# Platform versus Workspace GLBNXT offers two distinct products that serve different audiences and purposes within an organisation. While both are built on the same commitment to European data sovereignty and enterprise-grade security, they are designed for fundamentally different use cases and user profiles. Understanding the distinction helps teams identify which product they are working with, where to find the right documentation, and how the two products can complement each other within the same organisation. ### GLBNXT Platform GLBNXT Platform is built for development teams. It provides the complete AI infrastructure stack that engineers, data scientists, and architects need to design, build, and deploy AI-powered applications and services. Platform users are builders. They work with model endpoints, vector databases, workflow automation, APIs, and application components to create AI solutions for their organisation or for clients. The platform gives them a managed, sovereign environment where all infrastructure concerns are handled by GLBNXT, and all building capabilities are available from day one. **Typical Platform users include:** * ML Engineers and Data Scientists building inference pipelines and AI-powered services * Data Engineers designing RAG systems and managing data pipelines * DevOps and IT Architects configuring environments, access controls, and security posture * Product Leaders and CTOs overseeing AI solution delivery and platform governance ### GLBNXT Workspace GLBNXT Workspace is built for knowledge workers and business teams. It provides a governed, compliant AI environment for employees who use AI to do their work, without needing to understand or interact with the infrastructure behind it. Workspace users are consumers of AI capability. They work with AI assistants, document analysis tools, knowledge search, and productivity features within a secure, policy-controlled environment. Workspace abstracts away all technical complexity so that business users can benefit from AI without requiring any technical background. **Typical Workspace users include:** * Business analysts and consultants working with documents and data * Legal, compliance, and finance teams using AI for research and review * Operations and HR teams automating routine knowledge work * Any employee using AI tools within a governed enterprise environment ### Choosing the Right Product The two products are not mutually exclusive. Many organisations use both: development teams build AI solutions and services on Platform, while business teams consume AI capabilities through Workspace. In this model, Platform is where solutions are created and Workspace is where they are used. | | GLBNXT Platform | GLBNXT Workspace | | ---------------------------- | ------------------------------------------- | -------------------------------------------------- | | **Primary audience** | Development teams | Knowledge workers | | **Primary activity** | Building AI solutions | Using AI capabilities | | **Technical depth required** | Medium to high | None | | **Key capabilities** | Model serving, RAG, agents, workflows, APIs | AI assistants, document analysis, knowledge search | | **Governance model** | Infrastructure and deployment controls | Usage policies and data access controls | | **Deployment** | Managed infrastructure environment | Managed workspace environment | ### Where to Go From Here If you are a developer, data scientist, or architect building AI solutions, you are in the right place. Continue with the Getting Started section to begin working with GLBNXT Platform. If you are a business user or knowledge worker looking to use AI within a governed environment, visit the GLBNXT Workspace documentation. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.glbnxt.com/platform/overview/platform-versus-workspace.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.