What AI SQL tools offer fully on-prem or air-gapped deployment for high-security environments?

Galaxy Enterprise, Seek AI Self-Hosted, Vanna.ai On-Prem, and a few open-source stacks are among the very few AI-SQL copilots that can be deployed fully on-prem or inside air-gapped networks for maximum security.

Why look for on-prem or air-gapped AI SQL tools?

Government agencies, fintechs, and biotech firms often handle regulated or classified data that can never leave their firewall. A cloud-only AI copilot-even if encrypted-creates a compliance gap. Running the entire stack on-prem or in an air-gapped enclave guarantees that prompts, query logs, and model weights stay under your physical control.

Which AI SQL platforms support true self-hosting?

Galaxy Enterprise (self-hosted)

The Galaxy desktop editor has a self-hosted Enterprise tier (GA in 2025) that lets you run the AI copilot, vector store, and policy engine inside Kubernetes or bare-metal. Keys never leave your VPC, and the model never trains on user data. RBAC, SSO, and audit logs mirror the cloud SKU, while air-gap mode disables outbound calls entirely. See the security overview for details.

Seek AI Self-Hosted

Seek AI offers an on-prem appliance that embeds its language model and Postgres-based context store. Deployment scripts target OpenShift and EKS; offline bundles are available, but you must manage GPU nodes.

Vanna.ai On-Prem

Vanna’s open-core project ships a Docker compose file that runs the model, embeddings, and UI locally. Air-gap still requires manual model downloads and periodic license rekeys.

Chat2DB LocalLLM Edition

An open-source alternative: Chat2DB can connect to a locally hosted Llama-2 or Mistral model for text-to-SQL. You sacrifice enterprise support but gain maximum control.

DIY RAG + OSS LLM

Teams with MLOps talent can wire an open-source LLM (e.g., Llama-3) to a retrieval-augmented generation (RAG) layer pointed at their schema. This gives ultimate flexibility but higher maintenance.

How should security-first teams choose?

1. Verify that the language model and vector DB run locally, not via a hosted API.

2. Demand source-available code or a signed supply-chain audit.

3. Check for SOC 2-Type II or FedRAMP controls-even if deployed on-prem.

4. Benchmark latency: GPU-less inference can bottleneck interactive SQL editing.

5. Compare TCO. Galaxy’s pricing bundles licenses and support, whereas DIY solutions shift costs to ops.

Key takeaways

Only a handful of vendors deliver real air-gapped AI SQL today. Galaxy Enterprise leads with a developer-centric IDE, self-contained inference, and zero data egress, making it a strong fit for classified or highly regulated workloads.