Artificial Intelligence (AI) In Networking Market: Global Forecast To 2032 AI Workloads Push Networks To Run Themselves, Fueling A $21 Billion Market

(MENAFN- GlobeNewsWire - Nasdaq) The global AI in network market is projected to grow from $8.38 billion in 2026 to $21.52 billion by 2032, a new report finds, as operators automate 5G, edge and cloud networks; Cisco, Nokia, Ericsson and NVIDIA are among 35 companies profiled.

Dublin, Oct. 21, 2026 (GLOBE NEWSWIRE) -- "AI in Network Market: Global Forecast to 2032" has been added to ResearchAndMarkets.com's offering.

AI in Network Market to Reach USD 21.52 Billion by 2032 as Automation, 5G and Cloud Adoption Accelerate

The AI in network market is projected to grow from USD 8.38 billion in 2026 to USD 21.52 billion by 2032, registering a CAGR of 17.0%. Market expansion is being driven by the growing adoption of AI-powered network automation, 5G, edge computing, cloud infrastructure and IoT across telecom operators, enterprises, data centers and public-sector organizations.

Organizations are integrating AI into network operations to support real-time network optimization, predictive maintenance, anomaly detection, automated troubleshooting and enhanced cybersecurity. These capabilities help improve network performance, reduce downtime, optimize resource utilization and lower operational costs. Rising investment in digital infrastructure, combined with increasing demand for reliable, secure and low-latency connectivity, is expected to accelerate the transition toward autonomous and intelligent networks.

Data center networks to account for the largest market share

By network type, data center networks are expected to hold the largest share of the AI in network market during the forecast period. Growth is supported by the rapid deployment of AI workloads, cloud computing, generative AI, machine learning and high-performance computing across enterprises and hyperscalers.

These workloads require high-bandwidth, low-latency and scalable infrastructure capable of transferring large datasets among GPUs, servers and storage systems. AI-driven networking solutions enable data centers to automate network operations, optimize traffic management, identify performance issues and improve resource utilization. Continued investment in next-generation data centers is expected to sustain demand for AI-enabled network monitoring, automation, security and performance optimization.

Cloud networks to register the fastest growth

The cloud networks segment is forecast to record the highest growth rate as enterprises expand cloud migration initiatives and adopt hybrid and multicloud architectures. Increasing infrastructure complexity is creating demand for AI-enabled solutions that provide real-time visibility, automated configuration, intelligent traffic management and predictive performance optimization.

AI-driven cloud networking supports anomaly detection, resource optimization and operational automation across cloud environments, data centers, branch locations and edge infrastructure. The expansion of cloud-native applications and the need for secure, resilient and scalable connectivity will further strengthen demand throughout the forecast period.

Asia Pacific positioned as the fastest-growing regional market

Asia Pacific is expected to register the highest growth rate in the AI in network market. China, India, Japan, South Korea and Australia are investing significantly in 5G infrastructure, data centers, cloud networks and advanced telecommunications systems to support increasing data traffic and connected devices.

Telecom operators and enterprises across the region are deploying AI-powered solutions for predictive maintenance, traffic management, anomaly detection, network optimization and automated operations. The proliferation of IoT, edge computing and 5G is generating substantial volumes of network data, expanding opportunities for AI-driven analytics. Government initiatives supporting digitalization, smart infrastructure and next-generation connectivity are also contributing to regional market growth.

Market opportunities, restraints and challenges

The report identifies the proliferation of AI agents, increasing DNS/IP demand, changing traffic patterns from AI workloads and growing interest in autonomous, self-optimizing and closed-loop networks as important market drivers. Opportunities include the adoption of generative and agentic AI for autonomous network operations, along with the expansion of AI-powered network management across cloud, edge and 5G environments.

Market growth may be constrained by limited explainability and trust in AI-driven network decisions, as well as the complexity of integrating AI with legacy network infrastructure. Additional challenges include maintaining high-quality, reliable and secure network data for AI models while addressing cybersecurity, data governance and operational risks.

