Enterprise Features

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✨ Enterprise Features

:::tip

To get a license, get in touch with us here

:::

Features:

• Security
  • ✅ SSO for Admin UI
  • ✅ Audit Logs with retention policy
  • ✅ JWT-Auth
  • ✅ Control available public, private routes
  • ✅ Secret Managers - AWS Key Manager, Google Secret Manager, Azure Key, Hashicorp Vault
  • ✅ [BETA] AWS Key Manager v2 - Key Decryption
  • ✅ IP address‑based access control lists
  • ✅ Track Request IP Address
  • ✅ Set Max Request Size / File Size on Requests
  • ✅ Enforce Required Params for LLM Requests (ex. Reject requests missing ["metadata"]["generation_name"])
  • ✅ Key Rotations
• Customize Logging, Guardrails, Caching per project
  • ✅ Team Based Logging - Allow each team to use their own Langfuse Project / custom callbacks
  • ✅ Disable Logging for a Team - Switch off all logging for a team/project (GDPR Compliance)
• Spend Tracking & Data Exports
  • ✅ Set USD Budgets Spend for Custom Tags
  • ✅ Set Model budgets for Virtual Keys
  • ✅ Exporting LLM Logs to GCS Bucket, Azure Blob Storage
  • ✅ /spend/report API endpoint
• Control Guardrails per API Key/Team
• Custom Branding
  • ✅ Custom Branding + Routes on Swagger Docs
  • ✅ Custom Email Branding

Blocking web crawlers

To block web crawlers from indexing the proxy server endpoints, set the block_robots setting to true in your litellm_config.yaml file.

general_settings:
  block_robots: true

How it works

When this is enabled, the /robots.txt endpoint will return a 200 status code with the following content:

User-agent: *
Disallow: /

Required Params for LLM Requests

Use this when you want to enforce all requests to include certain params. Example you need all requests to include the user and ["metadata]["generation_name"] params.

Step 1 Define all Params you want to enforce on config.yaml

This means ["user"] and ["metadata]["generation_name"] are required in all LLM Requests to LiteLLM

general_settings:
  master_key: sk-1234
  enforced_params:  
    - user
    - metadata.generation_name

curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
    "enforced_params": ["user", "metadata.generation_name"]
}'

Step 2 Verify if this works

curl --location 'http://localhost:4000/chat/completions' \
    --header 'Authorization: Bearer sk-5fmYeaUEbAMpwBNT-QpxyA' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "hi"
        }
    ]
}'

Expected Response

{"error":{"message":"Authentication Error, BadRequest please pass param=user in request body. This is a required param","type":"auth_error","param":"None","code":401}}% 

curl --location 'http://localhost:4000/chat/completions' \
    --header 'Authorization: Bearer sk-5fmYeaUEbAMpwBNT-QpxyA' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gpt-3.5-turbo",
    "user": "gm",
    "messages": [
        {
        "role": "user",
        "content": "hi"
        }
    ],
   "metadata": {}
}'

Expected Response

{"error":{"message":"Authentication Error, BadRequest please pass param=[metadata][generation_name] in request body. This is a required param","type":"auth_error","param":"None","code":401}}% 

curl --location 'http://localhost:4000/chat/completions' \
    --header 'Authorization: Bearer sk-5fmYeaUEbAMpwBNT-QpxyA' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gpt-3.5-turbo",
    "user": "gm",
    "messages": [
        {
        "role": "user",
        "content": "hi"
        }
    ],
   "metadata": {"generation_name": "prod-app"}
}'

Expected Response

{"id":"chatcmpl-9XALnHqkCBMBKrOx7Abg0hURHqYtY","choices":[{"finish_reason":"stop","index":0,"message":{"content":"Hello! How can I assist you today?","role":"assistant"}}],"created":1717691639,"model":"gpt-3.5-turbo-0125","object":"chat.completion","system_fingerprint":null,"usage":{"completion_tokens":9,"prompt_tokens":8,"total_tokens":17}}%  

Control available public, private routes

See Control Public & Private Routes for detailed documentation on configuring public routes, admin-only routes, allowed routes, and wildcard patterns.

