AI coding assistant evaluation checklist for small teams

How to use this checklist

Copy this into a doc, then run it while piloting the tool on one real task from your codebase — not the vendor’s demo. I have learned the hard way that every AI tool looks brilliant on a toy example and reveals itself on your actual code.

Do the check with the engineers who will use it daily, not just whoever pays the bill. Answer every item. A “no” is not failure — it is information.

The five evaluation gates

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Best for: AI-native teams

AI-native editor with agent mode, codebase-aware chat and a rules file for team conventions.

Open Cursor

GitHub Copilot

Best for: Least disruptive rollout

Mature assistant inside VS Code and JetBrains with organization policy controls.

Open Copilot

Tabnine

Best for: Privacy and self-hosting

Privacy-first assistant with self-hosting and strict data controls for regulated code.

Open Tabnine

The usual candidates to run through this checklist. Confirm each tool's current data-handling and admin features on its site — they change often.

Gate 1: Code quality

• Output compiles and passes our existing tests on a real task.
• Suggestions follow our conventions (naming, structure, style), not generic patterns.
• It does not invent functions, imports or APIs that do not exist.
• A reviewer can read and explain the generated code without reverse-engineering it.
• It handles our language and framework well, not just popular defaults.

Gate 2: Privacy and data handling

• We know exactly where our code is sent and processed.
• We have read the data-retention policy and how long code is stored.
• We confirmed whether our code can be used to train the vendor’s models — and opted out if needed.
• A zero-retention or self-hosted option exists if our code is sensitive.
• The terms are compatible with any client or contractual IP obligations.

Gate 3: Workflow fit

• It works in (or replaces well) the editor the team actually uses.
• Common actions are fast enough to use all day, not just impressive in a demo.
• It fits our git and review flow instead of fighting it.
• The learning curve is acceptable for the least technical teammate who needs it.
• It supports team conventions/rules so behavior is consistent across people.

Gate 4: Cost

• We priced it per seat, for the whole team, for 12 months.
• We accounted for the higher tier we will realistically want in month three.
• Usage limits (requests, premium models) are clear and won’t surprise us.
• The productivity gain plausibly justifies the annual spend.

Gate 5: Governance and offboarding

• Admin controls let us manage seats, policies and access centrally.
• No single person exclusively owns the account and billing.
• We can remove someone’s access in minutes when they leave.
• There is an owner for conventions, the prompt library and the quarterly review.

Scoring the result

Related

• Compare the shortlist in best AI coding assistants for small teams.
• Roll it out safely with how to add an AI coding assistant without wrecking code quality.
• For any other tool, use the general software decision checklist.