Resources
Resources
Learn how multi-model AI, AI teammates and orchestration work — and how ModelCrew approaches them.
Learn
Core concepts and practical guides to help you get up to speed.
Guide
What Is a Multi-Model AI Workspace?
Understand the multi-model workspace and when it makes sense.
Guide
AI Models vs AI Agents vs AI Teammates
Understand the differences and why roles are independent of models.
Guide
ChatGPT vs OpenAI API: What's the Difference?
The key differences between consumer chat and API-powered apps.
Guide
How Do AI Agents Keep Consistent Roles?
What persists when your teammate switches models.
How ModelCrew Works
Go deeper into roles, model choice, orchestration and human review.
Guide
Why Make the AI Model Behind an Agent Interchangeable?
Keep the teammate. Change the AI model behind it.
Guide
What Is AI Agent Orchestration?
How a Project Lead can delegate and synthesize work.
Use case
How to Build a Team of AI Agents
Define roles, collaboration rules and approval points.
Guide
What Is BYOK AI? Bring Your Own API Key Explained
How direct API connections and provider billing work.
Comparisons
Explore the practical tradeoffs before choosing your AI workflow.
Comparison
ModelCrew vs Using ChatGPT, Claude and Gemini Separately
An honest comparison of a multi-model workspace (ModelCrew) versus using ChatGPT, Claude, and Gemini as separate apps: context, model switching, persistent roles, native features, and setup.
Comparison
Multi-Model vs Single-Provider AI Workspaces
Should your AI workspace support many providers or commit to one? Flexibility, model choice, and redundancy versus simplicity, native integration, and one bill — neither is universally superior.
Comparison
A Multi-Model Workspace vs Copying Between AI Tools
A side-by-side comparison of doing multi-model AI work manually (copying between ChatGPT, Claude, and Gemini) versus using a shared multi-model workspace where persistent teammates, context, and delegation live in one place.
Insights
Ideas and perspectives on the evolving way people work with AI.
Insight
Stop Looking for One “Best” AI Model
The useful question is no longer which AI model wins everything — it's which model fits each task. An evidence-based case for model specialization: cost, latency, context, tools, reasoning, writing, coding, and provider risk.
Insight
From AI Chatbot to AI Team
The shift from one user talking to one AI assistant toward one user working with multiple role-defined AI collaborators — what changes, what improves, and what honestly doesn't.
Insight
Why Model Independence May Matter More Than Model Loyalty
Models improve fast and leadership changes. Why binding your AI workflows permanently to one provider is riskier than it looks — and what practical, honest portability actually means.
Ready to see it in action?
Explore AI teammates, model choice, and collaboration in one workspace.