Competitive landscape and research coverage

The report evaluates the market by offering, network type, end user and region. It also provides competitive analysis, company profiles, recent developments, product and business strategies, research and development activity, new launches, investment trends and opportunities in underserved markets.

Leading participants include Cisco, HPE, Nokia, Ericsson, Huawei, Arista Networks, NVIDIA, Extreme Networks, Broadcom, IBM, Fujitsu, Dell Technologies, Ciena, NEC, Forward Networks, BlueCat Networks, A10 Networks, ScienceLogic, Versa Networks, Dynatrace, Infoblox, EfficientIP, Riverbed, NETSCOUT, SolarWinds, NetBrain, ManageEngine, VIAVI Solutions, Selector, Aviz Networks, Nile, NetBox Labs, Celona, Shabodi and A5G Networks.

Breakdown of primary research participants

• By Company Type: Tier 1 (34%), Tier 2 (43%) and Tier 3 (23%)
• By Designation: C-level Executives (50%), Directors (30%) and Others (20%)
• By Region: North America (25%), Europe (30%), Asia Pacific (30%), Middle East & Africa (10%) and Latin America (5%)

The report provides market leaders and new entrants with revenue estimates, segment-level growth projections and analysis of key drivers, restraints, opportunities and challenges. These insights can help stakeholders assess the competitive environment, refine go-to-market strategies, identify product development priorities and evaluate growth opportunities across global AI networking markets.

Key Attributes:

Report Attribute	Details
No. of Pages	376
Forecast Period	2026 - 2032
Estimated Market Value (USD) in 2026	$8.38 billion
Forecasted Market Value (USD) by 2032	$21.52 billion
Compound Annual Growth Rate	17.0%
Regions Covered	Global

