Comparing AI coding assistants without the hype

Vendor demos all look good. Your codebase, compliance rules, and team habits do not. Use a checklist, run a short trial on your tasks, and document tradeoffs. Skip marketing scorecards that claim a single winner.

What you are actually buying

An assistant is a bundle of:

• Model access (and how often it routes to a stronger model)
• Editor / CI integration
• Context assembly (open files, repo index, docs)
• Telemetry and data-retention policy
• Pricing shape (seat vs usage vs both)

Evaluate the bundle, not a single chat screenshot.

Decision dimensions

1. Data boundary

Ask vendors (and verify in docs):

• Is code used for training by default?
• Can you disable retention?
• Is there a zero-retention or enterprise endpoint?
• Where is inference hosted?

If you cannot get a clear answer, treat that as a no for private repos.

2. Context quality

Weak assistants fail less from “dumb models” and more from missing files. Test:

• Multi-file refactors
• Monorepo path awareness
• Ability to follow existing abstractions (not invent a parallel style)

3. Edit UX

Prefer tools that:

• Show diffs you can accept hunk-by-hunk
• Do not silently overwrite uncommitted work
• Work offline or degrade gracefully when the API is down

4. Pricing predictability

Seat-only is easy to budget; usage-based can spike with agents and long contexts. Estimate with a token cost model if the product exposes token usage. Include your CI bot if it shares the bill.

5. Team fit

• Who can enable it (security review)?
• Shared prompt/rules files?
• Works in the IDEs people already use?

A 5-day trial protocol

Day 1: Install, connect repo, write a one-page “house rules” prompt.
Day 2: Feature task (new endpoint + test).
Day 3: Bugfix in unfamiliar module.
Day 4: Refactor across 3+ files.
Day 5: Docs / PR description generation; review false confidence.

Score each day 1–5 on: correctness, time saved, review burden, surprise edits. Review burden matters — an assistant that writes fast but wrong is negative leverage.

Red flags

• Invented APIs that “look right”
• Drive-by dependency additions
• Ignoring linter / type errors
• No way to pin model version
• Aggressive upsell mid-flow

Capture the decision

Use the AI tool comparison checklist to record scores and paste into your RFC. The goal is a documented choice your future self can defend — not a viral “I ranked 12 tools” post.

After you pick one

• Commit a short AI_RULES.md (style, forbidden patterns, test expectations)
• Set a monthly spend alert
• Revisit in 90 days; the market moves, and so will your stack