AIOps - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)

The AIOps market stood at USD 16.42 billion in 2025 and is forecast to reach USD 36.60 billion by 2030, advancing at a 17.39% CAGR.

Demand rises as enterprises struggle with complex hybrid clouds, escalating observability data, and the pressure to cut operating costs while raising service resilience. Vendors now embed large language models into traditional monitoring, enabling autonomous incident response that reduces noise, accelerates root-cause discovery, and optimizes capacity planning. Platform consolidation is gathering pace as buyers tire of fragmented tool sets that inflate license spend and slow decision-making. Consumption-based pricing and open standards such as OpenTelemetry also lower entry barriers, pulling small and medium enterprises into the purchasing cycle.

Global AIOps Market Trends and Insights

AI-Driven Observability Demand Surge

Telemetry volume now runs into petabytes per day, overwhelming traditional monitoring. Modern AIOps platforms correlate logs, metrics, and traces to cut alert noise by up to 75%, while mission-critical sectors such as financial services record 99% mainframe task automation after consolidation onto a single platform. The capability becomes pivotal as cloud-native applications generate 10 times more data than monoliths, making manual triage impractical. Vendors embed machine learning that detects anomalous patterns across data silos, preventing user-visible failures and sustaining uptime requirements.

Shift to Hybrid/Multi-Cloud Architectures

About 82% of enterprises run hybrid strategies and 92% use multiple public clouds, creating fragmented visibility and diverse API surfaces.Forty-five percent already deploy AIOps to unify monitoring, and early adopters report 38% faster incident resolution once cross-domain correlation is automated. Economic urgency mounts as cloud expenditure climbs, making algorithmic resource optimization a board-level priority.

Tool Sprawl and ROI Uncertainty

Many organizations still juggle five or more monitoring tools, fragmenting context and delaying action. Integration costs rise before AIOps delivers its promised value, creating executive hesitation. The pressure is most visible in North America, where budgets tighten and procurement teams demand clear business-case evidence before greenlighting new platforms.

Other drivers and restraints analyzed in the detailed report include:

1. Need for Faster MTTR and SRE Adoption
2. Gen-AI Copilots for Ops
3. Shortage of AIOps-Savvy Talent

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Platform offerings captured 82.4% of 2024 revenue, reinforcing the view that unified telemetry ingestion and analytics trump point solutions. Services made up the remaining 17.6% as buyers sought configuration, model training, and change-management assistance. Enterprises confirm that a single console cuts swivel-chair fatigue and accelerates decision loops. Vendors now embed pretrained models that evolve through federated learning, raising detection accuracy over time. Services growth tracks the complexity of hybrid estates, where consultants map legacy systems into modern pipelines and enforce best-practice governance.

The platform-centric shift addresses lessons from earlier tool sprawl. Proprietary engines inside leading suites deliver granular anomaly scoring that is difficult to replicate via custom integration. Partner ecosystems deepen as specialists build turnkey dashboards and agentic add-ons. RapDev's Datadog-native AI agents illustrate the monetization potential in value-added layers, while IBM channels showcase Instana to capture adjacent service revenue.

On-premise deployments retained 56.2% share in 2024, upheld by strict data-residency rules in finance and government. The cloud segment, however, is scaling at an 18.7% CAGR to 2030 as buyers pivot to usage-based contracts that offload infrastructure management. Cloud vendors refresh AI models continuously, meaning subscribers gain incremental accuracy without forklift upgrades. Hybrid configurations now dominate proof-of-concept discussions, letting sensitive datasets stay on site while cloud analytics engines run correlation and inference at scale.

Cloud momentum signals a broader shift toward elasticity. When incidents spike, the platform can burst compute, completing multidimensional causal analysis in seconds. Encryption and zero-trust controls assuage prior security objections, encouraging even regulated entities to pilot managed observability. Cost governance features alert operations teams when ingestion volumes threaten budget thresholds, reducing surprise invoices.

Aiops Market is Segmented by Component (Platform and Services), Deployment Mode (On-Premises and Cloud), Organization Size (Small and Medium Enterprises and Large Enterprises), End-User Industry (IT and Telecom, BFSI, and More), and by Geography. The Market Forecasts are Provided in Terms of Value (USD).

Geography Analysis

North America led the AIOps market with 38.2% revenue in 2024. Early adopter enterprises, a robust vendor ecosystem, and sizable cloud budgets give the region scale advantages. Federal agencies log more than 1,200 AI use cases, 228 of which run in production, proving operational maturity in mission-critical settings. Mergers and acquisitions remain active, typified by ServiceNow's purchase of Logik.ai to enhance real-time workflow automation.

Asia-Pacific is the fastest-growing geography, forecasting a 19.2% CAGR. Governments in China, India, and Southeast Asian nations sponsor AI accelerators and subsidize cloud infrastructure, pushing enterprises to modernize operations. Observability investments deliver a median annual value of USD 10.08 million, exceeding other regions and highlighting the scale of digital transformation. Telecom operators integrate AIOps into 5G core networks to reduce outage penalties, while financial super-apps deploy anomaly detection to curb fraud at scale.

Europe maintains steady expansion propelled by ESG mandates, stringent data-sovereignty rules, and a preference for open standards. The region insists on algorithmic explainability, pressuring vendors to expose model logic and offer on-prem training options. Enterprises align AIOps rollouts with green-ops targets, measuring power consumption per telemetry gigabyte. Partnerships such as NTT DATA and HPE Aruba deliver sustainability-tuned observability suites that auto-scale resources in line with demand. Regulatory rigor slows initial procurement but ultimately cements vendor credibility when compliance certification is achieved.

1. IBM
2. Cisco (AppDynamics)
3. Splunk
4. Dynatrace
5. Broadcom (incl. VMware, CA)
6. BMC
7. BigPanda
8. Moogsoft
9. Elastic
10. New Relic
11. Datadog
12. PagerDuty
13. ServiceNow (Loom Systems)
14. ExtraHop
15. StackState
16. OpsRamp
17. Juniper (Mist AI)
18. Microsoft Azure Monitor
19. Amazon DevOps Guru
20. Google Cloud AIOps (Operations Suite)
21. SolarWinds

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support