Spend Tracking

Viewing Spend per tag

/spend/tags Request Format

curl -X GET "http://0.0.0.0:4000/spend/tags" \
-H "Authorization: Bearer sk-1234"

/spend/tagsResponse Format

[
  {
    "individual_request_tag": "model-anthropic-claude-v2.1",
    "log_count": 6,
    "total_spend": 0.000672
  },
  {
    "individual_request_tag": "app-ishaan-local",
    "log_count": 4,
    "total_spend": 0.000448
  },
  {
    "individual_request_tag": "app-ishaan-prod",
    "log_count": 2,
    "total_spend": 0.000224
  }
]

:::tip For comprehensive spend tracking features including budgets, alerts, and detailed analytics, check out Spend Tracking.

:::

Guardrails - Secret Detection/Redaction

❓ Use this to REDACT API Keys, Secrets sent in requests to an LLM.

Example if you want to redact the value of OPENAI_API_KEY in the following request

Incoming Request

{
    "messages": [
        {
            "role": "user",
            "content": "Hey, how's it going, API_KEY = 'sk_1234567890abcdef'",
        }
    ]
}

Request after Moderation

{
    "messages": [
        {
            "role": "user",
            "content": "Hey, how's it going, API_KEY = '[REDACTED]'",
        }
    ]
}

Usage

Step 1 Add this to your config.yaml

litellm_settings:
  callbacks: ["hide_secrets"]

Step 2 Run litellm proxy with --detailed_debug to see the server logs

litellm --config config.yaml --detailed_debug

Step 3 Test it with request

Send this request

curl --location 'http://localhost:4000/chat/completions' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "llama3",
    "messages": [
        {
        "role": "user",
        "content": "what is the value of my open ai key? openai_api_key=sk-1234998222"
        }
    ]
}'

Expect to see the following warning on your litellm server logs

LiteLLM Proxy:WARNING: secret_detection.py:88 - Detected and redacted secrets in message: ['Secret Keyword']

You can also see the raw request sent from litellm to the API Provider

POST Request Sent from LiteLLM:
curl -X POST \
https://api.groq.com/openai/v1/ \
-H 'Authorization: Bearer gsk_mySVchjY********************************************' \
-d {
  "model": "llama3-8b-8192",
  "messages": [
    {
      "role": "user",
      "content": "what is the time today, openai_api_key=[REDACTED]"
    }
  ],
  "stream": false,
  "extra_body": {}
}

Secret Detection On/Off per API Key

❓ Use this when you need to switch guardrails on/off per API Key

Step 1 Create Key with hide_secrets Off

👉 Set "permissions": {"hide_secrets": false} with either /key/generate or /key/update

This means the hide_secrets guardrail is off for all requests from this API Key

curl --location 'http://0.0.0.0:4000/key/generate' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'Content-Type: application/json' \
    --data '{
        "permissions": {"hide_secrets": false}
}'

# {"permissions":{"hide_secrets":false},"key":"sk-jNm1Zar7XfNdZXp49Z1kSQ"}  

curl --location 'http://0.0.0.0:4000/key/update' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'Content-Type: application/json' \
    --data '{
        "key": "sk-jNm1Zar7XfNdZXp49Z1kSQ",
        "permissions": {"hide_secrets": false}
}'

# {"permissions":{"hide_secrets":false},"key":"sk-jNm1Zar7XfNdZXp49Z1kSQ"}  

Step 2 Test it with new key

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Authorization: Bearer sk-jNm1Zar7XfNdZXp49Z1kSQ' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "llama3",
    "messages": [
        {
        "role": "user",
        "content": "does my openai key look well formatted OpenAI_API_KEY=sk-1234777"
        }
    ]
}'

Expect to see sk-1234777 in your server logs on your callback.

