---
title: "AWS Sagemaker"
url: "/docs/providers/aws_sagemaker"
canonical_url: "https://docs.litellm.ai/docs/providers/aws_sagemaker"
type: "docs"
last_updated: "2026-10-08"
summary: "LiteLLM supports All Sagemaker Huggingface Jumpstart Models"
related:
  - "/docs/providers/anthropic_preserved_thinking"
  - "/docs/providers/bedrock"
---
# AWS Sagemaker

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

LiteLLM supports All Sagemaker Huggingface Jumpstart Models

:::tip

**We support ALL Sagemaker models, just set `model=sagemaker/<any-model-on-sagemaker>` as a prefix when sending litellm requests**

:::

### API KEYS
```python
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""
```

### Usage
```python
import os 
from litellm import completion

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

response = completion(
            model="sagemaker/<your-endpoint-name>", 
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            temperature=0.2,
            max_tokens=80
        )
```

### Usage - Streaming
Sagemaker currently does not support streaming - LiteLLM fakes streaming by returning chunks of the response string

```python
import os 
from litellm import completion

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

response = completion(
            model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", 
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            temperature=0.2,
            max_tokens=80,
            stream=True,
        )
for chunk in response:
    print(chunk)
```

## **LiteLLM Proxy Usage**

Here's how to call Sagemaker with the LiteLLM Proxy Server

### 1. Setup config.yaml

```yaml
model_list:
  - model_name: jumpstart-model
    litellm_params:
      model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614
      aws_access_key_id: os.environ/CUSTOM_AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/CUSTOM_AWS_SECRET_ACCESS_KEY
      aws_region_name: os.environ/CUSTOM_AWS_REGION_NAME
```

All possible auth params: 

```
aws_access_key_id: Optional[str],
aws_secret_access_key: Optional[str],
aws_session_token: Optional[str],
aws_region_name: Optional[str],
aws_session_name: Optional[str],
aws_profile_name: Optional[str],
aws_role_name: Optional[str],
aws_web_identity_token: Optional[str],
```

### 2. Start the proxy 

```bash
litellm --config /path/to/config.yaml
```
### 3. Test it

**Curl Request**

```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "jumpstart-model",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'
```
**OpenAI v1.0.0+**

```python
import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

response = client.chat.completions.create(model="jumpstart-model", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)

```
**Langchain**

```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
    model = "jumpstart-model",
    temperature=0.1
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)

print(response)
```

## Set temperature, top p, etc.

**SDK**

```python
import os
from litellm import completion

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

response = completion(
  model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
  messages=[{ "content": "Hello, how are you?","role": "user"}],
  temperature=0.7,
  top_p=1
)
```
**PROXY**

**Set on yaml**

```yaml
model_list:
  - model_name: jumpstart-model
    litellm_params:
      model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614
      temperature: <your-temp>
      top_p: <your-top-p>
```

**Set on request**

```python

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

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="jumpstart-model", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
],
temperature=0.7,
top_p=1
)

print(response)

```

## **Allow setting temperature=0** for Sagemaker

By default when `temperature=0` is sent in requests to LiteLLM, LiteLLM rounds up to `temperature=0.1` since Sagemaker fails most requests when `temperature=0`

If you want to send `temperature=0` for your model here's how to set it up (Since Sagemaker can host any kind of model, some models allow zero temperature)

**SDK**

```python
import os
from litellm import completion

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

response = completion(
  model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
  messages=[{ "content": "Hello, how are you?","role": "user"}],
  temperature=0,
  aws_sagemaker_allow_zero_temp=True,
)
```
**PROXY**

**Set `aws_sagemaker_allow_zero_temp` on yaml**

```yaml
model_list:
  - model_name: jumpstart-model
    litellm_params:
      model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614
      aws_sagemaker_allow_zero_temp: true
```

**Set `temperature=0` on request**

```python

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

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="jumpstart-model", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
],
temperature=0,
)

print(response)

```

## Pass provider-specific params 

If you pass a non-openai param to litellm, we'll assume it's provider-specific and send it as a kwarg in the request body. [See more](../completion/input.md#litellm-specific-params)

**SDK**

```python
import os
from litellm import completion

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

response = completion(
  model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
  messages=[{ "content": "Hello, how are you?","role": "user"}],
  top_k=1 # 👈 PROVIDER-SPECIFIC PARAM
)
```
**PROXY**

**Set on yaml**

```yaml
model_list:
  - model_name: jumpstart-model
    litellm_params:
      model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614
      top_k: 1 # 👈 PROVIDER-SPECIFIC PARAM
```

**Set on request**

```python

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

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="jumpstart-model", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
],
temperature=0.7,
extra_body={
    "top_k": 1 # 👈 PROVIDER-SPECIFIC PARAM
}
)

print(response)

```

### Passing Inference Component Name

If you have multiple models on an endpoint, you'll need to specify the individual model names, do this via `model_id`.  

```python
import os 
from litellm import completion

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

response = completion(
            model="sagemaker/<your-endpoint-name>", 
            model_id="<your-model-name",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            temperature=0.2,
            max_tokens=80
        )
```

### Passing credentials as parameters - Completion()
Pass AWS credentials as parameters to litellm.completion
```python
import os 
from litellm import completion

response = completion(
            model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            aws_access_key_id="",
            aws_secret_access_key="",
            aws_region_name="",
)
```

### Applying Prompt Templates
To apply the correct prompt template for your sagemaker deployment, pass in it's hf model name as well. 

```python
import os 
from litellm import completion

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

response = completion(
            model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", 
            messages=messages,
            temperature=0.2,
            max_tokens=80,
            hf_model_name="meta-llama/Llama-2-7b",
        )
```