Key Topics Covered:
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 MARKET SCOPE
1.3.1 MARKET SEGMENTATION AND REGIONAL SCOPE
1.3.2 INCLUSIONS AND EXCLUSIONS
1.4 YEARS CONSIDERED
1.5 CURRENCY CONSIDERED
1.6 STAKEHOLDERS
2 EXECUTIVE SUMMARY
2.1 MARKET HIGHLIGHTS AND KEY INSIGHTS
2.2 KEY MARKET PARTICIPANTS: MAPPING OF STRATEGIC DEVELOPMENTS
2.3 DISRUPTIVE TRENDS IN AI IN NETWORK MARKET
2.4 HIGH-GROWTH SEGMENTS
2.5 REGIONAL SNAPSHOT: MARKET SIZE, GROWTH RATE, AND FORECAST
3 PREMIUM INSIGHTS
3.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI IN NETWORK MARKET
3.2 AI IN NETWORK MARKET, BY SERVICE AND REGION
3.3 AI IN NETWORK MARKET, BY OFFERING
3.4 AI IN NETWORK MARKET, BY END USER
4 MARKET OVERVIEW AND INDUSTRY TRENDS
4.1 INTRODUCTION
4.2 MARKET DYNAMICS
4.2.1 DRIVERS
4.2.1.1 AI agent proliferation increasing DNS/IP demand
4.2.1.2 Growing adoption of AI, generative AI, and AI agents for network operations and automation
4.2.1.3 Rising complexity and changing traffic patterns driven by AI workloads
4.2.1.4 Increasing focus on autonomous, self-optimizing, and closed-loop networks
4.2.2 RESTRAINTS
4.2.2.1 Lack of explainability and trust in AI-driven network decisions
4.2.2.2 High complexity of integrating AI with legacy network infrastructure
4.2.3 OPPORTUNITIES
4.2.3.1 Expansion of autonomous and intent-based networking enabled by AI
4.2.3.2 Expansion of AI-powered network management across cloud, edge, and 5G environments
4.2.4 CHALLENGES
4.2.4.1 Ensuring high-quality, reliable, and secure network data for AI models
4.2.4.2 Managing cybersecurity, data governance, and operational risks
4.3 INTERCONNECTED MARKETS AND CROSS-SECTOR OPPORTUNITIES
4.3.1 CLOUD COMPUTING AND DATA CENTER MARKET
4.3.2 INTERNET OF THINGS (IOT) MARKET
4.3.3 CYBERSECURITY MARKET
4.3.4 TELECOMMUNICATIONS AND 5G MARKET
4.3.5 HEALTHCARE AND LIFE SCIENCES
4.3.6 MANUFACTURING AND INDUSTRIAL AUTOMATION
4.3.7 TRANSPORTATION AND SMART MOBILITY
4.3.8 ENERGY AND UTILITIES
4.3.9 RETAIL AND SMART ENTERPRISE
4.4 STRATEGIC MOVES BY TIER-1/2/3 PLAYERS
5 INDUSTRY TRENDS
5.1 PORTER'S FIVE FORCES ANALYSIS
5.1.1 THREAT OF NEW ENTRANTS
5.1.2 THREAT OF SUBSTITUTES
5.1.3 BARGAINING POWER OF SUPPLIERS
5.1.4 BARGAINING POWER OF BUYERS
5.1.5 INTENSITY OF COMPETITIVE RIVALRY
5.2 MACROECONOMICS INDICATORS
5.2.1 INTRODUCTION
5.2.2 GDP TRENDS AND FORECAST
5.2.3 TRENDS IN TELECOMMUNICATIONS INDUSTRY
5.2.4 TRENDS IN DATA CENTER & CLOUD INFRASTRUCTURE INDUSTRY
5.3 SUPPLY CHAIN ANALYSIS
5.4 ECOSYSTEM ANALYSIS
5.5 PRICING ANALYSIS
5.5.1 AVERAGE SELLING PRICE OF AI-ENABLED NETWORK MANAGEMENT PLATFORMS, BY KEY PLAYERS
5.5.2 INDICATIVE PRICING ANALYSIS OF KEY PLAYERS, BY SOLUTION, 2025
5.6 KEY CONFERENCES AND EVENTS, 2026-2027
5.7 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.8 INVESTMENT AND FUNDING SCENARIO
5.9 CASE STUDY ANALYSIS
