How to Choose an AI Code Assistant

The AI code assistant market has a lot of overlap. Most tools can autocomplete, chat, explain code, and generate functions from comments. Choosing between them is less about finding one with a unique feature and more about figuring out which fits your team's actual workflow.

Here's the framework I'd use.

Start with the constraint that rules others out

Before comparing features, a few constraints narrow the field immediately:

Editor lock-in — Cursor is an excellent tool, but it's an editor, not a plugin. If your team isn't willing to switch editors, Cursor is out. GitHub Copilot, Codeium, and Continue all work as extensions across VS Code, JetBrains, and others.

Data residency — If code can't leave your infrastructure, you need either Tabnine Enterprise (self-hosted) or Continue with a local model. Every cloud-based tool sends code to external servers for processing. That's fine for most teams and a hard blocker for some.

Budget — If the answer is "zero spend," Codeium is the most capable genuinely free option. Continue is free if you run local models (you'll need a decent GPU), or inexpensive if you use your own API key.

Ecosystem integration — If you're on GitLab Premium, Duo Pro is included and the platform integration (merge request summaries, CI chat) is better than what a standalone tool provides. If you're AWS-heavy, Amazon Q Developer's infrastructure suggestions and security scanning are worth the different UX.

The three decisions that actually matter

1. Plugin or editor?

Cursor is AI-native from the ground up. Every interaction is designed around AI being in the loop. The Composer makes multi-file edits from a single prompt. Tab completion fills in entire blocks, not just lines.

GitHub Copilot, Codeium, Continue, and others are plugins — they add AI to your existing editor without changing how you work. For most teams, this is the right call. Switching editors is a real cost, and plugins have gotten very capable.

The plugin approach wins if: your team is distributed across VS Code, JetBrains, Vim, etc.; you don't want to change your workflow; or you want to try AI assistance with low commitment.

The editor approach wins if: you're willing to commit, you want the deepest integration, and the AI features feel like they're fighting the editor rather than part of it.

2. Autocomplete focus or full-context AI?

Some tools are primarily autocomplete engines: they predict what comes next as you type. Supermaven is the clearest example — it's optimized for speed and prediction quality, with a 300,000-token context window specifically for better multi-line suggestions.

Others are more like AI pair programmers you can have a conversation with. Sourcegraph Cody indexes your entire codebase and answers questions about it. Continue connects to powerful models and supports file references, symbol lookups, and custom context in chat.

Most tools do both, but with different emphasis. Think about where your current tooling falls short: if it's "suggestions are too slow or wrong," lean autocomplete. If it's "I need to understand and modify code across many files," lean full-context.

3. How much control do you want?

Tools like GitHub Copilot and Codeium are opinionated: they work well out of the box, they handle model selection and infrastructure, and configuration is minimal.

Continue is the opposite: you choose the model, you configure the context, you decide what data is sent where. This is more powerful and more work.

For most teams, the low-configuration option is the right choice — the productivity gain from AI assistance doesn't require customization to capture. For teams with specific privacy requirements, unusual codebases, or developers who want fine-grained control, Continue's flexibility is worth the setup time.

Language and stack coverage

Most major tools handle Python, JavaScript/TypeScript, Java, Go, Rust, and C++ well. The differences show up at the edges:

• Infrastructure as code (Terraform, Pulumi, CloudFormation): Amazon Q Developer and GitHub Copilot are strongest here
• Mobile (Swift, Kotlin): JetBrains AI Assistant has an edge for teams in the JetBrains ecosystem
• Niche or newer languages: Check specifically before committing — some tools have noticeably weaker support for less common languages

Running a real evaluation

Free tiers and trials exist specifically for this. Use them. A two-week trial on a real project is worth more than any feature comparison.

What to measure during a trial:

Relevance rate — What fraction of suggestions are actually useful? A fast tool with low relevance trains you to ignore it. That's worse than a slower tool with higher accuracy.

Multi-file capability — Can it help with changes that touch more than one file? This matters more than single-file completion quality for most real work.

Context understanding — Does it understand your project's patterns and conventions after a few days, or does it keep suggesting things that don't fit your codebase?

IDE performance — Does it slow down your editor? Some tools add noticeable latency.

Irrelevance friction — How much effort does it take to dismiss a bad suggestion? If you're constantly closing unwanted completions, the UX is fighting you.

Quick decision guide

A note on irreversibility

Most AI code assistants aren't very sticky — you can switch with a day's effort. The exception is Cursor, where switching means switching editors. That's worth thinking about before committing to it as your team's standard.

The other practical consideration: whatever you choose, establish a team norm around reviewing AI-generated code. The tools are capable enough to generate plausible-looking code that's subtly wrong. Speed gains evaporate if you're debugging AI-generated bugs in production.