How to route local and cloud LLM requests through one gateway

If your applications call local and cloud models through separate integrations, centralize them behind an AI gateway for local and cloud models. This guide shows how to define one request path, connect approved destinations, add routing and fallback rules, and verify that requests remain controlled and observable.

1. Define the gateway contract first

Before connecting providers, decide what every application sends to the gateway. Keep the request format provider-neutral: The deciding factor among AI products is whether they support this exact workflow.

• Model or task name
• Prompt and response settings
• Team, agent, or project identity
• Data classification
• Latency and availability requirements
• Budget or usage limit

The gateway should authenticate the caller, apply policy, select a model, and return a consistent response. This makes a centralized LLM access gateway possible without rewriting every application when a provider changes.

For teams building a self-hosted LLM product, keep credentials and provider-specific settings inside the gateway rather than in application code.

2. Connect local and approved cloud destinations

Add each destination as a named backend:

1. Register the local model endpoint inside your infrastructure.
2. Add only approved cloud providers and store their credentials securely.
3. Record capabilities such as context size, modality, speed, and cost.
4. Mark which teams or data classes may use each backend.
5. Test health, authentication, timeout, and response normalization.

This creates an AI gateway for approved model providers and supports self-hosted LLM model routing. Sensitive requests can remain local, while permitted workloads can use a cloud model when policy allows.

A simple AI inference gateway architecture looks like this:

Application or agent
        |
Authentication and policy
        |
Gateway router
   /              \
Local models   Approved cloud models
        |
Usage, budget, and audit data

A gateway should also log the selected destination, request owner, latency, token usage, and outcome. These records support AI infrastructure products decisions without scattering observability code across projects.

3. Add routing and fallback rules

Start with explicit rules instead of automatic complexity. For example:

• Route confidential data to a local model.
• Route short, low-risk tasks to the lowest-cost approved model.
• Route difficult tasks to a stronger model when the requester is authorized.
• Set a timeout for each backend.
• If a local model is unavailable, fail over only to a permitted cloud destination.
• Never fail over across a data boundary without an explicit policy.

This is the core of an AI gateway with model routing and AI model routing with fallback. A request should carry enough metadata for the router to make a predictable decision.

Example policy:

if data_class == "confidential":
    use local_model
elif task == "summarization":
    use economical_approved_model
else:
    use primary_model
on timeout:
    use permitted_fallback

Test each path with a fixed set of requests. Confirm that the gateway routes correctly, blocks unauthorized destinations, and returns a clear error when no fallback is allowed. A multi-provider AI inference gateway improves resilience only when fallback rules respect security and budget limits.

4. Verify control before expanding access

Run a small pilot with one application and one agent. Check:

• Every request passes through the gateway.
• Direct provider credentials are removed from application configuration.
• Local and cloud calls are distinguishable in analytics.
• Team and agent budgets are enforced.
• Provider failures trigger only approved fallback behavior.
• Logs contain enough information for an audit without exposing sensitive prompts.

YABANIN AI provides a self-hosted gateway approach for centralizing model access, routing, budgets, and analytics inside your infrastructure. It can help teams centralize enterprise LLM requests while retaining control over local models and approved cloud providers.

When the checks pass, expand access by team and task, not by handing out provider keys. If you need to compare deployment approaches before implementation, use this guide to deploy a self-hosted AI gateway.