What They Do Best:
Seamless integration with enterprise service workflows closing the loop from detection to resolution
Natural language processing automating ticket categorization and knowledge article recommendations
Considerations:
AIOps features require ServiceNow ITSM platform investment and adoption
Monitoring and observability capabilities less comprehensive than dedicated APM vendors
BMC Helix Operations Management
BMC combines decades of IT operations experience with modern AIOps capabilities in Helix Operations Management. The platform performs event management with AI-powered correlation, predictive service insights anticipating issues before impact, and autonomous remediation through runbook automation.
What They Do Best:
Deep expertise managing hybrid environments combining legacy infrastructure with modern cloud services
Autonomous remediation capabilities executing runbooks automatically based on AI-predicted issues
Considerations:
Legacy architecture may feel less modern compared to cloud-native competitors
Implementation complexity for organizations without existing BMC infrastructure
Conclusion
Organizations evaluating AIOps should assess their current operational pain points, existing toolchain investments and desired outcomes to select providers and solutions aligned with their journey toward autonomous IT operations that deliver the reliability modern business demands.
After assessing what you need and what your strategies are, depending on your budget you can go for a company you think would answer your needs.
Note: Some visuals on this blog post were generated using AI tools.