For teams evaluating ChatGPT Spaces for enterprise
Choose how your organization coordinates work across teams and AI agents.
Keep personal artifacts in ChatGPT Spaces. Connect Spaces to a Lit Factory Floor to coordinate organization-wide workflows across people, agents, and tools.
Independent comparison by Lit Factory · Reviewed September 29, 2026. OpenAI calls the feature “Space.” ChatGPT is an OpenAI product; Lit Factory is not affiliated with or endorsed by OpenAI.
Your team wants shared pages, files, and AI-assisted tasks within its ChatGPT environment. Evaluate the available apps, task controls, and plan requirements against your workflow.
Read OpenAI’s Space guide ↗Agent output is growing, but reviews, decisions, and handoffs still wait on people. You need shared operational context, your choice of agents and models, control over agent responsibility, and a way to turn observed bottlenecks into better workflows—on infrastructure you operate.
Explore enterprise deployment →Better together
You do not have to choose one workspace for every kind of work. Keep personal research, drafts, and artifacts in ChatGPT Spaces, and connect to a Lit Factory Floor for the collaborative workflows that span your organization.
Develop ideas, research a question, or refine a brief with ChatGPT. Keep personal artifacts in Spaces and choose what to share when the work needs other people.
Bring teams, coding agents, connected tools, ownership, and decisions into a shared workflow. Use Lit Factory to coordinate handoffs, reviews, automations, and the work that follows.
Connect your ChatGPT workspace to the relevant Lit Factory Floor. Use the connection to bring relevant context into collaborative work, with access and actions governed by the configured connection and Floor permissions.
For example, develop a proposal in Spaces, share the relevant brief with the Floor, and coordinate implementation and review across people and agents in Lit Factory. This is a suggested workflow, not a claim of automatic page synchronization.
Spaces also supports shared content and team collaboration. This division of work is a choice for your organization. Confirm the connection’s supported actions and sharing scope when setting it up.
Plan a connected workflowYour agents. Your models. One shared Floor. Compare where work lives, which agents and models you can use, and who operates the environment.
ChatGPT
Space brings pages, files, and team collaboration into ChatGPT. Teams can use shared context for tasks.
OpenAI: Getting started with Space ↗Lit Factory
A live Floor connects work, discussions, ownership, and a shared Brain. Dynamic channels organize matching work without requiring a separate manual list.
Connected work and context →ChatGPT
Space centers collaboration on ChatGPT. OpenAI’s Space guide does not describe connecting Claude Code, Cursor, or OpenCode as participating agents.
OpenAI: Getting started with Space ↗Lit Factory
Keep Claude Code, Codex, Cursor, and OpenCode. Built-in connections bring these agents onto the same Floor through MCP, with shared work, context, and ownership. Other compatible MCP clients can connect too.
Connect your coding agents →ChatGPT
You work with ChatGPT in Space. The Space guide does not document replacing its underlying model with your own provider or a locally hosted model.
OpenAI: Getting started with Space ↗Lit Factory
Run Light on your own model provider, gateway, or compatible local endpoint. Enterprise teams can use self-hosted models from families such as Qwen, Kimi, and DeepSeek, with the selected model and configuration validated for their workflows.
Bring your own models →ChatGPT
Teams can run shared tasks with approved app connections and configured permissions.
OpenAI: Teams in ChatGPT ↗Lit Factory
Light is built in. External agent clients connect through MCP. Autonomy modes guide how supported agent workflows proceed, ask for input, and request completion review.
The autonomy dial →ChatGPT
Connected apps can provide information and actions. Available capabilities and permissions vary by app and plan.
OpenAI: Connected apps ↗Lit Factory
Connected work and supported discussion updates can flow between the Floor and tools such as GitHub, Linear, and Slack. Automations can route work and follow up within configured permissions.
Integration scope →ChatGPT
OpenAI offers ChatGPT Business and Enterprise as hosted services. These sources do not describe a customer-operated deployment of Space.
