HPE announced the successful completion of its previously announced acquisition of Juniper Networks, Inc., a provider of AI-native networks.
The combination positions HPE to capture the growing AI and hybrid cloud market opportunity by creating an industry-leading cloud-native and AI-driven IT portfolio, including a full, modern networking stack.
The transaction doubles the size of HPE’s networking business and provides customers with a comprehensive portfolio of networking solutions. It also accelerates the company’s portfolio mix shift to higher-margin, higher-growth areas and positions the company for long-term profitable revenue growth.
“Today begins a new era for HPE – we are now at the epicenter of the transformation of IT, where AI and networking are converging,” said Antonio Neri, president and CEO of HPE. “In addition to positioning HPE to offer our customers a modern network architecture alternative and an even more differentiated and complete portfolio across hybrid cloud, AI, and networking, this combination accelerates our profitable growth strategy as we deepen our customer relevance and expand our total addressable market into attractive adjacent areas. We look forward to welcoming the Juniper team to HPE.”
“HPE and Juniper have a unique opportunity to disrupt the networking industry at the most important and relevant time,” said Rami Rahim, former CEO of Juniper Networks, who will now lead the combined HPE Networking business. “Together, we’ll be able to provide customers and partners with a secure network that is purpose-built with AI and for AI.”
Strategic & Financial Benefits:
- Transformative for HPE’s strategic evolution. The acquisition accelerates HPE’s strategic vision with a full networking IP stack: from silicon, to hardware, to the operating system, to security, to software and services, with a cloud-native and AI-driven approach. This integration will accelerate customers’ deployment and adoption of both hybrid cloud and AI.
- Bolsters HPE’s position as a networking leader. The acquisition doubles the size of HPE’s networking business, substantially increasing its scope and total addressable market. The combined company will reach large adjacent markets, including data center, firewalls, and routers, bridging the global strength of HPE in enterprise security-first networking and SASE security with Juniper’s position in data center, service provider, and AI-native solutions.
- Provides customers with a leading AI-native foundation for their end-to-end networking needs. The transaction builds on the combined capabilities of HPE and Juniper to provide customers of all sizes with the modern networking architecture to manage and simplify increasingly complex connectivity needs – particularly those driven by data-intensive, hybrid AI workloads. Greater research and development scale will enable faster innovation across networking silicon, systems, and software.
- Gives customers access to HPE’s full portfolio offering across networking, hybrid cloud, and AI. Networking customers will benefit from HPE innovation across its full portfolio offering – including hybrid cloud, storage, compute, and software – to accelerate and simplify their AI transformations.
- Capitalizes on HPE’s go-to-market scale. The transaction creates revenue growth opportunities, as Juniper offerings benefit from HPE’s large, global go-to-market model and team. The combined company will offer secure, AI-native solutions with the ability to collect, analyze, and act on insightful network data across a broader installed base.
- Attractive financial profile expected to deliver strong value for HPE shareholders. The acquisition of Juniper’s high-margin business is expected to be accretive in the near- and long-term for the combined company. The transaction will be accretive to non-GAAP EPS in year 1, post close, with the combined networking business contributing more than 50% of total company operating income.
The acquisition was originally announced on January 9, 2024, and was approved by Juniper shareholders on April 2, 2024.
The Latest
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...
Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...
Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...
As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...
For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...
I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...
Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...