AI tools for coding

Start with the artifact you need to deliver. Explaining an unfamiliar function, extracting repeated logic and implementing a cross-file change require different amounts of repository context. A chat assistant may be sufficient for a small pasted example; an integrated editor becomes relevant when the task must inspect files, make changes and run a local command. Keep that distinction visible when comparing access tiers.

Write the acceptance boundary before choosing a subscription. For a maintenance change, specify the public interface, existing tests and files the tool may edit. The result should be a reviewable patch with preserved behavior, not simply a long answer or a large number of generated lines. Inspect assertions as carefully as implementation code: weakening a test can make an incorrect change look successful.

Check costs in the unit the provider actually bills. A seat, model credit, request allowance and token pool are different quantities. Estimate your required seats separately from variable usage, then record consumption during a representative task. Do not divide two providers' headline allowances and treat the result as a quality or capacity ratio.

Finally, decide which repository data can leave the machine and who controls agent commands. Privacy settings, model availability and workspace permissions answer different questions. Keep a clean recovery branch and a known test command before the first edit. If a tool cannot access the required files or run your tests under the available plan, reduce the exercise to an explicit proposal and review that limitation before adopting it.