5.9.1 CISCO'S SELF-DEFENDING NETWORK FOR AI-DRIVEN SECURITY
5.9.2 NOKIA AI-POWERED AUTONOMOUS RAN OPERATIONS FOR STC
5.9.3 ERICSSON AI-DRIVEN NETWORK OPTIMIZATION FOR AT&T
5.9.4 SHABODI ENABLES AI-DRIVEN COGNITIVE FACTORY FOR GLOBAL MANUFACTURER
5.9.5 AVIZ NETWORKS BUILDS AI-READY NETWORK FOR GLOBAL E-COMMERCE
5.10 IMPACT OF 2025 US TARIFF - AI IN NETWORK MARKET
5.10.1 INTRODUCTION
5.10.2 KEY TARIFF RATES
5.10.3 PRICE IMPACT ANALYSIS
5.10.3.1 Strategic shifts and emerging trends
5.10.4 IMPACT ON COUNTRIES/REGIONS
5.10.4.1 US
5.10.4.2 Europe
5.10.4.3 Asia Pacific
5.10.5 IMPACT ON END-USER INDUSTRIES
5.10.5.1 Enterprises
5.10.5.2 Telecom & communication service providers
5.10.5.3 Government & defense
6 TECHNOLOGICAL ADVANCEMENTS, AI-DRIVEN IMPACT, PATENTS, INNOVATIONS, AND FUTURE APPLICATIONS
6.1 KEY EMERGING TECHNOLOGIES
6.1.1 ARTIFICIAL INTELLIGENCE (AI)
6.1.2 MACHINE LEARNING (ML)
6.1.3 GENERATIVE AI (GEN AI) AND LARGE LANGUAGE MODELS (LLMS)
6.1.4 AGENTIC AI
6.1.5 AI-NATIVE NETWORK AUTOMATION AND AUTONOMOUS NETWORKING
6.1.6 COMPLEMENTARY TECHNOLOGIES
6.1.6.1 Edge computing
6.1.6.2 Cloud computing
6.1.6.3 Network telemetry and observability
6.1.6.4 5G and 6G connectivity
6.1.7 ADJACENT TECHNOLOGIES
6.1.7.1 Digital twins
6.1.7.2 Internet of Things (IoT)
6.1.7.3 Intent-based networking (IBN)
6.1.7.4 Zero-touch network management
6.2 TECHNOLOGY/PRODUCT ROADMAP
6.2.1 SHORT-TERM ROADMAP (2026-2027)
6.2.2 MID-TERM ROADMAP (2028-2029)
6.2.3 LONG-TERM ROADMAP (2030-2032)
6.3 PATENT ANALYSIS
6.4 IMPACT OF AI/GEN AI ON AI IN NETWORK MARKET
6.4.1 TOP USE CASES AND MARKET POTENTIAL
6.4.2 BEST PRACTICES IN AI IN NETWORK PROCESS
6.4.3 CASE STUDIES OF AI IMPLEMENTATION IN AI IN NETWORK MARKET
6.4.4 INTERCONNECTED ADJACENT ECOSYSTEM AND IMPACT ON MARKET PLAYERS
6.4.5 CLIENTS' READINESS TO ADOPT GENERATIVE AI IN AI IN NETWORK
7 REGULATORY LANDSCAPE
7.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
7.2 INDUSTRY STANDARDS
8 CUSTOMER LANDSCAPE AND BUYING BEHAVIOR
8.1 DECISION-MAKING PROCESS
8.2 KEY STAKEHOLDERS INVOLVED IN BUYING PROCESS AND THEIR EVALUATION CRITERIA
8.2.1 KEY STAKEHOLDERS IN BUYING PROCESS
8.2.2 BUYING CRITERIA
8.3 ADOPTION BARRIERS AND INTERNAL CHALLENGES
8.4 UNMET NEEDS FROM VARIOUS END-USE INDUSTRIES
9 AI IN NETWORK MARKET, BY OFFERING
9.1 INTRODUCTION
9.1.1 OFFERING: AI IN NETWORK MARKET DRIVERS
9.2 SOLUTIONS
9.2.1 ENABLING INTELLIGENT, AUTOMATED, AND AUTONOMOUS NETWORK OPERATIONS
9.2.2 AI-DRIVEN NETWORK MANAGEMENT
9.2.2.1 Network monitoring & management
9.2.2.2 Network observability
9.2.2.3 DDI
9.2.2.4 Fault management
9.2.2.5 Performance management
9.2.3 AI-DRIVEN NETWORK OPTIMIZATION
9.2.3.1 Traffic optimization
9.2.3.2 Capacity optimization
9.2.3.3 Predictive performance analytics
9.2.3.4 Observability-driven optimization
9.2.4 AI-DRIVEN NETWORK SECURITY
9.2.4.1 Network policy & risk analytics
