Question 1

What is an AI coding assistant?

Accepted Answer

An AI coding assistant is a model-powered tool that suggests, writes, refactors, tests, and reviews code inside the IDE, terminal, or as a standalone repository-aware agent. Capability ranges from single-line autocomplete to autonomous pull-request authoring with full reasoning traces across large multi-file refactors.

Question 2

How much do AI coding tools cost?

Accepted Answer

Individual plans run £8–25 per month. Team tiers with agent capability and admin governance controls land between £30–65 per seat per month. Enterprise tiers with zero-retention, audit logs, SSO, and on-premises deployment options reach £80–180 per seat per month on annual contracts.

Question 3

How do I choose the best AI coding tool for my tech stack?

Accepted Answer

Trial the tool on your actual repository with your real languages and frameworks — model behaviour varies sharply by stack and codebase size. Strong context-window handling, repository-aware indexing, and native IDE integration matter more than headline benchmark scores on standardised coding tasks.

Question 4

Do AI coding tools share or train on my source code?

Accepted Answer

Default consumer tiers may use prompts and code to improve models. Enterprise tiers with contractual zero-retention guarantees, encrypted storage, and explicit training opt-out are the standard for regulated environments or IP-sensitive codebases. Always verify data-handling terms in writing before sending any production source code.

Question 5

Can AI coding tools do more than autocomplete?

Accepted Answer

Yes — the frontier capability is multi-file agentic refactors, autonomous test generation and execution, PR authoring with reasoning traces, and repository-level code review. Tools limited to single-line autocomplete are being repriced downward. Evaluate on the agentic capability you actually intend to deploy, tested on your real codebase.

Question 6

How should I measure whether an AI coding tool is actually productive?

Accepted Answer

Track suggestion acceptance rate, post-acceptance rework rate, and time spent on routine boilerplate tasks before and after adoption. Avoid renewing on perceived productivity or anecdotal feedback. Teams that measure see the real picture — acceptance rate alone without rework rate overstates the benefit significantly.

AI Coding marketplace

IntelliJ IDEA

GitHub

JetBrains

Codenvy

Emergent