Choose an AI coding assistant for a small product team with a clear trial plan

Three assistant shapes (pick a class first)

Most small teams choose among classes, not eternal winners:

1. In-editor assistant — suggests and edits inside the IDE
2. Chat-with-repo — answers questions across files you point it at
3. Agent-style loops — proposes multi-step changes you still review in git

You can mix later. For a first purchase, pick the class that matches how you already work. IDE-heavy teams usually start with (1). Docs-and-onboarding pain often starts with (2). Large refactors tempt (3)—only after review habits exist.

Scorecard you can fill in one sitting

Rate each candidate 1–5 on criteria you name, for example:

• Fit with your main language and framework
• How it handles your private code (business plan / settings you understand)
• Review friction (easy to inspect diffs before accept)
• Team admin (seats, SSO if you need it, offboarding)
• Cost clarity for your seat count (check the vendor’s current public pricing page)
• Offline or degraded-mode behavior when the model is slow

Cap the shortlist at three. More than three is avoidance. Same rule as hire-vs-tool briefs.

Run a two-week bake-off on real work

Do not judge on toy repos.

Week 1–2 protocol:

1. Same three tasks for each tool (e.g. add a test, explain a rusty module, draft a small refactor)
2. Same engineer runs both, or two peers swap
3. Log: time to usable diff, lines you threw away, surprises in review
4. Log: anything that almost shipped wrong

Keep/kill on a calendar date. Feelings without a log become religion.

Security and IP hygiene

Before seats go to the whole team:

• Read the vendor’s public terms on training and retention for business tiers
• Decide what never leaves the laptop (secrets, prod dumps, customer exports)
• Prefer business accounts over personal free accounts for company code
• Align with your data traffic-light

If you cannot explain the data path to a new hire in two minutes, you are not ready to mandate the tool.

Team rules that prevent chaos

Write one page:

• Approved assistant(s)
• “Accept suggestion” still means you understand the change
• No pasting secrets into chat panes
• Generated code needs the same review as human code
• How to report a bad suggestion that almost shipped

Juniors need extra coaching: the assistant is a fast junior pair, not a staff engineer.

Cost without fantasy ROI

Count seats, setup time, and review time—not only the sticker. A cheap tool that floods review with junk is expensive. A pricier tool that cuts blank-page time on boring tests can be cheap. Use a simple before/after on one workflow; see AI ROI.

Worked example (pattern only)

A four-person TypeScript team lives in one IDE. They shortlist two in-editor assistants, skip agent-style tools for now, and run the same “add integration test” task twice. Tool A drafts faster but invents an API the repo does not have. Tool B is slower and wrong less often. They pick B, write the invent-API failure into onboarding, and revisit agents next quarter.

How a five-person team runs this week

Day 1: write the four job lines and pick one assistant class. Day 2–3: shortlist two tools max and set business accounts. Day 4–10: same three real tasks on both, log throws and near-misses. Day 11: keep/kill meeting with the scorecard on screen. Day 12: publish the one-page team rules. If nobody has time for the bake-off, you do not have time for a second tool either—stay with what you have until the log exists.

Short close

Pick a class, score three options max, bake off on real tasks, then freeze team rules—that is how a small team chooses.