Why Cloudera Leads the 2026 IDC Asia/Pacific MarketScape for Sovereign AI

Cloudera has been named a Leader in the 2026 IDC MarketScape for Asia/Pacific Sovereign AI Platforms.

The recognition comes as enterprises are thinking more broadly about what sovereign AI requires. Data residency remains important, but control also extends to the AI workloads using that data, the models behind them, and the infrastructure they run on. For organizations moving AI into production, that means having greater choice over how and where their AI operates.

IDC highlighted three differentiators in Cloudera’s approach: flexible deployment, integrated governance, and open architecture. Together, they support Cloudera’s AI Anywhere approach — bringing AI to enterprise data across cloud, on-premises, edge, and air-gapped environments while maintaining governance and control.

Flexible deployment: Run AI wherever your data lives

For large enterprises, AI will not run in one environment.

Some workloads will run in the public clouds. Others will need to stay on-premises, in a private cloud, at the edge, or in air-gapped environments because of security, regulatory, latency, or data residency requirements.

That makes deployment flexibility a core part of sovereign AI. Cloudera supports data and AI workloads across these environments, allowing organizations to bring compute to governed data instead of moving sensitive data to an external AI service.

Bank Negara Indonesia (BNI) is already putting this model into practice. BNI runs Cloudera AI Inference, built on NVIDIA NIM microservices, in its own data centers, enabling BNI to support AI use cases while maintaining greater control over sensitive data and AI workloads.

This is where Cloudera AI Inference on premises becomes a key differentiator. It gives organizations the ability to serve generative AI models within their own infrastructure, giving teams greater control over sensitive data, model deployment, and infrastructure while still supporting production AI workloads.

For enterprise teams, especially in regulated industries, the result is a more practical path to sovereign AI: keeping sensitive workloads where they need to run, while preserving flexibility across the broader AI estate.

Integrated governance: Extend control into the AI lifecycle

As AI moves into production, enterprises need visibility into the data behind it — who can access it, where it came from, how it was transformed, and how it is used across analytics and AI workflows.

Axis Bank shows what this looks like in practice. The bank runs Cloudera across on-premises and AWS environments and uses Cloudera AI for machine learning and analytics, supporting GenAI and Agentic AI initiatives on top of a governed hybrid data foundation.

Cloudera brings governance, lineage, and observability to a single platform with Cloudera Data Lineage and Cloudera Observability. That foundation carries into the broader Cloudera AI stack, spanning AI development, agentic applications, and model inference.

For sovereign AI, governance needs to carry across the full lifecycle, from the enterprise data itself to the AI systems that rely on it.

Open architecture: Keep control without creating lock-in

Sovereignty is also about technology choice. It means maintaining the freedom to choose how data is stored, where workloads run, which models and infrastructure teams use, and how those choices evolve over time, without having to redesign the entire AI platform around a single vendor.

An organization can meet data residency requirements and still become dependent on a single cloud, model provider, hardware platform, or proprietary data architecture.

Cloudera’s open architecture is designed to give enterprises greater flexibility across all those layers. That includes support for open technologies such as Apache Iceberg, a broad ecosystem of AI models and infrastructure, and deployment across multiple environments.

For AI teams, this means they can choose the models, infrastructure, and deployment patterns that make sense for each use case without rebuilding their data and governance foundation each time.

Building the Operating Model for Sovereign AI

Sovereign AI requires more than a deployment choice. It requires an operating model that keeps data, infrastructure, and governance aligned across environments.

Cloudera brings those components together through flexible deployment, integrated governance, and an open architecture, helping enterprises build and run AI wherever their data needs to stay.

Explore how Cloudera supports sovereign AI across any environment.