Every engineering team has bought at least one AI coding tool by now. Most are disappointed with the results on anything beyond simple autocomplete.
The problem isn't that these tools are bad — it's that they solve different problems, and most teams picked the wrong one for their workflow. Here's what actually works.
The Understanding Gap
Before we rank tools, let's name the real issue. AI coding tools fall into two categories:
Code generation — they write code for you (Copilot, Cursor, Cline)
Code intelligence — they help you understand what to build (Glue, Sourcegraph, CodeSee)
Most teams bought category 1 and expected category 2. That's why your Copilot ROI feels underwhelming on complex, multi-file tickets.
Tier 1: The Heavyweights
GitHub Copilot
Best for: Individual developers writing new code in familiar patterns.
Copilot remains the most widely adopted AI coding tool. Its inline completions are fast and contextually aware within a single file. The chat feature has improved significantly with GPT-4 Turbo.
Where it breaks down: Cross-file refactors, legacy codebases with tribal knowledge, understanding feature boundaries. Copilot sees the file you're in — it doesn't understand your architecture.
Cursor
Best for: Developers who want AI-native editing with multi-file context.
Cursor's composer feature can reason across multiple files and make coordinated changes. The codebase indexing gives it broader context than Copilot.
Where it breaks down: Very large codebases (100K+ files), understanding business logic embedded in code patterns, knowing why code is structured a certain way.
Claude Code (Anthropic)
Best for: Complex reasoning tasks, architecture decisions, code review.
Claude Code's strength is reasoning depth. It handles ambiguous, multi-step problems better than any other tool. The terminal-native interface means it works with your existing workflow.
Where it breaks down: It works best when given good context. Without codebase-level understanding, even Claude is guessing about your architecture.
Tier 2: Specialized Tools
Sourcegraph Cody
Best for: Teams with large monorepos who need precise code search and context.
Cody combines Sourcegraph's code search with AI chat. The code graph context means it can answer questions about code relationships.
Glue
Best for: Teams that need to understand codebases before writing code — the pre-code intelligence gap.
Glue takes a fundamentally different approach. Instead of helping you write code, it helps you understand what to build. Paste a ticket, get a battle plan: affected files, feature boundaries, tribal knowledge from git history, blast radius analysis.
Unique capabilities: Feature discovery via graph clustering, competitive gap analysis, team knowledge risk mapping, AI-powered code tours.
Tabnine
Best for: Enterprise teams with strict data privacy requirements.
Tabnine can run entirely on-premise with private models trained on your code. The completions are good (not great), but the security story is unmatched.
Codeium / Windsurf
Best for: Individual developers who want free AI coding assistance.
Solid free tier with good completions. The Windsurf editor adds multi-file editing similar to Cursor.
Tier 3: Emerging Tools
Amazon Q Developer
AWS's entry into AI coding. Best for teams deep in the AWS ecosystem. The security scanning and upgrade features are useful.
JetBrains AI Assistant
Tight integration with IntelliJ-family IDEs. The refactoring suggestions leverage JetBrains' deep understanding of code structure.
Replit Agent
Best for prototyping and vibe coding. Can scaffold entire applications from descriptions. Less useful for production codebases.
The Verdict
There is no single best AI coding tool. The right choice depends on your bottleneck:
Writing code faster? → Copilot or Cursor
Understanding complex codebases? → Glue or Sourcegraph Cody
Complex reasoning and architecture? → Claude Code
Privacy and compliance? → Tabnine
Budget-conscious? → Codeium/Windsurf
The teams getting the most value are using multiple tools together. Glue for understanding what to build, Claude Code or Cursor for building it, and Copilot for the routine completions. The intelligence layer upstream makes every downstream tool smarter.
What Most Comparisons Miss
Every comparison article ranks these tools on autocomplete speed and code generation quality. That's measuring the wrong thing.
The real bottleneck for most teams isn't writing code — it's understanding what to write. We call this the Understanding Tax: the 30-90 minutes per ticket developers spend figuring out where to start, which files to touch, what might break.
No amount of faster autocomplete fixes that. The teams that have solved this are the ones shipping 3-4x faster — not because they type faster, but because they start coding with full context instead of spending an hour grepping and Slacking.
Keep Reading
The real bottleneck isn't choosing the right AI tool — it's the time developers spend understanding what to build before they start coding. This is what we call the Understanding Tax, and it's eating 20-35% of your engineering capacity.
Glue is the pre-code intelligence platform that fills this gap. Paste a ticket, get a battle plan — every function traced, every dependency mapped, before you write a line of code.