Arista Networks vs IBM Cloud
Side-by-side comparison of AI visibility scores, market position, and capabilities
Santa Clara cloud networking (NYSE: ANET) at $7.0B FY2024 revenue (+20%); AI networking +50%, 400G/800G GPU cluster switches for Microsoft/Meta/Google competing with Cisco and NVIDIA InfiniBand for AI data center fabric.
About
Arista Networks, Inc. is a Santa Clara, California-based cloud networking company — publicly traded on the New York Stock Exchange (NYSE: ANET) as an S&P 500 Information Technology component with a market capitalization of approximately $120 billion — designing and selling programmable Ethernet switches, routers, and network management software for cloud data centers, artificial intelligence compute clusters, and enterprise campus networks through approximately 4,400 employees worldwide. In fiscal year 2024, Arista reported revenue of $7.0 billion (+20% year-over-year), with AI networking revenue growing more than 50% as hyperscale customers (Microsoft, Meta, Google) deployed Arista's 400G and 800G Ethernet switches as the network fabric interconnecting tens of thousands of NVIDIA H100 and H200 GPUs in AI training clusters. CEO Jayshree Ullal has led Arista since 2008, executing the strategy of building the world's most advanced Ethernet switching operating system (EOS, Extensible Operating System) and using EOS's programmability and reliability advantages to win the hyperscale cloud data center market from Cisco — a share gain that created Arista's growth from a startup to a $7B+ revenue company in a decade. Arista's EOS software runs across the entire product family (campus, data center, AI cluster switches) with a single code base and management plane, enabling network operators to write Python scripts and custom automation for every switch in the network from one operating system.
NYSE: IBM | $62.8B revenue FY2024; watsonx AI platform surpassed $2B in deal signings; hybrid cloud via Red Hat OpenShift; quantum computing leader; focused on regulated enterprise verticals
About
IBM Cloud is the enterprise cloud and AI platform from IBM, the technology company founded in 1911 and headquartered in Armonk, New York. IBM Cloud was built to serve the distinctive needs of regulated industries and large enterprises — financial services, healthcare, government, and telecommunications — that require hybrid cloud architectures, compliance certifications, and enterprise-grade security that public cloud hyperscalers often struggle to provide at equivalent depth. Its core technology integrates bare metal and virtual compute, managed Kubernetes via Red Hat OpenShift, and the watsonx AI platform for enterprise LLM deployment and AI lifecycle management.\n\nIBM Cloud's product portfolio encompasses infrastructure-as-a-service, platform-as-a-service, and a growing AI services layer anchored by watsonx — a suite of enterprise AI tools covering foundation model deployment, data governance, and AI model monitoring. The platform integrates tightly with Red Hat OpenShift, giving enterprises a consistent Kubernetes runtime across on-premises data centers, multiple clouds, and IBM Cloud regions. IBM also operates quantum computing services through IBM Quantum, making its cloud the only major platform offering commercial access to quantum hardware alongside classical compute. With more than 100 data centers globally, IBM Cloud serves enterprises requiring geographic data residency and low-latency access to cloud services.\n\nIBM Cloud is central to IBM's strategic transformation from a legacy hardware and services company into a hybrid cloud and AI platform business, a shift that has driven substantial product investment since the $34 billion acquisition of Red Hat in 2019. IBM's enterprise relationships, compliance certifications for regulated industries, and watsonx AI platform create a differentiated stack for organizations that cannot move workloads to public hyperscalers without significant architectural complexity. As enterprises accelerate AI adoption and hybrid cloud standardization, IBM Cloud's combination of regulatory credibility, OpenShift integration, and enterprise AI tooling addresses requirements that generic hyperscaler platforms are less equipped to meet.
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