There are now dozens of credible AI coding assistants, and every one of them will show you a demo where it writes a working function in seconds. The demo is the easy part. The decision — which one fits how your team actually writes software — is where most teams get it backwards, because they start with the model and end with their workflow, when the order should be reversed.

This checklist runs in the order that predicts adoption six months out. Work through it top to bottom before you open a pricing page.

1. Write down your actual tasks, not your job title

"Software development" is not a use case. For one week, log the concrete requests you'd want help with:

  • Boilerplate and scaffolding
  • Writing tests for untested legacy code
  • Explaining unfamiliar codebases
  • Debugging a failing CI run
  • Refactoring across files
  • Writing documentation or commit messages

Rank them. Different assistants have genuinely different centres of gravity — some are strongest on new-feature generation, others on codebase navigation and explanation, others on terminal/agent-style multi-step work. If 60% of your list is "explain and modify this existing repo," you are shopping for a different tool than someone whose list is mostly greenfield scripting.

2. Check the data policy before the feature list

This is the step teams skip and regret. For each candidate, find explicit written answers to:

  • Is your code used to train models by default? Can that be disabled, and on which plan?
  • Where is code processed and stored, and for how long?
  • Is there a zero-retention or enterprise option, and does it cost extra?
  • Has the provider published a subprocessor list?

If a vendor's answers live only in a sales call rather than a documentation page, treat that as an answer in itself. A coding assistant sees your repository, your secrets-that-shouldn't-be-secrets, and your internal architecture. "Trust us" is not a data policy.

3. Verify the models behind the dropdown

Many assistants now let you switch models — proprietary, open-weight, sometimes self-hosted. When they don't say which model you're getting, ask:

  • Is the default model identified by name and version, or served behind an alias you can't verify?
  • Can you pin a version, or does the vendor upgrade it under you with notice?
  • What happens to quality and cost if you switch models — is pricing per model, per seat, or per token?

An unidentified model can't be compared on published specifications, so any benchmark a vendor quotes about "our assistant" may not describe what you'll actually use next month.

4. Trial it inside your real repository, not theirs

Every trial should follow the same shape, or you're comparing vibes:

  1. Pick one representative task from your step-1 list.
  2. Run it in your actual codebase (in a sandbox branch), not the vendor's sample project.
  3. Record: time saved, number of suggested edits you accepted, number you had to rewrite, and how often it hallucinated an API that didn't exist.
  4. Repeat with a second, different task type.

Two tasks is enough to expose a mismatch. If the tool needs constant hand-holding on your stack — your framework, your conventions, your monorepo layout — a bigger model won't fix that.

5. Count the review burden, not just the keystrokes saved

Generated code is cheap; reviewing generated code is not. Ask during the trial:

  • Do the diffs arrive small and reviewable, or as wall-of-text patches?
  • Does the assistant explain why it made a change?
  • Do your junior engineers understand the output, or are they rubber-stamping it?

A tool that writes 400 lines you must fully re-read may net negative on a complex repo. A tool that writes 60 lines you can verify at a glance may win.

6. Define your exit criteria before you subscribe

Write the kill switch down while you're not emotionally invested:

  • "If fewer than X of my trial tasks pass, we don't buy."
  • "If data-policy terms change, we can export and leave within N days."
  • "We review per-seat spend monthly against actual usage."

Assistant pricing in 2026 mixes seat fees, token meters, and rate-limited free tiers — a plan that looks cheap at trial scale can surprise a team of ten. Your exit criteria are what turn a subscription back into a decision.

The order is the point

Workflow → data policy → model → trial → review cost → exit criteria. Teams that start at step three ("which model is best?") end up benchmarking tools that were never going to fit their work. Teams that follow the order above often discover the "less famous" tool wins — because the choice was never about whose demo was flashiest.

Do the comparison on evidence, not demos. Oryxen maintains a source-attributed index of 36,000+ AI and developer tools — every record states its source, fit, and freshness, and no listing can pay to move up the page. Start with our guide to choosing an AI coding assistant, or search the index directly.