It is not meant to be a public model marketplace. The point is not to replace OpenRouter or any other provider. The point is to make my own AI workflow less scattered.

The problem

Once you use multiple model providers, the configuration spreads quickly:

• local model endpoint
• hosted model provider keys
• fallback behavior
• model names
• pricing assumptions
• tool-specific environment variables
• different capabilities for chat, embeddings, speech, and transcription

Every client wants a slightly different setup. That is annoying for normal use and worse for agents, because agent configuration should be boring and repeatable.

Tailgate puts that complexity behind one OpenAI-compatible surface.

Design shape

The core API follows familiar endpoints:

• GET /v1/models
• POST /v1/chat/completions
• POST /v1/embeddings
• POST /v1/audio/speech
• POST /v1/audio/transcriptions

Behind that surface, the gateway can route requests to local qwen-local, DeepSeek, OpenRouter, or future compatible providers. It tracks provider health, supports streaming passthrough, and can apply simple route selection rules.

The most useful rule is not fancy AI logic. It is policy:

• prefer local when the task fits
• keep secrets off client machines
• avoid sending private work to external providers accidentally
• fall back only when the route explicitly allows it

Why private

Tailgate contains too many assumptions about my own environment to be a clean open source project. It is shaped around private networking, provider credentials, model preferences, and operational defaults.

The public lesson is still useful: an AI gateway does not need to start as a large platform. For one person, it can simply be a policy boundary.

What I learned

The biggest value of a gateway is not only key management. It is reducing mental overhead.

Before the gateway, every tool needed to know too much. After the gateway, tools only need:

• one base URL
• one API key or private network policy
• normal OpenAI-compatible request shapes

That makes experiments cheaper. I can change the provider map without editing every client.

The second lesson is that local models need protection. A small local model service may only handle one heavy inference at a time. A gateway can enforce concurrency and fallback rules so clients do not accidentally overload the local runtime.

June 2026 update

The pushed version has a clearer premium route. premium/chat now prefers the configured DeepSeek premium model before falling back to the configured OpenRouter premium model. Config order is also used as a tiebreaker when candidates have the same price ranking, which keeps routing behavior predictable instead of surprising.

This is a good example of the gateway’s real job: not to be clever, but to make model policy explicit. A client can ask for premium/chat; Tailgate decides which concrete upstream model should satisfy that tier.

Current status

Tailgate is active and private. I expect it to stay private unless the configuration model becomes generic enough to be useful outside my own setup.

Discovering Tailscale Through WebVM

My journey with Tailscale began when I came across WebVM, an impressive project that allows you to run a virtual machine directly in your browser. Intrigued by the possibilities, I delved deeper and discovered that Tailscale could help me create a seamless, private network across all my devices.

What is Tailscale?

Tailscale is a mesh VPN network built on top of WireGuard, specifically using the WireGuard-go implementation. It allows you to create a secure, encrypted network between your devices, no matter where they are located.

Key Features

• Free Plan Available: Tailscale offers a free plan that is sufficient for personal use, allowing up to 20 devices.
• Ease of Use: Setting up Tailscale is straightforward. With minimal configuration, you can have your own network up and running quickly.
• Cross-Platform Support: Tailscale works exceptionally well across the Apple ecosystem, including iOS, tvOS, and macOS.
• Magic DNS Service: It provides a built-in DNS service that makes it easy to address your devices by name.

Performance on Different Platforms

While Tailscale shines on Apple devices, in my experience, it hasn’t performed as well on Windows. I encountered some connectivity and stability issues on Windows machines, which may vary based on individual setups.

My Tailscale Setup

Here’s how I leveraged Tailscale to connect my devices and access my home network seamlessly.

Running Tailscale on Apple TV

I installed Tailscale on my Apple TV, which stays online 24/7. This makes it an excellent candidate for a consistently available node in my network.

• Enabling Subnet Routing: By enabling subnet routing on the Apple TV, I can access other devices on the same local network, such as my NAS and router, as if I were connected locally.
• Setting Up an Exit Node: I configured the Apple TV as an exit node, allowing me to route internet traffic through my home network. This is useful when I need to access geo-restricted content or ensure a secure connection.

Connecting Other Devices

I also installed Tailscale on my MacBook and iPhone, which allows all my personal devices to communicate over the secure network, no matter where I am.

Benefits I’ve Enjoyed

• Secure Remote Access: I can securely access my home network devices from anywhere.
• Consistent Environment: All my devices appear on the same network, simplifying file sharing and remote management.
• No Need for Complex VPN Setups: Tailscale eliminates the need for traditional VPN configurations, port forwarding, or dynamic DNS services.

Conclusion

Tailscale has transformed the way I interact with my devices across different locations. Its ease of use and robust feature set make it an excellent choice for anyone looking to create a personal, secure network.

If you’re interested in simplifying your network setup and want a hassle-free way to connect your devices, I highly recommend giving Tailscale a try.

Links:

• Tailscale Official Website
• WebVM Project on GitHub
• WireGuard-go on GitHub

Note: This post reflects my personal experiences with Tailscale. Performance may vary based on individual configurations and devices.