Glue vs Cortex

By Arjun Mehta, Principal Engineer at Glue

GitHub Copilot is the most widely adopted AI coding tool in the world. If you are evaluating AI codebase analysis tools and Copilot appears on your list, you are comparing two tools that use AI for fundamentally different purposes. Copilot writes code. Glue reads code. Copilot helps engineers build faster. Glue helps the entire team understand what has been built.

I have used Copilot daily for over a year. It is a genuine productivity multiplier for writing code. But it does not solve the problem that most product teams face: understanding the codebase without reading it.

GitHub Copilot launched in 2021 and has grown to over 1.8 million paid subscribers, making it the most widely adopted AI developer tool in the world. It excels at one thing: helping engineers write code faster. Autocomplete suggestions, function generation, test scaffolding, and in-IDE chat make it a daily productivity companion for developers across languages and frameworks.

Glue operates on the other side of the AI-for-software divide. While Copilot helps engineers create code, Glue helps the entire team understand code that already exists. The audience is different (product teams vs. engineers), the data model is different (full codebase index vs. current file context), and the purpose is different (strategic understanding vs. tactical coding assistance).

These tools do not compete. They complement each other at different layers of the software organization. The question is not which one to choose. It is whether your team has both layers covered.

Copilot has earned its adoption numbers through genuine utility for developers.

Code completion. Copilot's real-time code suggestions are its flagship capability. As you type, it predicts what you are writing and offers completions that range from single lines to entire functions. For routine code, the suggestions are frequently accurate and save meaningful time.

Code generation from prompts. Describe what you want in a comment or prompt, and Copilot generates the code. "Write a function that validates email addresses and returns a boolean" produces working code in seconds. This is especially valuable for boilerplate, utility functions, and well-understood patterns.

Test generation. Copilot can generate test cases for existing functions, including edge cases that engineers might overlook. While the tests still require review, the starting point accelerates the testing process.

Copilot Chat. GitHub's chat interface allows engineers to ask questions about the code they are working on. It can explain functions, suggest refactoring approaches, and help debug issues. The context is limited to the current file or selected code, not the entire codebase, but for local questions it is useful.

Broad IDE support. Copilot integrates with VS Code, JetBrains, Neovim, and other popular editors. Engineers can use it without changing their development environment.

The fundamental difference: Copilot is a coding tool for engineers. Glue is an intelligence tool for the entire team.

Audience. Copilot lives in the IDE. Its users are engineers. Product managers, engineering leaders, and CTOs do not open VS Code. Glue is designed for the people who make decisions about software without writing it. The natural language interface, feature catalogs, and dependency visualizations are built for non-technical consumption.

Scope of understanding. Copilot Chat understands the code in your current file or selection. Glue understands the entire codebase. When you ask "how does the billing system work?", Copilot Chat can help if you are already looking at the billing code. Glue can answer from anywhere because it has indexed and analyzed the complete repository. For a deeper look at AI codebase analysis, the distinction between local and global code understanding is critical.

Strategic vs. tactical. Copilot helps with tactical coding tasks: writing this function, debugging this error, generating this test. Glue helps with strategic product tasks: what features exist, what dependencies a project involves, where technical debt is concentrated, what the blast radius of a change would be. These are different levels of the decision stack.

Proactive insight. Copilot is reactive: it helps when you ask. Glue surfaces information proactively: knowledge concentration risks, feature overlaps, dependency tangles. The insights that prevent planning failures are often ones nobody thought to ask about.

Product-engineering alignment. Copilot makes engineers more productive. Glue makes product and engineering conversations more productive. When PMs understand the codebase and engineers do not have to explain it repeatedly, the entire team operates with less friction. See our AI product management guide for the broader view of AI tools that support this alignment.

Choose Copilot when your goal is engineering productivity: faster code writing, better code completion, test generation, and in-IDE assistance. If your engineers are spending too much time on boilerplate or routine code, Copilot provides immediate velocity gains.

Copilot is the right choice for any engineering team that writes code and wants to write it faster.

Choose Glue when your goal is codebase understanding across the organization. If PMs need to see what the system contains, if estimates are wrong because of invisible complexity, if roadmaps slip because dependencies are hidden, if leadership needs data on technical debt, Glue provides the intelligence layer.

Glue is the right choice when the challenge is not writing code faster but understanding the code that already exists.

Yes, and they complement each other perfectly. Copilot helps engineers build. Glue helps the team understand what has been built. There is zero overlap: Copilot operates in the IDE for individual developers, while Glue operates as a team intelligence platform for product, engineering leadership, and executive stakeholders.

The combination is particularly powerful: Copilot accelerates development, and Glue ensures that development is guided by accurate codebase understanding at the planning level.

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Is Glue a competitor to GitHub Copilot?

No. Glue and GitHub Copilot serve different purposes for different audiences. Copilot is a code generation tool for engineers that helps them write code faster. Glue is a codebase intelligence platform for product teams that helps them understand code without reading it. They operate in different parts of the software development lifecycle: Copilot in implementation, Glue in planning and strategy. Most organizations benefit from both.

Can GitHub Copilot explain a codebase to non-technical people?

Not effectively. Copilot Chat can explain code within the IDE, but its context is limited to the current file or selection, and its interface requires using a code editor. Product managers and executives need codebase understanding presented in natural language through a non-technical interface, with organization-wide context about features, dependencies, and architecture. This is a fundamentally different use case than what Copilot is designed for.

What AI tools do product teams need in 2026?

Product teams benefit from three categories of AI tools: (1) general-purpose AI (ChatGPT, Claude) for document drafting, research, and brainstorming, (2) AI codebase intelligence (Glue) for understanding the software system and making informed product decisions, and (3) AI coding tools (Copilot, Cursor) for engineering teams to build faster. Most product organizations have adopted category 1 and their engineers use category 3, but category 2, the codebase intelligence layer, is the missing piece that connects product strategy to engineering reality.