# AI infrastructure access

Last validated May 18, 2026

Tailscale helps teams secure their AI infrastructure by providing private, encrypted connectivity between GPU clusters, model servers, and development environments. Whether you're running AI workloads across multiple clouds or self-hosting LLMs, Tailscale ensures only authorized users and services can access your AI resources.

## Popular workflows

[**Connect inference and training servers**](/docs/use-cases/ai-infrastructure-access/connect-inference-servers) — Give GPU and inference servers identity-based access on your tailnet using tags and auth keys, with multi-tenant isolation and containerized workload support.

[**Centralize LLM access and spending**](/docs/use-cases/ai-infrastructure-access/centralize-llm-access-and-spending) — Route all LLM requests through a single gateway to eliminate API key sprawl, track per-user spending, and enforce access controls across developers, agents, and CI/CD pipelines.

[**Secure your AI training cluster**](/docs/use-cases/ai-infrastructure-access/secure-ai-training-cluster) — Remove the Kubernetes API server from the public internet and enforce identity-based access, session recording, and device posture for AI training cluster operations.