---
title: "How Model Access Works"
description: "Each model onboarded is a \"model deployment\" in LiteLLM."
type: tutorial
canonical_url: https://claudary.paisolsolutions.com/tutorials/model-access-guide
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:31:07.725Z
license: CC-BY-4.0
attribution: "How Model Access Works — Claudary (https://claudary.paisolsolutions.com/tutorials/model-access-guide)"
---

# How Model Access Works
Each model onboarded is a "model deployment" in LiteLLM.

## Overview

# How Model Access Works

## Concept 

Each model onboarded is a "model deployment" in LiteLLM. 

These model deployments are assigned to a "model group", via the "model_name" field in the config.yaml. 

## Example

```yaml
model_list:
  - model_name: my-custom-model
    litellm_params:
      model: openai/gpt-4o
      api_key: os.environ/OPENAI_API_KEY
```

In here, we onboard a model deployment for the model `gpt-4o` and assign it to the model group `my-custom-model`.

## Client-side request

Here's what a client-side request looks like:

```bash
curl --location 'http://localhost:4000/chat/completions' \\
-H 'Authorization: Bearer <your-api-key>' \\
-H 'Content-Type: application/json' \\
-d '{"model": "my-custom-model", "messages": [{"role": "user", "content": "Hello, how are you?"}]}'

```

## Access Control
When you give access to a key/user/team, you are giving them access to a "model group". 

Example:

```bash
curl --location 'http://localhost:4000/key/generate' \\
--header 'Authorization: Bearer <your-master-key>' \\
--header 'Content-Type: application/json' \\
--data-raw '{"models": ["my-custom-model"]}'
```

## Loadbalancing 

You can add multiple model deployments to a single "model group". LiteLLM will automatically load balance requests across the model deployments in the group.

Example:

```yaml
model_list:
  - model_name: my-custom-model
    litellm_params:
      model: openai/gpt-4o
      api_key: os.environ/OPENAI_API_KEY
  - model_name: my-custom-model
    litellm_params:
      model: azure/gpt-4o
      api_key: os.environ/AZURE_API_KEY
      api_base: os.environ/AZURE_API_BASE
      api_version: os.environ/AZURE_API_VERSION
```

This way, you can maximize your rate limits across multiple model deployments. 

## Fallbacks 

You can fallback across model groups. This is useful, if all "model deployments" in a "model group" are down (e.g. raising 429 errors).

Example:

```yaml
model_list:
  - model_name: my-custom-model
    litellm_params:
      model: openai/gpt-4o-mini
      api_key: os.environ/OPENAI_API_KEY
  - model_name: my-other-model
    litellm_params:
      model: openai/gpt-4o
      api_key: os.environ/OPENAI_API_KEY

litellm_settings:
  fallbacks: [{"my-custom-model": ["my-other-model"]}]
```

Fallbacks are done sequentially, so the first model group in the list will be tried first. If it fails, the next model group will be tried.


## Advanced: Model Access Groups

For advanced use cases, use [Model Access Groups](./model_access_groups) to dynamically group multiple models and manage access without restarting the proxy.

---

Source: [Claudary](https://claudary.paisolsolutions.com/tutorials/model-access-guide) · https://claudary.paisolsolutions.com