Network monitoring market growth to reach $7.3 billion by 2033

The doubling of this market reflects a critical shift from simple device tracking to full-stack observability as streaming architectures become more distributed. For streaming providers, maintaining uptime now requires AI-driven tools capable of identifying latency and packet loss across multi-cloud and edge environments simultaneously. As companies like Cisco Unified Edge AI platform and Datadog compete for dominance, the integration of security analytics with performance monitoring will become the standard for protecting high-bandwidth video traffic. Watch for a consolidation of specialized monitoring tools into unified platforms as large enterprises seek to reduce the high deployment costs associated with heterogeneous network stacks.

The competitive landscape for network monitoring is intensifying as major vendors race to integrate AI-driven observability into their platforms. In June 2026, Ericsson launched its AI in RAN commercial software subscription claiming up to 20% higher downlink throughput across more than 15 live deployments, signaling that AI-powered network intelligence is moving from pilot to production across carrier-grade infrastructure. Verizon disclosed that its 60,000-site vRAN is now applying agentic AI to planned configuration changes, service assurance, and network optimization, while publicly calling for industry-wide interoperability standards for agentic systems. These deployments underscore the same complexity pressures driving the network monitoring market toward unified, AI-native platforms capable of spanning physical, virtual, and edge environments simultaneously.

Nokia is building a parallel ecosystem around its Autonomous Network Fabric, positioning it as an operating system for telco radio, core, transport, and service domains. Nokia announced partnerships with AWS and Databricks to build the data, cloud, and control layers for autonomous networks, with the Databricks collaboration addressing unified data platforms and AWS handling AI cloud integration. Nokia claims its autonomous networks portfolio is already delivering automation rates higher than 90 percent, service delivery times of four hours or less, and up to 85 percent reduction in slice rollout time. Ericsson has countered with its own agentic service experience layer spanning customer journeys, revenue management, and network operations, running on Amazon Bedrock with more than 20 cloud-native AI applications positioned across OSS and BSS functions. The vendor rivalry mirrors the consolidation dynamics expected in the broader network monitoring market as platforms converge on similar AI-first architectures.

The technical divergence between Ericsson and Nokia on AI-RAN illustrates how monitoring and observability requirements are fragmenting even within the same vendor category. Ericsson and Nokia are diverging on AI-RAN architecture, with Nokia building its entire RAN strategy on Nvidia GPUs and CUDA while Ericsson retains its custom silicon approach, creating distinct observability challenges for operators running multi-vendor environments. The TM Forum's Autonomous Networks L4/5 roadmap and the 3GPP 6G standardization process will need to incorporate agentic AI interoperability as a core requirement, according to IEEE ComSoc analysis. For streaming infrastructure operators, this fragmentation reinforces the need for vendor-neutral monitoring tools like , , and NETSCOUT that can provide consistent visibility across heterogeneous network stacks without requiring platform-specific instrumentation.