9.2.4.2 DNS security
9.2.4.3 Anomaly detection
9.2.4.4 Threat detection & response
9.2.5 AI-DRIVEN NETWORK AUTOMATION
9.2.5.1 Configuration automation
9.2.5.2 Provisioning automation
9.2.5.3 Policy automation
9.2.5.4 Closed-loop automation
9.2.5.5 DDI automation
9.2.6 AI-DRIVEN NETWORK ORCHESTRATION
9.2.6.1 Multi-domain network orchestration
9.2.6.2 Service orchestration
9.2.6.3 Intent-based orchestration
9.2.6.4 Autonomous network orchestration
9.2.7 AI-DRIVEN NETWORK PLANNING & DESIGN
9.2.7.1 Capacity planning & forecasting
9.2.7.2 Network architecture & topology design
9.2.7.3 Network simulation & digital twin
9.2.8 OTHER SOLUTIONS
9.3 SERVICES
9.3.1 ENABLING AI-DRIVEN NETWORK TRANSFORMATION AND AUTONOMOUS OPERATIONS
9.3.2 PROFESSIONAL SERVICES
9.3.2.1 Consulting & advisory
9.3.2.2 Integration & deployment
9.3.2.3 Support & maintenance
9.3.3 MANAGED SERVICES
10 AI IN NETWORK MARKET, BY NETWORK TYPE
10.1 INTRODUCTION
10.1.1 NETWORK TYPE: AI IN NETWORK MARKET DRIVERS
10.2 DATA CENTER NETWORKS
10.2.1 AI-DRIVEN OPTIMIZATION OF HIGH-PERFORMANCE INFRASTRUCTURE
10.3 CLOUD NETWORKS
10.3.1 AI-ENABLED MANAGEMENT OF DISTRIBUTED CLOUD INFRASTRUCTURE
10.4 WIRELESS NETWORKS
10.4.1 AI-DRIVEN OPTIMIZATION OF NEXT-GENERATION CONNECTIVITY
10.5 SOFTWARE-DEFINED WIDE AREA NETWORKS (SD-WAN)
10.5.1 AI-ENABLED INTELLIGENT TRAFFIC MANAGEMENT
10.6 CAMPUS NETWORKS
10.6.1 AI-DRIVEN NETWORK AUTOMATION AND PERFORMANCE MANAGEMENT
10.7 OTHER NETWORK TYPES
11 AI IN NETWORK MARKET, BY END USER
11.1 INTRODUCTION
11.1.1 END USER: AI IN NETWORK MARKET DRIVERS
11.2 SERVICE PROVIDERS
11.2.1 INCREASING ADOPTION OF AI TO ENABLE AUTONOMOUS AND INTELLIGENT NETWORK OPERATIONS
11.3 ENTERPRISES
11.3.1 GROWING DEPLOYMENT OF AI-DRIVEN NETWORKS TO ENHANCE OPERATIONAL EFFICIENCY, SECURITY, AND CONNECTIVITY
11.3.2 BFSI
11.3.3 HEALTHCARE
11.3.4 RETAIL & E-COMMERCE
11.3.5 MANUFACTURING
11.3.6 GOVERNMENT & DEFENSE
11.3.7 ENERGY & UTILITIES
11.3.8 OTHER ENTERPRISES
12 AI IN NETWORK MARKET, BY REGION
12.1 INTRODUCTION
12.2 NORTH AMERICA
12.2.1 NORTH AMERICA: AI IN NETWORK MARKET DRIVERS
12.2.2 US
12.2.2.1 Strong AI infrastructure investments driving autonomous and AI-native networks
12.2.3 CANADA
12.2.3.1 Sovereign AI infrastructure and 5G modernization supporting intelligent networks
12.3 EUROPE
12.3.1 EUROPE: AI IN NETWORK MARKET DRIVERS
12.3.2 UK
12.3.2.1 AI-enabled network automation and growing demand for intelligent connectivity
12.3.3 GERMANY
12.3.3.1 Industry 4.0 and autonomous network operations driving AI adoption
12.3.4 ITALY
12.3.4.1 5G modernization and industrial digitalization accelerating AI in networks
12.3.5 FRANCE
12.3.5.1 AI-native communications and digital sovereignty strengthening network intelligence
12.3.6 REST OF EUROPE
12.4 ASIA PACIFIC
12.4.1 ASIA PACIFIC: AI IN NETWORK MARKET DRIVERS
12.4.2 CHINA