:::info The hide_secrets guardrail check did not run on this request because api key=sk-jNm1Zar7XfNdZXp49Z1kSQ has "permissions": {"hide_secrets": false} :::

Content Moderation

Content Moderation with LLM Guard

Set the LLM Guard API Base in your environment

LLM_GUARD_API_BASE = "http://0.0.0.0:8192" # deployed llm guard api

Add llmguard_moderations as a callback

litellm_settings:
    callbacks: ["llmguard_moderations"]

Now you can easily test it

• Make a regular /chat/completion call

• Check your proxy logs for any statement with LLM Guard:

Expected results:

LLM Guard: Received response - {"sanitized_prompt": "hello world", "is_valid": true, "scanners": { "Regex": 0.0 }}

Turn on/off per key

1. Update config

litellm_settings:
    callbacks: ["llmguard_moderations"]
    llm_guard_mode: "key-specific"

2. Create new key

curl --location 'http://localhost:4000/key/generate' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
    "models": ["fake-openai-endpoint"],
    "permissions": {
        "enable_llm_guard_check": true # 👈 KEY CHANGE
    }
}'

# Returns {..'key': 'my-new-key'}

3. Test it!

curl --location 'http://0.0.0.0:4000/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer my-new-key' \ # 👈 TEST KEY
--data '{"model": "fake-openai-endpoint", "messages": [
        {"role": "system", "content": "Be helpful"},
        {"role": "user", "content": "What do you know?"}
    ]
    }'

Turn on/off per request

1. Update config

litellm_settings:
    callbacks: ["llmguard_moderations"]
    llm_guard_mode: "request-specific"

2. Create new key

curl --location 'http://localhost:4000/key/generate' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
    "models": ["fake-openai-endpoint"],
}'

# Returns {..'key': 'my-new-key'}

3. Test it!

import openai
client = openai.OpenAI(
    api_key="sk-1234",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages = [
        {
            "role": "user",
            "content": "this is a test request, write a short poem"
        }
    ],
    extra_body={ # pass in any provider-specific param, if not supported by openai, https://docs.litellm.ai/docs/completion/input#provider-specific-params
        "metadata": {
            "permissions": {
                "enable_llm_guard_check": True # 👈 KEY CHANGE
            },
        }
    }
)

print(response)

curl --location 'http://0.0.0.0:4000/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer my-new-key' \ # 👈 TEST KEY
--data '{"model": "fake-openai-endpoint", "messages": [
        {"role": "system", "content": "Be helpful"},
        {"role": "user", "content": "What do you know?"}
    ]
    }'

Content Moderation with LlamaGuard

Currently works with Sagemaker's LlamaGuard endpoint.

How to enable this in your config.yaml:

litellm_settings:
   callbacks: ["llamaguard_moderations"]
   llamaguard_model_name: "sagemaker/jumpstart-dft-meta-textgeneration-llama-guard-7b"

Make sure you have the relevant keys in your environment, eg.:

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

Customize LlamaGuard prompt

To modify the unsafe categories llama guard evaluates against, just create your own version of this category list

Point your proxy to it

callbacks: ["llamaguard_moderations"]
  llamaguard_model_name: "sagemaker/jumpstart-dft-meta-textgeneration-llama-guard-7b"
  llamaguard_unsafe_content_categories: /path/to/llamaguard_prompt.txt

Content Moderation with Google Text Moderation

Requires your GOOGLE_APPLICATION_CREDENTIALS to be set in your .env (same as VertexAI).

How to enable this in your config.yaml:

litellm_settings:
   callbacks: ["google_text_moderation"]

Set custom confidence thresholds

Google Moderations checks the test against several categories. Source

Set global default confidence threshold

By default this is set to 0.8. But you can override this in your config.yaml.

litellm_settings: 
    google_moderation_confidence_threshold: 0.4 

Set category-specific confidence threshold

Set a category specific confidence threshold in your config.yaml. If none set, the global default will be used.

litellm_settings: 
    toxic_confidence_threshold: 0.1

Here are the category specific values:

Swagger Docs - Custom Routes + Branding

:::info

Requires a LiteLLM Enterprise key to use. Get a free 2-week license here

:::

Set LiteLLM Key in your environment

LITELLM_LICENSE=""

Customize Title + Description

In your environment, set:

DOCS_TITLE="TotalGPT"
DOCS_DESCRIPTION="Sample Company Description"

Customize Routes

Hide admin routes from users.

