Cloud repatriation is back on the agenda

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Anthropic’s Claude models in Microsoft Foundry — hosted on Microsoft Azure and running on NVIDIA GB300 Blackwell Ultra GPUs — are now generally available, giving Azure-native enterprises a powerful new way to build autonomous and domain-specific AI agents. As agentic AI continues to drive enterprise innovation and becomes more autonomous, organizations need access to computing […]

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For years, the enterprise narrative focused on moving to the public cloud for flexibility and leaving behind old infrastructure. While the public cloud remains a powerful platform for burst capacity, global reach, and modern application development, leaders now evaluate where each workload can achieve the best financial performance, operational efficiency, and risk. Cloud repatriation is back on the CIO’s agenda. Cloud repatriation does not always mean dragging workloads back into a company-owned data center. In many cases, enterprises are moving applications and data from hyperscale public cloud platforms into colocation environments, hosted private clouds, or MSP-operated infrastructure. The common thread is not nostalgia for on-premises IT. It is the desire for a more suitable workload placement. Enterprises are deciding that some systems belong in public cloud while others are better served in environments with more predictable economics, tighter control, and fewer architectural co

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For the past several years, the default assumption in enterprise IT was that AI would follow the same path as many other workloads and settle into the public cloud. That assumption seemed reasonable on the surface. The hyperscalers had the infrastructure, GPU capacity, managed services, and developer ecosystems. If you wanted to move fast, public cloud AI looked like the obvious answer. That logic is now being challenged by reality. As enterprises move from AI experiments to AI in production, they increasingly find that the public cloud is a convenient place to start but not the most practical place to stay. Enterprises are wondering if they can afford to base their long-term AI strategies on cost models they do not control, risks they cannot fully contain, and architectures that are optimized for provider scale rather than enterprise economics. This is why private cloud AI is becoming more popular. Enterprises are not moving on-premises because it’s a fashionable choice. They are movi

Claude Tag is Anthropic’s latest attempt at getting Claude out of your DMs and into your team’s Slack channels. AI assistants are increasingly showing up in the workplace to perform research, coding, writing, and analysis, but the results of those interactions typically remains tied to individual conversations rather than being shared across projects and teams. That limitation is what Anthropic is addressing with Claude Tag, a new Slack channel-based experience for its Enterprise and Team customers, designed to give them a shared AI collaborator that retains context across conversations and participates in work with multiple employees. Tag will replace Anthropic’s previous attempt at this, Claude in Slack, would only interact with one person (although it’s responses were visible to all in a channel) and its context was limited to the last 20 messages in a channel. Claude Tag has a much larger context and can be asked to complete tasks on its own, returning with results and a log of how