Tabnine capabilities

An autonomous, task-oriented AI assistant that operates directly in the developer's environment to complete complex multi-step tasks — including codebase-wide refactoring, test generation, documentation synthesis, and policy validation — while checking in for human approval at key decision points.

An AI agent that reviews pull requests and in-IDE code against team-specific rules, flags deviations, and provides guided fixes. Teams supply rules in plain English from existing docs, wikis, or codebases; Tabnine converts them into validation rules and also ships a predefined set of more than 140 rules per supported language (about half security-related, the rest covering correctness, readability, and performance; many are library-specific).

Tabnine's Enterprise Context Engine is an organizational-intelligence layer that ingests and correlates a company's repositories, CI/CD systems, code reviews, documentation, tickets, APIs, and infrastructure metadata. It goes beyond traditional RAG by combining semantic search with a knowledge graph: it extracts entities, relationships, dependencies, and patterns into a living structured model that AI agents can query and reason over, so they understand architecture, ownership, and constraints rather than just retrieving matching text. It is the context layer underneath Tabnine's Agentic Platform and is agent-agnostic - it also feeds context to Cursor, GitHub Copilot, Claude Code, and internal agents - without exposing source code.

Tabnine ships native plugins for VS Code, all JetBrains IntelliJ-platform IDEs, Eclipse, and Visual Studio 2022/2026. Older or community-contributed plugins for other editors exist but do not support advanced completions or Tabnine Chat.

A persistent chat panel inside the IDE for coding assistance throughout the full SDLC — asking coding questions, generating code from specs, writing tests, and explaining existing code. Users can select a model per conversation and see which model answered each response.

Real-time, AI-powered code suggestions that appear inline as you type; accept with Tab. Supports whole-line completions, full-function completions triggered after function declarations, and comment-to-code (natural language comments converted to code on the next line).

Tabnine connects to Jira (Cloud and Data Center) to use issue titles and descriptions as context for two AI agents — an Implementation Agent that generates code from a Jira issue with one click, and a Validation Agent that checks whether existing code meets the issue's requirements.

Tabnine Agent connects to Model Context Protocol (MCP) servers so the agent can call external tools, data sources, and APIs (for example Git operations, issue trackers, databases, and internal services) as part of multi-step agentic coding workflows, extending its reach beyond the local filesystem and built-in native tools.

Users can switch the AI model powering Tabnine Chat in real time using a model selector; each response indicates which model answered. Enterprise admins control which models are available org-wide. Enterprises can also connect their own private LLM endpoints (BYOAI / Self-Managed Models) while keeping Tabnine's governance and UX.

Tabnine offers two paid platform tiers — Code Assistant ($39/user/month) and Agentic Platform ($59/user/month) — plus an Enterprise Private Installation plan for self-hosted deployments. The former Dev plan is sunset (no new signups). No public free tier is listed.

Tabnine can be deployed in four modes — Secure SaaS (Tabnine-hosted), VPC (your cloud, no Tabnine access), On-Premises (your servers, Kubernetes), and fully Air-Gapped (no external connectivity) — giving enterprises complete control over data residency and network isolation.

Tabnine extends Chat context beyond the local workspace to remote repositories via Global RAG (Connection feature) and, on the Agentic Platform, a full Enterprise Context Engine that indexes repositories, CI/CD, docs, and ticketing systems into a multi-dimensional knowledge graph for system-wide reasoning.

Tabnine operates a "no-train-no-retain" policy — code sent for inferencing is held only in memory and deleted immediately after a response is returned; it is never stored, never shared with third parties, and never used to train Tabnine's public models. Tabnine's own code-completion model (and its legacy Tabnine-Protected chat model, being sunset after v6.2.0) are trained only on open-source code under permissive licenses.