Guides
Practical guides to running and routing AI across your own hardware, approved machines and frontier APIs.
Air-gapped AI: a practical guide for enterprises
Learn how to deploy air-gapped AI, keep model requests on approved hardware and test offline workflows with Aventail™ Link, Control and Connect.
Claude Code security for enterprises: a practical deployment checklist
Review Claude Code permissions, inference routing, fallback rules and telemetry before rolling out private AI coding across your enterprise.
OpenCode vs Claude Code: choosing an AI coding tool for your team
Compare OpenCode and Claude Code for team AI. Evaluate model compatibility, permissions, privacy, administration and cost with a repeatable pilot.
LLM gateway vs inference engine: what does your enterprise need?
Compare LLM gateways, inference engines and fleet management. Choose an architecture for private models, approved cloud APIs and policy-based routing.
Use open models in Claude with Aventail™
Use supported open-weight models in Claude Desktop and Claude Code through Aventail™ Connect, with model requests under your routing rules.
How to use OpenCode with Aventail™ Connect
Connect OpenCode to Aventail™ Link in Connected Apps: no provider config or API key, model chosen in Link, and how to check where requests really ran.
Deploying a private AI workspace across laptops and servers
Deploy a private AI workspace across laptops and servers with Aventail. Plan self-hosted and hybrid setups, staged rollout and governance checks.
How to route between local models and approved cloud APIs
Route AI requests between local open-weight models, peer machines and approved cloud APIs: rules, fallback hops and how to verify where requests ran.
How to reduce AI provider dependency
Keep a tested choice between approved frontier APIs and supported models on your own hardware, without assuming models are interchangeable.
Ollama vs Aventail™: apps, devices and routing
Compare Ollama and Aventail™ for Claude apps, local AI and managing multiple devices. Explore Connect, fleet management and routing to peer devices in beta.
LM Studio vs Aventail™: local AI, devices and routing for teams
Compare LM Studio and Aventail™ for local AI, remote devices and enterprise routing. Assess deployment, data boundaries, cost and beta routing with a practical checklist.
Keeping sensitive AI requests within approved infrastructure
Decide where each AI request may run, evaluate it on the device, and keep every permitted fallback within the same boundaries.
From GPU capacity to a usable inference service
Give customers a way to deploy models and use your compute, with an inference engine and orchestration layer working together.