2026 Global: Artificial Intelligence (Ai) In Monitoring-Competitive Review (2032) report
Description
The 2026 Global: Artificial Intelligence (Ai) In Monitoring-Competitive Review (2031) report features the global market size and projected growth/decline data for the period 2021 through 2032. The report primarily provides an examination of the business strategies for the ten largest global companies in the market and how their strategies differ.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) in monitoring by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) in monitoring market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
Nvidia, Microsoft, Google (Alphabet), Amazon, IBM, Datadog, Dynatrace, Splunk, LogicMonitor, and Databricks are ten major companies shaping AI in monitoring through a mix of GPU hardware, cloud AI services, observability platforms, and data infrastructure. Nvidia supplies the essential GPU compute and software stacks that accelerate model training and real‑time inference for monitoring workloads, and its CUDA/ecosystem remains central to large model and telemetry processing pipelines. Microsoft integrates advanced AI across Azure Monitor and Azure AI services, embedding generative and anomaly‑detection capabilities into platform telemetry and enabling enterprise Copilots that interpret logs, traces, and metrics at scale. Google leverages its Vertex AI, observability tooling, and DeepMind research to provide AI‑driven insights across cloud monitoring, integrating model‑based anomaly detection and automated root‑cause analysis into Google Cloud operations. Amazon Web Services (AWS) couples scalable telemetry ingestion with services like Amazon DevOps Guru and SageMaker to apply machine learning to detect anomalies, forecast capacity, and suggest remediations across hybrid environments. IBM brings AIops and enterprise monitoring together through Watson and its observability suites, focusing on regulated industries where explainability and integration with legacy systems are key.
Datadog, Dynatrace, Splunk, LogicMonitor, and Databricks represent leading observability and data platforms that operationalize AI for monitoring, each with distinct strengths. Datadog unifies logs, metrics, traces, and security telemetry into an AI‑enriched observability platform that performs anomaly detection and correlates multi‑layer signals for faster troubleshooting. Dynatrace uses its Davis® AI engine to provide automated root‑cause analysis and predictive remediation across cloud‑native and microservice architectures, and it is frequently cited in analyst evaluations for its automated problem resolution capabilities. Splunk applies machine learning to security and operational telemetry, enabling correlation, predictive analytics, and incident investigation across large enterprise datasets. LogicMonitor emphasizes AI and ML for predictive maintenance and cross‑layer correlation of server, network, application, and cloud telemetry to identify issues before they affect users. Databricks supplies the data and model platform—its lakehouse architecture and Mosaic AI tooling—that underpins many monitoring AI workflows by enabling scalable feature engineering, model training, and deployment on unified telemetry datasets.
Together these companies cover the stack required for AI‑powered monitoring: compute and models (Nvidia), cloud AI and platform integration (Microsoft, Google, AWS, IBM), observability and AIOps applications (Datadog, Dynatrace, Splunk, LogicMonitor), and data-platform foundations for building and operationalizing models (Databricks). Their combined offerings address detection (real‑time anomaly and drift detection), diagnosis (automated root‑cause and causal analysis), prediction (capacity planning and failure forecasting), and automation (remediation playbooks and AI copilots), making modern monitoring more proactive and scalable across hybrid and multi‑cloud environments.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) in monitoring by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) in monitoring market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
Nvidia, Microsoft, Google (Alphabet), Amazon, IBM, Datadog, Dynatrace, Splunk, LogicMonitor, and Databricks are ten major companies shaping AI in monitoring through a mix of GPU hardware, cloud AI services, observability platforms, and data infrastructure. Nvidia supplies the essential GPU compute and software stacks that accelerate model training and real‑time inference for monitoring workloads, and its CUDA/ecosystem remains central to large model and telemetry processing pipelines. Microsoft integrates advanced AI across Azure Monitor and Azure AI services, embedding generative and anomaly‑detection capabilities into platform telemetry and enabling enterprise Copilots that interpret logs, traces, and metrics at scale. Google leverages its Vertex AI, observability tooling, and DeepMind research to provide AI‑driven insights across cloud monitoring, integrating model‑based anomaly detection and automated root‑cause analysis into Google Cloud operations. Amazon Web Services (AWS) couples scalable telemetry ingestion with services like Amazon DevOps Guru and SageMaker to apply machine learning to detect anomalies, forecast capacity, and suggest remediations across hybrid environments. IBM brings AIops and enterprise monitoring together through Watson and its observability suites, focusing on regulated industries where explainability and integration with legacy systems are key.
Datadog, Dynatrace, Splunk, LogicMonitor, and Databricks represent leading observability and data platforms that operationalize AI for monitoring, each with distinct strengths. Datadog unifies logs, metrics, traces, and security telemetry into an AI‑enriched observability platform that performs anomaly detection and correlates multi‑layer signals for faster troubleshooting. Dynatrace uses its Davis® AI engine to provide automated root‑cause analysis and predictive remediation across cloud‑native and microservice architectures, and it is frequently cited in analyst evaluations for its automated problem resolution capabilities. Splunk applies machine learning to security and operational telemetry, enabling correlation, predictive analytics, and incident investigation across large enterprise datasets. LogicMonitor emphasizes AI and ML for predictive maintenance and cross‑layer correlation of server, network, application, and cloud telemetry to identify issues before they affect users. Databricks supplies the data and model platform—its lakehouse architecture and Mosaic AI tooling—that underpins many monitoring AI workflows by enabling scalable feature engineering, model training, and deployment on unified telemetry datasets.
Together these companies cover the stack required for AI‑powered monitoring: compute and models (Nvidia), cloud AI and platform integration (Microsoft, Google, AWS, IBM), observability and AIOps applications (Datadog, Dynatrace, Splunk, LogicMonitor), and data-platform foundations for building and operationalizing models (Databricks). Their combined offerings address detection (real‑time anomaly and drift detection), diagnosis (automated root‑cause and causal analysis), prediction (capacity planning and failure forecasting), and automation (remediation playbooks and AI copilots), making modern monitoring more proactive and scalable across hybrid and multi‑cloud environments.
Table of Contents
32 Pages
- 1.0 Scope of Report and Methodology
- 2.0 Market SWOT Analysis and Players
- 2.1 Market Definition
- 2.2 Market Segments
- 2.3 Market Strengths
- 2.4 Market Weaknesses
- 2.5 Market Threats
- 2.6 Market Opportunities
- 2.7 Major Players
- 3.0 Competitive Analysis
- 3.1 Market Player 1
- 3.2 Market Player 2
- 3.3 Market Player 3
- 3.4 Market Player 4
- 3.5 Market Player 5
- 3.6 Market Player 6
- 3.7 Market Player 7
- 3.8 Market Player 8
- 3.9 Market Player 9
- 3.10 Market Player 10
- 4.0 Comparative Business Strategies
- 4.1 Comparative Business Strategies of Player 1 and 2
- 4.2 Comparative Business Strategies of Player 1 and 3
- 4.3 Comparative Business Strategies of Player 1 and 4
- 4.4 Comparative Business Strategies of Player 2 and 3
- 4.5 Comparative Business Strategies of Player 2 and 4
- 4.6 Comparative Business Strategies of Player 3 and 4
- 5.0 Appendix
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