Comparing AI Coding Tools for .NET Developers: A Hands-On Experience

Comparing AI Coding Tools for .NET Developers: A Hands-On Experience

The AI coding assistant landscape has exploded over the past two years. As a .NET developer, I have tried nearly every major tool in this space—some as my daily driver, others for specific tasks. The honest takeaway? There is no single AI coding tool that does everything well for .NET development. You will likely end up using two or three depending on what you need.

Here is my hands-on comparison of the tools I have used, what they are good at, and where they fall short.

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1. Cursor

Cursor is probably the most talked-about AI IDE right now, and for good reason. It works exceptionally well with .NET projects, especially ASP.NET Core. The codebase indexing is solid, the inline suggestions are context-aware, and its code review for changed code is genuinely useful—it highlights what was modified and explains the impact clearly.

What it does well:

• Strong autocomplete and multi-line completions for C# and Razor
• Good at understanding ASP.NET Core patterns (middleware, DI, controllers)
• Code review for staged changes is a standout feature
• Composer mode handles multi-file refactoring reasonably well

Where it falls short:

• Debugging experience is nowhere near JetBrains Rider or Visual Studio. When something breaks at runtime, you still need a proper debugger.
• API token consumption is aggressive. Opus tokens in particular run out surprisingly fast during intensive sessions.
• Auto mode (where Cursor decides which model to use) is inconsistent. Sometimes it picks a weaker model at the wrong moment.
• The cost-to-value ratio can be hard to justify when you are hitting token limits mid-task.

Best for: Writing new code, refactoring, understanding unfamiliar codebases.

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2. VS Code with GitHub Copilot + Claude Code

This combination has become my primary setup for daily development. VS Code is a great platform for open-source contributors because GitHub provides free or discounted Copilot access through its open-source program—that alone makes it worth considering if you contribute to public repositories.

Using GitHub Copilot for inline completions alongside the Claude Code extension for larger tasks gives you a well-rounded experience. Copilot handles the line-by-line suggestions, while Claude Code tackles more complex, multi-step operations like generating entire modules, writing tests, or walking through architectural decisions.

What it does well:

• Best ecosystem for extensions and customization
• Copilot's inline suggestions have improved dramatically and feel natural for C# development
• Claude Code handles context-heavy tasks that require understanding across multiple files
• Free/discounted Copilot access for open-source contributors is a meaningful advantage
• Claude recently announced an open-source program which is really great. You can access it here https://claude.com/contact-sales/claude-for-oss
• Works well with the C# Dev Kit, providing solid language support without requiring full Visual Studio

Where it falls short:

• Running two AI tools simultaneously can feel disjointed; you need to develop a workflow
• Claude Code operates more like a terminal-based agent, which takes some adjustment
• Copilot's suggestions can be generic for domain-specific patterns (like ABP Framework or ASP.NET Zero conventions)

Best for: Day-to-day development, open-source contributions, teams already using VS Code.

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3. Vibe Kanban

Vibe Kanban is an AI-assisted task management and development tool aimed at translating project tasks into code. I used it for smaller, isolated tasks where the scope was clear and contained.

What it does well:

• Useful for short, self-contained coding tasks
• The task-to-code workflow is intuitive when it works
• Good for non-coding tasks like writing or polishin technical articles or documentation

Where it falls short:

• A design overhaul changed the experience significantly, and the newer version no longer fits my workflow
• Not suitable for complex .NET projects where deep context is needed
• Limited value compared to using a general-purpose AI coding tool directly

Best for: Simple, isolated tasks if the current UI suits your workflow.

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4. JetBrains Rider

Rider is not primarily an AI tool, but it remains in my workflow for one specific reason: debugging. When an AI tool gets stuck generating code that compiles but behaves incorrectly at runtime, Rider's debugger is the fastest way to find out why. Its .NET-specific tooling—heap analysis, async call stack visualization, EF Core query viewer—is unmatched.

Rider now includes AI Assistant, which adds basic code generation and chat features. It is functional but not competitive with the dedicated AI IDEs.

What it does well:

• Best-in-class debugger for .NET
• Excellent for finding stubborn runtime bugs that AI tools cannot diagnose
• Strong refactoring tools that are deterministic and reliable
• Database and EF Core tooling built in

Where it falls short:

• The AI Assistant is basic compared to Cursor or Copilot
• Cost is a consideration if you are paying for both Rider and a separate AI tool
• Heavy on resources compared to VS Code

Best for: Debugging, profiling, and situations where you need deterministic tooling rather than AI-generated suggestions.