In your environment, set:

DOCS_FILTERED="True" # only shows openai routes to user

<Image img={require('../../img/custom_swagger.png')} style={{ width: '900px', height: 'auto' }} />

Enable Blocked User Lists

If any call is made to proxy with this user id, it'll be rejected - use this if you want to let users opt-out of ai features

litellm_settings: 
     callbacks: ["blocked_user_check"] 
     blocked_user_list: ["user_id_1", "user_id_2", ...]  # can also be a .txt filepath e.g. `/relative/path/blocked_list.txt` 

How to test

Set user=<user_id> to the user id of the user who might have opted out.

import openai
client = openai.OpenAI(
    api_key="sk-1234",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages = [
        {
            "role": "user",
            "content": "this is a test request, write a short poem"
        }
    ],
    user="user_id_1"
)

print(response)

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "gpt-3.5-turbo",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ],
      "user": "user_id_1" # this is also an openai supported param 
    }
'

:::info

Suggest a way to improve this

:::

Using via API

Block all calls for a customer id

curl -X POST "http://0.0.0.0:4000/customer/block" \
-H "Authorization: Bearer sk-1234" \ 
-D '{
"user_ids": [<user_id>, ...] 
}'

Unblock calls for a user id

curl -X POST "http://0.0.0.0:4000/user/unblock" \
-H "Authorization: Bearer sk-1234" \ 
-D '{
"user_ids": [<user_id>, ...] 
}'

Enable Banned Keywords List

litellm_settings: 
     callbacks: ["banned_keywords"]
     banned_keywords_list: ["hello"] # can also be a .txt file - e.g.: `/relative/path/keywords.txt`

Test this

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "gpt-3.5-turbo",
      "messages": [
        {
          "role": "user",
          "content": "Hello world!"
        }
      ]
    }
'

Public AI Hub

Share a public page of available models and agents for users

Learn more

<Image img={require('../../img/model_hub.png')} style={{ width: '900px', height: 'auto' }}/>

[BETA] AWS Key Manager - Key Decryption

This is a beta feature, and subject to changes.

Step 1. Add USE_AWS_KMS to env

USE_AWS_KMS="True"

Step 2. Add LITELLM_SECRET_AWS_KMS_ to encrypted keys in env

LITELLM_SECRET_AWS_KMS_DATABASE_URL="AQICAH.."

LiteLLM will find this and use the decrypted DATABASE_URL="postgres://.." value in runtime.

Step 3. Start proxy

$ litellm

How it works?

• Key Decryption runs before server starts up. Code
• It adds the decrypted value to the os.environ for the python process.

Note: Setting an environment variable within a Python script using os.environ will not make that variable accessible via SSH sessions or any other new processes that are started independently of the Python script. Environment variables set this way only affect the current process and its child processes.

Set Max Request / Response Size on LiteLLM Proxy

Use this if you want to set a maximum request / response size for your proxy server. If a request size is above the size it gets rejected + slack alert triggered

Usage

Step 1. Set max_request_size_mb and max_response_size_mb

For this example we set a very low limit on max_request_size_mb and expect it to get rejected

:::info In production we recommend setting a max_request_size_mb / max_response_size_mb around 32 MB

:::

model_list:
  - model_name: fake-openai-endpoint
    litellm_params:
      model: openai/fake
      api_key: fake-key
      api_base: https://exampleopenaiendpoint-production.up.railway.app/
general_settings: 
  master_key: sk-1234

  # Security controls
  max_request_size_mb: 0.000000001 # 👈 Key Change - Max Request Size in MB. Set this very low for testing 
  max_response_size_mb: 100 # 👈 Key Change - Max Response Size in MB

Step 2. Test it with /chat/completions request

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "fake-openai-endpoint",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'

Expected Response from request We expect this to fail since the request size is over max_request_size_mb

{"error":{"message":"Request size is too large. Request size is 0.0001125335693359375 MB. Max size is 1e-09 MB","type":"bad_request_error","param":"content-length","code":400}}