12.4.2.1 Government-led AI integration and extensive 5G infrastructure driving autonomous networks
12.4.3 INDIA
12.4.3.1 Rapid 5G expansion and high data consumption fueling AI-driven network optimization
12.4.4 JAPAN
12.4.4.1 Advanced telecom infrastructure and AI automation supporting next-generation networks
12.4.5 REST OF ASIA PACIFIC
12.5 MIDDLE EAST & AFRICA
12.5.1 MIDDLE EAST & AFRICA: AI IN NETWORK MARKET DRIVERS
12.5.2 UAE
12.5.2.1 Sovereign AI and advanced 5G infrastructure accelerating AI-native network development
12.5.3 KSA
12.5.3.1 Vision 2030 and AI infrastructure investments driving intelligent connectivity
12.5.4 SOUTH AFRICA
12.5.4.1 5G Expansion and Digital Infrastructure Investment Supporting AI-Enabled Networks
12.5.5 REST OF MIDDLE EAST & AFRICA
12.6 LATIN AMERICA
12.6.1 LATIN AMERICA: AI IN NETWORK MARKET DRIVERS
12.6.2 BRAZIL
12.6.2.1 5G densification and operator automation driving adoption of AI in network
12.6.3 MEXICO
12.6.3.1 5G expansion and industrial digitalization supporting intelligent network growth
12.6.4 REST OF LATIN AMERICA
13 COMPETITIVE LANDSCAPE
13.1 INTRODUCTION
13.2 KEY PLAYER COMPETITIVE STRATEGIES/RIGHT TO WIN, 2023-2026
13.3 MARKET SHARE ANALYSIS, 2025
13.4 REVENUE ANALYSIS, 2021-2025
13.5 BRAND/PRODUCT COMPARISON
13.6 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2025
13.6.1 STARS
13.6.2 EMERGING LEADERS
13.6.3 PERVASIVE PLAYERS
13.6.4 PARTICIPANTS
13.6.5 COMPANY FOOTPRINT: KEY PLAYERS, 2025
13.6.5.1 Company footprint
13.6.5.2 Region footprint
13.6.5.3 Offering footprint
13.6.5.4 Solution footprint
13.6.5.5 Service footprint
13.6.5.6 Network type footprint
13.6.5.7 End user footprint
13.7 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2025
13.7.1 PROGRESSIVE COMPANIES
13.7.2 RESPONSIVE COMPANIES
13.7.3 DYNAMIC COMPANIES
13.7.4 STARTING BLOCKS
13.7.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2025
13.7.5.1 Detailed list of key startups/SMEs
13.7.5.2 Competitive benchmarking of key startups/SMEs
13.8 COMPANY VALUATION AND FINANCIAL METRICS
13.9 COMPETITIVE SCENARIO
13.9.1 PRODUCT LAUNCHES/ENHANCEMENTS
13.9.2 DEALS
14 COMPANY PROFILES
14.1 MAJOR PLAYERS
14.1.1 CISCO
14.1.1.1 Business overview
14.1.1.2 Products/Solutions/Services offered
14.1.1.3 Recent developments
14.1.1.3.1 Product launches/enhancements
14.1.1.3.2 Deals
14.1.1.4 Analyst's view
14.1.1.4.1 Right to win
14.1.1.4.2 Strategic choices
14.1.1.4.3 Weaknesses and competitive threats
14.1.2 HPE
14.1.2.1 Business overview
14.1.2.2 Products/Solutions/Services offered
14.1.2.3 Recent developments
14.1.2.3.1 Product launches/enhancements
14.1.2.3.2 Deals
14.1.2.4 Analyst's view
14.1.2.4.1 Right to win
14.1.2.4.2 Strategic choices
14.1.2.4.3 Weaknesses and competitive threats
14.1.3 NOKIA
14.1.3.1 Business overview
14.1.3.2 Products/Solutions/Services offered