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5. OpenCode

OpenCode is an open-source AI coding agent that runs in your terminal, IDE, or as a desktop app. What sets it apart is its support for 75+ LLM providers—Claude, GPT, Gemini, and local models—giving you full flexibility over which model backs your coding sessions. It also integrates with Language Server Protocols (LSP) automatically, meaning it gets proper C# type information and project context without extra configuration.

What it does well:

• Works with virtually any AI model, including local models via Ollama
• Privacy-first: does not store your code or context data, making it viable for sensitive enterprise projects
• Parallel sessions let you run multiple agents on the same project simultaneously
• Open source with a large community (100K+ GitHub stars)
• Supports GitHub Copilot and ChatGPT Plus login, so you can reuse existing subscriptions
• LSP integration gives it real language context for C# rather than relying purely on text

Where it falls short:

• Terminal-first workflow takes adjustment if you are used to GUI-based tools
• No polished IDE experience comparable to Cursor or VS Code with Copilot
• Quality still depends on the model you choose

Best for: Developers who want full model flexibility, privacy guarantees, or a terminal-native AI coding workflow.

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6. Windsurf (by Codeium)

Windsurf is a newer entrant from Codeium that takes a similar approach to Cursor—a full IDE built around AI. Its "Cascade" agent handles multi-step tasks and can run terminal commands, edit files, and iterate on results autonomously.

What it does well:

• Cascade agent is competitive with Cursor's Composer for multi-file tasks
• More generous free tier than Cursor
• Decent C# and ASP.NET Core support

Where it falls short:

• Less mature than Cursor for .NET-specific workflows
• Smaller community and fewer integrations

Best for: Developers who want a Cursor-like experience with a lower entry cost.

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7. Continue (Open Source)

Continue is an open-source AI coding assistant that runs as a VS Code or JetBrains extension. Its key advantage is flexibility—you can connect it to any model provider, including local models via Ollama, OpenAI, Anthropic, or Azure OpenAI.

What it does well:

• Full control over which model you use and what it costs
• Supports local models for offline or privacy-sensitive development
• Codebase indexing and context-aware completions
• Works inside Rider, which is useful if you want AI assistance without leaving your debugger

Where it falls short:

• Requires more setup than a managed tool like Cursor or Copilot
• Quality depends entirely on the model you connect; results vary
• No polished agent mode for autonomous multi-step tasks

Best for: Teams with specific model requirements, privacy constraints, or those who want to avoid per-seat AI tool pricing.

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The Real Takeaway: You Will Use More Than One Tool

After months of trying to find the single best AI coding assistant for .NET development, I can say with confidence: it does not exist yet.

The practical workflow that works for me looks like this:

• Write new code and refactor → VS Code with Copilot + Claude Code, or Cursor
• Debug runtime issues and find stubborn bugs → JetBrains Rider
• Large, autonomous multi-file tasks → VS Code with Copilot + Claude Code
• Privacy-sensitive or model-flexible workflows → Continue with a local or Azure-hosted model

The AI tools are genuinely impressive at generating code and explaining patterns. But when something breaks at runtime—especially in complex ASP.NET Core applications with middleware chains, async flows, or EF Core query issues—you still need a real debugger. No AI tool currently replaces what Rider or Visual Studio offers there.

The other constraint is cost. Cursor's Opus token limits run out faster than you expect during intensive development sessions. If you are building a complex feature over several hours, you may hit limits at the worst moment. Budgeting across tools or switching to a model with more generous limits is something worth planning for.

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Quick Comparison

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Conclusion

The AI coding assistant space is moving fast, and the tools are getting better every quarter. But for .NET developers today, the winning strategy is to treat AI tools as productivity multipliers for writing and understanding code—not as replacements for proper debuggers and IDE tooling.

Pick one or two AI tools for your primary development flow, keep Rider or Visual Studio available for debugging, and stay open to switching as the tools evolve. The landscape six months from now will look different from today.

If you are building enterprise .NET applications with ASP.NET Zero, these tools work particularly well with the framework's layered architecture—AI assistants are excellent at generating application service implementations, DTO mappings, and unit tests once they understand the project structure. The key is giving them enough context about ASP.NET Zero's conventions before asking them to generate code.

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ASP.NET Zero works very well with almost all AI coding tools, see why here https://aspnetzero.com/ai