Tabnine is one of the longest-running AI coding assistants on the market, founded in 2013 in Tel Aviv. Originally a code completion tool that predates GitHub Copilot, the product has evolved into a full agentic platform aimed at enterprises that care deeply about privacy, deployment control and licensing risk. Tabnine is used by more than a million developers, with a strong footprint among regulated and security-conscious organisations.
The defining promise of Tabnine is that customer code never trains shared models, never leaves the customer's chosen deployment boundary and is not retained on Tabnine's infrastructure. The platform trains its own models exclusively on permissively licensed open-source code and offers IP indemnification for enterprise customers, providing legal cover against copyright claims arising from generated code. Deployment options include SaaS, single-tenant VPC, on-premises and fully air-gapped installs.
Product capabilities now extend well beyond inline completion. Tabnine offers chat over a developer's codebase, an agentic Code Review Agent, test and refactor agents and an Enterprise Context Engine that grounds suggestions in the customer's own repositories, tickets and documentation. The platform can route requests to Tabnine's own models or to leading third-party LLMs depending on policy.
Pricing currently spans a free Dev Preview, a Dev plan at roughly $9 per user per month, Enterprise at around $39 per user per month and an Agentic Platform plan at roughly $59 per user per month. Tabnine retired its earlier free Basic tier in April 2025 and now offers a 14-day trial before customers move onto a paid plan. When Tabnine routes to third-party LLMs, pricing reflects provider costs plus a small handling fee.
Tabnine raised a $25M Series B led by Telstra Ventures in April 2023, bringing total disclosed funding to approximately $55M from investors including Atlassian, Hyperwise, Khosla Ventures and others. Compared with GitHub Copilot, Cursor, Codeium and Sourcegraph Cody, Tabnine differentiates on its private-by-design architecture, IP indemnification and deep enterprise deployment flexibility rather than purely on raw model quality.