LocalAI + Areebi

LocalAI provides an OpenAI-compatible API that runs entirely on your own infrastructure - a drop-in replacement for OpenAI's API that keeps all data on-premise. Organisations use LocalAI to serve open-source models via Docker containers, gaining the familiar OpenAI API interface without sending a single byte to external servers. Areebi's integration with LocalAI adds the enterprise governance layer that self-hosted deployments critically lack: DLP scanning, audit logging, access controls, and compliance reporting that match what cloud AI providers offer through Areebi.

The appeal of LocalAI is straightforward: teams that have built applications against the OpenAI API can switch to self-hosted models by changing a single endpoint URL. But this architectural simplicity creates a governance gap. Without Areebi, a LocalAI deployment is an ungoverned API endpoint that any application, script, or developer with network access can call - with no visibility into what data is being sent, who is sending it, or whether usage complies with organisational policies. The same shadow AI risks that apply to cloud providers apply to self-hosted infrastructure, compounded by the false sense of security that "on-premise means safe."

Areebi sits between your applications and LocalAI's API, applying the same governance controls that protect cloud AI usage. Every API call passes through Areebi's DLP engine, every request is logged with user identity and content, and every interaction is subject to your organisation's AI usage policies. The result is a self-hosted AI stack that delivers both the data sovereignty benefits of on-premise deployment and the governance rigour that auditors and regulators expect.

LocalAI's OpenAI-compatible API means that governance must operate at the API layer. Areebi functions as a governance proxy: applications that previously called LocalAI directly now call Areebi's endpoint, which applies DLP scanning, policy checks, and audit logging before forwarding the request to LocalAI. This architecture requires no changes to application code beyond updating the endpoint URL - the same API contract is preserved, but every request is now governed.

For organisations running multiple LocalAI instances across Docker hosts or Kubernetes clusters, Areebi provides centralised governance across all endpoints. A single policy set applies to all LocalAI instances regardless of where they run in your infrastructure. Administrators can track aggregate usage, enforce consistent DLP rules, and generate unified compliance reports across the entire self-hosted AI fleet. Token budgets prevent any single team from monopolising shared GPU resources, and model-level access controls ensure that sensitive models are only accessible to authorised groups.

Self-hosted AI deployments are not automatically compliant. Running models on your own Docker infrastructure addresses data residency requirements, but compliance frameworks like SOC 2 and HIPAA require evidence of access controls, monitoring, and data protection - not just data locality. A LocalAI deployment without governance lacks the audit trail, DLP scanning, and policy enforcement that auditors look for. Areebi provides these controls natively, generating the compliance artefacts that demonstrate your self-hosted AI infrastructure meets regulatory standards.

For organisations that chose LocalAI specifically for data sovereignty, Areebi ensures that the governance layer itself respects that boundary. All DLP scanning, policy evaluation, and audit logging happen on-premise alongside LocalAI. No governance metadata, usage data, or audit records leave your network. This end-to-end on-premise architecture satisfies the strictest data sovereignty requirements while providing the compliance evidence that frameworks like ISO 27001, NIST AI RMF, and sector-specific regulations demand.

Learn more about how the Areebi platform governs AI across cloud and on-premise deployments, review our trust centre for security architecture details, explore pricing for self-hosted environments, or request a demo to see LocalAI governance in your own infrastructure.