14.1.3.3 Recent developments
14.1.3.3.1 Product launches/enhancements
14.1.3.3.2 Deals
14.1.3.4 Analyst's view
14.1.3.4.1 Right to win
14.1.3.4.2 Strategic choices
14.1.3.4.3 Weaknesses and competitive threats
14.1.4 HUAWEI
14.1.4.1 Business overview
14.1.4.2 Products/Solutions/Services offered
14.1.4.3 Recent developments
14.1.4.3.1 Product launches/enhancements
14.1.4.3.2 Deals
14.1.4.3.3 Other developments
14.1.4.4 Analyst's view
14.1.4.4.1 Right to win
14.1.4.4.2 Strategic choices
14.1.4.4.3 Weaknesses and competitive threats
14.1.5 ERICSSON
14.1.5.1 Business overview
14.1.5.2 Products/Solutions/Services offered
14.1.5.3 Recent developments
14.1.5.3.1 Product launches/enhancements
14.1.5.3.2 Deals
14.1.5.3.3 Other developments
14.1.5.4 Analyst's view
14.1.5.4.1 Right to win
14.1.5.4.2 Strategic choices
14.1.5.4.3 Weaknesses and competitive threats
14.1.6 ARISTA NETWORKS
14.1.6.1 Business overview
14.1.6.2 Products/Solutions/Services offered
14.1.6.3 Recent developments
14.1.6.3.1 Product launches/enhancements
14.1.6.3.2 Deals
14.1.7 NVIDIA
14.1.7.1 Business overview
14.1.7.2 Products/Solutions/Services offered
14.1.7.3 Recent developments
14.1.7.3.1 Product launches/enhancements
14.1.7.3.2 Deals
14.1.8 DYNATRACE
14.1.8.1 Business overview
14.1.8.2 Products/Solutions/Services offered
14.1.9 IBM
14.1.9.1 Business overview
14.1.9.2 Products/Solutions/Services offered
14.1.9.3 Recent developments
14.1.9.3.1 Product launches/enhancements
14.1.9.3.2 Deals
14.1.10 INFOBLOX
14.1.10.1 Business overview
14.1.10.2 Products/Solutions/Services offered
14.1.10.3 Recent developments
14.1.10.3.1 Product launches/enhancements
14.1.10.3.2 Deals
14.2 OTHER PLAYERS
14.2.1 BROADCOM
14.2.2 FUJITSU
14.2.3 DELL TECHNOLOGIES
14.2.4 NEC
14.2.5 A10 NETWORKS
14.2.6 SCIENCELOGIC
14.2.7 CIENA
14.2.8 EXTREME NETWORKS
14.2.9 EFFICIENTIP
14.2.10 RIVERBED
14.2.11 NETSCOUT
14.2.12 SOLARWINDS
14.2.13 NETBRAIN
14.2.14 MANAGEENGINE
14.2.15 VIAVI
14.3 SMES/STARTUPS
14.3.1 FORWARD NETWORKS
14.3.2 BLUECAT NETWORKS
14.3.3 VERSA NETWORKS
14.3.4 SELECTOR
14.3.5 AVIZ NETWORKS
14.3.6 NILE
14.3.7 NETBOX LABS
14.3.8 CELONA
14.3.9 SHABODI
14.3.10 A5G NETWORKS
15 RESEARCH METHODOLOGY
15.1 RESEARCH DATA
15.1.1 SECONDARY DATA
15.1.2 PRIMARY DATA
15.1.2.1 Primary interviews with experts
15.1.2.2 Breakdown of primary profiles
15.1.2.3 Key data from primary sources
15.1.2.4 Key industry insights
15.2 MARKET BREAKUP AND DATA TRIANGULATION
15.3 MARKET SIZE ESTIMATION
15.3.1 TOP-DOWN APPROACH
15.3.2 BOTTOM-UP APPROACH
15.4 MARKET FORECAST
15.4.1 FACTOR ANALYSIS
15.5 RESEARCH ASSUMPTIONS
15.6 RESEARCH LIMITATIONS
16 APPENDIX
16.1 DISCUSSION GUIDE
16.2 KNOWLEDGESTORE: THE SUBSCRIPTION PORTAL
16.3 CUSTOMIZATION OPTIONS
16.4 RELATED REPORTS
16.5 AUTHOR DETAILS
LIST OF TABLES [419]
LIST OF FIGURES [55]
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