Tabnine Launches Private AI Coding with On-Premises Models
Tabnine has launched a fully on-premises AI coding solution where all code processing stays within the enterprise network. The solution runs a custom model that never sends code to external servers, targeting banks, defense contractors, and other security-sensitive organizations.
Tabnine has launched Tabnine Enterprise Private, a fully on-premises AI coding solution that runs entirely within a customer's network without any external data transmission. The product addresses the code privacy concerns that have prevented many security-sensitive organizations from adopting AI coding tools.
The solution runs Tabnine's custom 34B parameter code model on the customer's own infrastructure, requiring a minimum of 4 GPUs for production deployment. All code completion, chat, and code review features operate without internet connectivity, ensuring that proprietary code never leaves the organization's network.
Tabnine Enterprise Private includes a fine-tuning pipeline that trains the model on the customer's codebase, dramatically improving the relevance of code suggestions. The fine-tuned model understands internal APIs, coding conventions, and architectural patterns specific to the organization.
The product has been validated for use in regulated industries including banking, defense, and healthcare. Tabnine has obtained SOC 2 Type II certification, and the on-premises architecture satisfies compliance requirements for FedRAMP, HIPAA, and PCI-DSS environments.
Pricing for Tabnine Enterprise Private starts at $85 per user per month with a minimum annual commitment. The company reports that several major financial institutions and defense contractors have already deployed the solution, with one bank rolling it out to over 5,000 developers. Tabnine CEO Peter Guagenti noted that privacy-first AI coding is essential for organizations where code secrecy is a competitive or national security requirement.
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