---
title: "How Model Access Works"
url: "/docs/proxy/model_access_guide"
canonical_url: "https://docs.litellm.ai/docs/proxy/model_access_guide"
type: "docs"
last_updated: "2026-10-02"
related:
  - "/docs/proxy/model_discovery"
  - "/docs/proxy/model_access"
---
# How Model Access Works

> Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt


## 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-5.6-terra
      api_key: os.environ/OPENAI_API_KEY
```

In here, we onboard a model deployment for the model `gpt-5.6-terra` 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-5.6-terra
      api_key: os.environ/OPENAI_API_KEY
  - model_name: my-custom-model
    litellm_params:
      model: azure/gpt-5.6-terra
      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-5.6-luna
      api_key: os.environ/OPENAI_API_KEY
  - model_name: my-other-model
    litellm_params:
      model: openai/gpt-5.6-terra
      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.

## Related pages

- [Model Discovery](https://docs.litellm.ai/docs/proxy/model_discovery.md)
- [Restrict Model Access](https://docs.litellm.ai/docs/proxy/model_access.md)