Local LLM Code Completion in 2026: Zed AI vs Continue vs Cursor

If you have been evaluating AI code completion tools, you have probably noticed something counterintuitive: the most popular option is not always the best for your specific workflow. After spending real time with three major local-LLM-based completion systems, here is what actually matters in production.

Why Local LLMs for Code Completion?

Before comparing tools, let us address the obvious question: why bother with local models when cloud options work immediately?

Privacy: Your proprietary code never leaves your machine. For enterprise projects, contract work, or sensitive applications, this is a hard requirement.

Cost: No monthly subscriptions. The hardware you already own powers everything. After the initial setup, the marginal cost is zero.

Customization: Run specialized fine-tuned models for specific languages, frameworks, or your own codebase patterns.

The Three Contenders

Zed AI

Zed AI is built directly into the Zed editor, a high-performance editor written in Rust. The local LLM integration uses Ollama under the hood.

Strengths:

• Sub-100ms completion latency on Apple Silicon


• Minimal, distraction-free completion UI


• Open source with a clear philosophy on data ownership


• Extremely lightweight on system resources

Weaknesses:

• Requires adopting the Zed editor (no VS Code or JetBrains support)


• Limited model configuration compared to alternatives

Continue

Continue is a VS Code and JetBrains extension that brings local LLM completion to whichever editor you already use. It supports Ollama, LM Studio, Jan, and other local model servers.

Strengths:

• Works inside VS Code or JetBrains (IntelliJ, PyCharm, WebStorm, etc.)


• Extremely configurable: pick any model, any server, any parameters


• Excellent for niche languages, older tech stacks, or domain-specific codebases

Weaknesses:

• Completions are generally slower than cloud alternatives


• Requires manual Ollama or server setup (20-30 minutes for beginners)

Cursor (Local Mode)

Cursor takes a hybrid approach: cloud models are the default, but local completion via Ollama became available in recent versions.

Strengths:

• Industry-leading AI features when using cloud: Tab advance, Composer, Agent mode


• Local mode available for when privacy is the priority

Weaknesses:

• Local mode is secondary to cloud mode, less optimized


• Requires Cursor Pro for the best experience

Side-by-Side Comparison

Real-World Impressions

After using each for at least one week on actual projects (a React TypeScript app, a Python data pipeline, and a Go CLI tool):

Zed AI surprised me most on the TypeScript project. The latency was genuinely imperceptible, and completions felt natural. The downside: I missed my VS Code muscle memory and several extensions.

Continue was the most flexible. DeepSeek-Coder produced the most contextually relevant completions for Go CLI work. But managing two AI systems in parallel created decision fatigue.

Cursor local mode was the weakest local experience. The cloud features are genuinely impressive, but local completion lagged behind both alternatives.

The Honest Recommendation

For developers who want local and already use VS Code/JetBrains: Start with Continue + Ollama. Yes, it takes 30 minutes to set up. That investment pays off in customization that cloud tools cannot match.

For developers willing to switch editors: Zed AI is the most thoughtfully integrated local completion experience available right now.

For developers who want the best AI features overall: Use Cursor with cloud models, and run Continue for local-only work. The two can coexist.

The Bottom Line

The local LLM code completion space is genuinely competitive now. All three tools are actively developed and free to try. No single tool dominates on all dimensions.

What local completion stack are you running? Share your setup in the comments.