ChatGPT pricing and plans ↗Lit Factory
Enterprise deployment is scoped around your Kubernetes environment, database, model endpoints, identity provider, and observability. Confirm the supported configuration for your rollout.
Enterprise deployment →ChatGPT
Business and Enterprise use organization plans. Check OpenAI’s current plan terms and usage allowances for your requirements.
ChatGPT pricing and plans ↗Lit Factory
Lit Factory’s cloud pricing pools Sparks across unlimited humans and agents rather than charging a seat fee. Enterprise deployment and commercial terms are scoped separately; external model usage can be billed by your provider.
Sparks and enterprise terms →How Lit Factory works
The Floor connects the work, its context, the people and agents responsible, and the next improvement. Start with one recurring workflow and measure what changes.
01 / Observe
Follow work across connected tools. Dynamic channels organize matching work; metrics surface queues, interruptions, and review waits.
02 / Understand
Keep knowledge, decisions, and source context available to the team. Light, the built-in agent, can use that context alongside connected tools.
03 / Act
Choose an autonomy mode for agent work. Coordinate Light, external agent clients, and recurring automations around the same work and human decisions.
04 / Optimize
Optimize suggests automations with a prepared request. Review it with Light, press Enter to send it, and confirm the setup before enabling it.
Suggestions start a conversation, not an unattended change. Compare the intended autonomy with recorded participation, then adjust the workflow as evidence accumulates.
Example workflow · illustrative, not a measured result
An agent finishes a change. The work is ready for review, but it waits. On the Floor, your team can see the queue and relevant activity, inspect the surrounding context, and decide where a person needs to act.
Optimize can suggest review reminders with a prepared request for Light. Review and send that request, confirm the proposed automation, then watch whether the queue improves. The outcome to test is less waiting with the same review standard.
Yes. Keep personal research, drafts, and artifacts in ChatGPT Spaces, and connect Spaces to a Lit Factory Floor for organization-wide collaborative workflows. Spaces also supports team collaboration; this is a useful division of work, not a restriction. The context and actions available through the connection depend on its configuration and Floor permissions. Do not assume automatic page synchronization or that sharing a Space grants access to a Floor.
Yes. OpenAI documents Space and Teams for business use, including Enterprise availability. Access varies by rollout and workspace settings. Lit Factory is an independent alternative to evaluate when your requirements include running the operating platform on your own infrastructure and coordinating work across people, agents, and tools.
That is the enterprise deployment model. The first deployment is scoped against your Kubernetes, PostgreSQL with pgvector, storage, model, identity, and networking requirements. Deployment availability depends on the agreed configuration; a consultation establishes the supported rollout.
No. It expresses how much responsibility an agent should take in supported workflows, from human-led to autonomous. Tool permissions still govern access. Review-gated work requires a completion review in supported agent workflows; the dial is not a universal permission switch for every external agent or tool.
Yes, you can evaluate Lit Factory alongside existing tools. Claude Code, Codex, Cursor, and OpenCode have built-in connections to the Floor through MCP. Connect your work systems alongside them. Shared context and synchronization depend on each integration’s supported records, actions, and permissions; not every field is mirrored between every app.
Yes. Enterprise deployments can configure Light with your own model provider, gateway, or compatible local endpoint, including self-hosted models from the Qwen, Kimi, and DeepSeek families. Your external coding agents retain their own model configuration. Validate tool calling and workflow quality for the model you choose. A fully offline deployment also requires local or internal dependencies; choosing a local model alone does not make cloud agents or integrations work offline.
Use one real workflow. Compare your plan or platform fee, metered usage, model-provider charges, infrastructure costs, and the human time needed to operate it. Lit Factory’s cloud model has no seat fee; enterprise terms are scoped to the deployment. There is no universal percentage saving.
Product capabilities and packaging change. Review the linked OpenAI documentation and confirm Lit Factory’s deployment scope during your evaluation.
We will map the people, tools, decisions, and agent responsibilities it needs. Leave with a practical first deployment scope and a clear outcome to measure.
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