Guide
What is a multi-model AI workspace?
A multi-model AI workspace is a single application that lets you use several large language models, from different providers, through one interface. You can switch between models without changing tools or losing your conversation context. Instead of juggling separate accounts and tabs for each provider, the workspace gives you one place to prompt, compare, and govern every model.
Why teams use a multi-model workspace
No single model is best at everything. A coding task, a long-context summary, and a fast customer-support reply may each be served better by a different model. A multi-model workspace lets teams match the right model to each job while keeping one consistent experience.
- Avoid lock-in to a single provider, so you can adopt new models as they ship.
- Pick the best model per task instead of compromising on one.
- Apply consistent governance and security across every model and user.
- Keep shared context, so conversations and history carry across model switches.
What to look for
Look for breadth of supported providers, including reasoning and multimodal models, plus the controls an organization needs to deploy AI responsibly. Single sign-on, access controls, and auditability matter as much as raw model quality.
Practical features to evaluate include per-organization model access, guardrails for sensitive data, web search for up-to-date answers, and a smooth way to compare model outputs side by side.
How ChatLite implements it
ChatLite routes to OpenAI, Anthropic Claude, Google Gemini, Mistral, DeepSeek, and xAI Grok, including reasoning and multimodal models, from one governed workspace. It's built so teams can standardize on a single tool while keeping access to the full range of leading models.
Governance is built in: SSO, guardrails, web search, and per-organization model access let administrators control exactly which models each team can use. You can explore the full feature set on the features page and review controls on the security page.