PUBLISHER: Stratistics Market Research Consulting | PRODUCT CODE: 1865400
PUBLISHER: Stratistics Market Research Consulting | PRODUCT CODE: 1865400
According to Stratistics MRC, the Global AIOps for Cloud Infrastructure Market is accounted for $1.83 billion in 2025 and is expected to reach $7.55 billion by 2032 growing at a CAGR of 22.4% during the forecast period. AIOps for cloud infrastructure are the application of artificial intelligence and machine learning to automate and optimize IT operations across cloud environments. By analyzing vast volumes of telemetry, logs, and performance data, AIOps enables predictive maintenance, anomaly detection, and intelligent resource allocation. It enhances operational efficiency, reduces downtime, and supports dynamic scaling. AIOps platforms integrate with cloud-native tools to deliver real-time insights, streamline incident response, and ensure resilient, cost-effective infrastructure management in complex, multi-cloud or hybrid deployments.
Rising cloud complexity & demand for predictive analytics
AIOps platforms are gaining traction for their ability to automate anomaly detection, correlate events across distributed systems, and forecast resource needs. The growing emphasis on predictive analytics enables IT teams to anticipate outages and optimize workloads proactively. This shift toward intelligent automation is further accelerated by the need for real-time insights and faster incident resolution. Organizations are leveraging AIOps to streamline operations, reduce manual intervention, and enhance service availability.
Legacy systems and siloed data
Legacy systems often lack the interoperability required for seamless data ingestion and analysis, limiting the scope of automation. Additionally, siloed operational data across departments or cloud environments can obstruct unified visibility, reducing the effectiveness of AI-driven insights. These challenges are compounded by the need for extensive reconfiguration and skilled personnel to bridge compatibility gaps. As a result, deployment timelines may be extended, and ROI delayed.
Autonomous remediation and closed-loop automation
Closed-loop automation enables continuous feedback between monitoring tools and orchestration engines, allowing for dynamic adjustments based on real-time conditions. This capability is particularly valuable in high-scale environments where manual troubleshooting is impractical. Vendors are investing in AI models that not only identify root causes but also trigger remediation workflows, such as restarting services or reallocating resources. These advancements are paving the way for resilient, adaptive cloud ecosystems.
Evolving AI governance and cloud compliance laws
Emerging legislation across regions mandates transparency in algorithmic decision-making and restricts data processing practices. Non-compliance can lead to legal penalties and reputational damage, especially for global enterprises operating across jurisdictions. Moreover, frequent changes in governance frameworks may require continuous updates to AIOps configurations and audit mechanisms. This regulatory volatility poses a strategic risk for vendors and users alike, potentially slowing innovation and adoption.
The pandemic accelerated digital transformation across industries, prompting a surge in cloud adoption and remote infrastructure management. AIOps emerged as a critical enabler for maintaining uptime and performance in distributed environments. However, initial disruptions in IT staffing and budget reallocations temporarily stalled implementation projects. As remote work became the norm, demand for intelligent monitoring and automated incident response grew significantly. Organizations prioritized tools that could operate with minimal human oversight, reinforcing the value proposition of AIOps.
The event correlation & root cause analysis segment is expected to be the largest during the forecast period
The event correlation & root cause analysis segment is expected to account for the largest market share during the forecast period propelled by, the segment's ability to synthesize vast volumes of telemetry data and pinpoint anomalies across complex environments. Enterprises rely on these capabilities to reduce mean time to resolution (MTTR) and prevent cascading failures. Advanced correlation engines are being integrated with observability platforms to provide contextual insights and actionable diagnostics. The segment's maturity and widespread applicability across industries contribute to its leading market position.
The performance monitoring & optimization segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the performance monitoring & optimization segment is predicted to witness the highest growth rate, influenced by, the increasing need to fine-tune cloud resources, minimize latency, and ensure consistent user experiences. AIOps tools in this segment leverage machine learning to detect performance bottlenecks and recommend configuration changes. The rise of containerized applications and microservices has further amplified the demand for granular, real-time performance insights. As organizations seek to align infrastructure efficiency with business outcomes, this segment is poised for rapid expansion.
During the forecast period, the Asia Pacific region is expected to hold the largest market share, fuelled by, Countries such as China, India, and Singapore are investing heavily in smart infrastructure and AI-driven IT operations. The region's thriving startup ecosystem and government-backed cloud modernization programs are fueling demand for scalable AIOps solutions. Additionally, the proliferation of hyperscale data centers and managed service providers is creating fertile ground for market growth. Enterprises in APAC are increasingly prioritizing automation to manage complex, high-volume workloads.
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by, its rapid technological advancement and expanding enterprise cloud footprint. The region's emphasis on AI innovation, coupled with rising investments in IT infrastructure, is accelerating AIOps adoption. Local vendors are introducing cost-effective, customizable platforms tailored to regional needs, boosting accessibility. Moreover, the growing awareness of operational resilience and cybersecurity is prompting organizations to deploy intelligent monitoring tools. This dynamic landscape positions APAC as a key growth engine for the global AIOps market.
Key players in the market
Some of the key players in AIOps for Cloud Infrastructure Market include Splunk, Dynatrace, IBM (Instana), SolarWinds, Moogsoft, PagerDuty, Datadog, New Relic, Elastic (ELK Stack), BMC Software, ServiceNow, Microsoft, Google, Amazon Web Services, AppDynamics, ScienceLogic, CA Technologies, and VMware.
In October 2025, Splunk expands its Observability Cloud to AWS Singapore, enhancing real-time insights for APAC enterprises. This move supports hybrid cloud adoption and strengthens Cisco-Splunk's regional footprint.
In October 2025, Dynatrace and ServiceNow announce strategic collaboration, the partnership aims to scale autonomous IT operations using agentic AI and intelligent automation. It combines Dynatrace's root cause analysis with ServiceNow's AIOps workflows.
In October 2025, IBM announces Instana GenAI Observability at TechXchange 2025. Instana now offers unified observability across IBM Turbonomic and Concert, enhancing AI-driven performance. The update supports resilience and spends optimization across complex IT environments.
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.