Plotly announces Dash Enterprise 6, bringing native Kubernetes deployment, and secure-by-default architecture to production Python data applications at scale.
MONTREAL, QUEBEC, CANADA, February 26, 2026 /EINPresswire.com/ — Plotly has announced the release of Dash Enterprise 6, a Kubernetes-native platform for data science teams that need production-grade data app deployment, security, and operations at scale.
Dash Enterprise 6 runs natively on Kubernetes, integrating with the infrastructure organizations already use, including container registries, observability tools, and cloud identity systems.
Automated containerization and dependency management mean teams can ship production-ready applications in minutes. High-availability architecture and self-healing user workloads keep applications online through hardware failures and platform updates alike.
Security is built into the architecture rather than layered on top, ensuring applications only reach the databases and secrets they need. Role-based access control and centralized governance give IT teams consistent policy enforcement across all deployed apps. Dash Enterprise 6 supports air-gapped deployments for organizations with strict regulatory requirements, alongside SSO, SCIM provisioning, and enterprise cloud storage integrations.
Dash Enterprise 6 also includes Plotly Studio, a data analytics platform to AI-generate fully functional Dash apps from natural language and data, offering a non-technical path for producing production-ready applications that run seamlessly on Dash Enterprise 6.
Organizations at major financial institutions, utilities, and data providers are currently running Dash Enterprise to deliver mission-critical applications, from field operations to financial analytics.
With over five million monthly downloads, Dash has become the standard Python framework for production data applications. Dash Enterprise 6 is the infrastructure organizations need to run those applications at the scale and reliability the enterprise demands.
Priyaanka Arora
Plotly
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