At EVOLVE26 in Singapore, Cloudera made a strategic statement that the next phase of enterprise AI will be defined by where AI runs, what data it can access, and how securely it operates at scale. Cloudera announced the Anywhere Cloud, positioned as the foundation of its “cloud anywhere, data anywhere, AI anywhere” strategy across public cloud, private infrastructure, sovereign environments, and the edge. The announcement was reinforced by a broader focus on agentic AI, private inference, open lakehouse architecture, governance, and AI economics.

The Future Of AI Architecture Is Scoped For The Enterprise

Rather than presenting these as a collection of new capabilities, Cloudera announced them as a broad, connected enterprise AI architecture. Anywhere Cloud is designed to give enterprises an operating model allowing workloads to run wherever security, compliance, performance, data sovereignty, and cost requirements dictate. For organizations managing increasingly fragmented hybrid and multicloud environments, this challenges the idea that AI modernization requires moving data and workloads into a single cloud.

EVOLVE26 Takeaways

I came away from EVOLVE26 with four important messages about the market:

  • The future of AI deployment is flexible. Cloudera Anywhere Cloud makes the case for flexible AI deployment. The platform is designed to provide a consistent cloud experience across public clouds, private data centers, sovereign environments, and the edge. Its core proposition is that enterprises should not have to move sensitive data to accommodate where an AI workload runs. Workloads can be located based on security, regulatory, performance, latency, and cost considerations.
  • Agentic AI is moving from experimentation to enterprise operations. Cloudera is shifting the conversation toward agents that can reason over enterprise data, orchestrate workflows, and take actions. Capabilities including Cloudera AI Agent Studio and agentic copilots are designed to enable these workflows in controlled enterprise environments. The company also introduced its “agentic system of record” vision for capturing and governing AI-driven decisions and actions.
  • Private and sovereign AI are becoming strategic requirements. Cloudera highlighted its AI Inference service for private, on-premises, and controlled environments. Those work alongside its AI gateway, designed to govern interactions across public and private models. Now, organizations can keep sensitive data closer to where it is governed while applying policies, monitoring usage, and managing AI costs. Axis Bank highlighted the relevance of this approach for regulated environments where compliance, data movement and TCO all matter.
  • The open lakehouse remains the core foundation for AI. Cloudera reinforced that successful AI depends on trusted, accessible, and governed data. Its open data lakehouse strategy, supported by technologies such as Apache Iceberg and Trino, is designed to enable organizations to access, share, and govern distributed data without forcing everything into a single repository. This becomes critical for agentic AI, where reliable decisions depend on current data, metadata, lineage, and business context.

What Enterprises Should Prioritize

The broader EVOLVE26 takeaway is that enterprises need the freedom to run AI anywhere without giving up control over their data, infrastructure, or economics. Organizations should prioritize architectures that allow AI workloads to run where they make the most sense while maintaining consistent security, governance, and control. Ultimately, enterprises should look beyond the presence of specific AI capabilities and evaluate whether platforms can deliver trusted AI anywhere, with the flexibility, control, interoperability, and economics required to operate at enterprise scale. Four recommendations for enterprises are to:

  • Evaluate AI architectures based on the ability to site workloads without centralizing all data.
  • Start with an architecture where production agents have trusted data, context, permissions, governance, and an auditable trail built in.
  • Treat sovereignty as an architectural decision rather than a compliance checkbox.
  • Strengthen your data foundation first, because agents are only as reliable as the data and governance behind them.

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