You can also pass in your own [custom prompt template](../completion/prompt_formatting.md#format-prompt-yourself)

## Sagemaker Messages API 

Use route `sagemaker_chat/*` to route to Sagemaker Messages API

```
model: sagemaker_chat/<your-endpoint-name>
```

**SDK**

```python
import os
import litellm
from litellm import completion

litellm.set_verbose = True # 👈 SEE RAW REQUEST

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

response = completion(
            model="sagemaker_chat/<your-endpoint-name>", 
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            temperature=0.2,
            max_tokens=80
        )
```

**PROXY**

#### 1. Setup config.yaml 

```yaml
model_list:
  - model_name: "sagemaker-model"
    litellm_params:
      model: "sagemaker_chat/jumpstart-dft-hf-textgeneration1-mp-20240815-185614"
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      aws_region_name: os.environ/AWS_REGION_NAME
```

#### 2. Start the proxy 

```bash
litellm --config /path/to/config.yaml
```
#### 3. Test it

```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "sagemaker-model",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'
```

[**👉 See OpenAI SDK/Langchain/Llamaindex/etc. examples**](../proxy/user_keys.md#chatcompletions)

## Completion Models 

:::tip

**We support ALL Sagemaker models, just set `model=sagemaker/<any-model-on-sagemaker>` as a prefix when sending litellm requests**

:::

Here's an example of using a sagemaker model with LiteLLM 

| Model Name                    | Function Call                                                                                       |
|-------------------------------|-------------------------------------------------------------------------------------------|
| Your Custom Huggingface Model               | `completion(model='sagemaker/<your-deployment-name>', messages=messages)`        | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']`      
| Meta Llama 2 7B               | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b', messages=messages)`        | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']`              |
| Meta Llama 2 7B (Chat/Fine-tuned)  | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b-f', messages=messages)`      | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']`              |
| Meta Llama 2 13B              | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-13b', messages=messages)`       | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']`              |
| Meta Llama 2 13B (Chat/Fine-tuned) | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-13b-f', messages=messages)`     | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']`              |
| Meta Llama 2 70B              | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b', messages=messages)`       | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']`              |
| Meta Llama 2 70B (Chat/Fine-tuned) | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b-b-f', messages=messages)`   | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']`              |

## Embedding Models

LiteLLM supports all Sagemaker Jumpstart Huggingface Embedding models. Here's how to call it: 

```python
from litellm import completion

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

response = litellm.embedding(model="sagemaker/<your-deployment-name>", input=["good morning from litellm", "this is another item"])
print(f"response: {response}")
```

### Embeddings on inference components

If the endpoint hosts inference components, pass the component to call via `model_id`, same as for completions. LiteLLM sends it as the `InferenceComponentName` of the invoke call and keeps it out of the request body.

```python
response = litellm.embedding(
    model="sagemaker/<your-endpoint-name>",
    model_id="<your-inference-component-name>",
    input=["good morning from litellm"],
)
```

### OpenAI-compatible embedding endpoints

By default LiteLLM sends embedding requests in the Hugging Face shape (`{"inputs": [...]}`) and reads a list of vectors back. If the container behind your endpoint serves the OpenAI embeddings API instead (`{"input": [...]}` in, `{"data": [{"embedding": [...]}], "usage": {...}}` out), add the `openai/` route in front of the endpoint name. The request then goes out in the OpenAI shape with any `extra_body` fields merged in, and `data[].embedding` and `usage` are read from the response.

**SDK**

```python
response = litellm.embedding(
    model="sagemaker/openai/<your-endpoint-name>",
    model_id="<your-inference-component-name>",  # only for endpoints with inference components
    extra_body={"model": "<model name the container expects>"},  # only if the container needs one
    input=["good morning from litellm"],
)
```

**PROXY**

```yaml
model_list:
  - model_name: my-embeddings
    litellm_params:
      model: sagemaker/openai/<your-endpoint-name>
      model_id: <your-inference-component-name>
      extra_body:
        model: <model name the container expects>
      aws_region_name: us-east-1
    model_info:
      mode: embedding
```

Provider-specific keys in `litellm_params` are sent in the request body as-is, so keep only the ones your container accepts.

## Nova Models on SageMaker

LiteLLM supports Amazon Nova models (Nova Micro, Nova Lite, Nova 2 Lite) deployed on SageMaker Inference real-time endpoints. These custom/fine-tuned Nova models use an OpenAI-compatible API format.

**Reference:** [AWS Blog - Amazon SageMaker Inference for Custom Amazon Nova Models](https://aws.amazon.com/blogs/aws/announcing-amazon-sagemaker-inference-for-custom-amazon-nova-models/)

### Usage

Use the `sagemaker_nova/` prefix with your SageMaker endpoint name:

```python
import litellm
import os

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = "us-east-1"

# Basic chat completion
response = litellm.completion(
    model="sagemaker_nova/my-nova-endpoint",
    messages=[{"role": "user", "content": "Hello, how are you?"}],
    temperature=0.7,
    max_tokens=512,
)
print(response.choices[0].message.content)
```

### Streaming

```python
response = litellm.completion(
    model="sagemaker_nova/my-nova-endpoint",
    messages=[{"role": "user", "content": "Write a short poem"}],
    stream=True,
    stream_options={"include_usage": True},
)
for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")
```

### Multimodal (Images)

Nova models on SageMaker support image inputs using base64 data URIs:

```python
response = litellm.completion(
    model="sagemaker_nova/my-nova-endpoint",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
            ]
        }
    ],
)
```

### Proxy Config

```yaml
model_list:
  - model_name: nova-micro
    litellm_params:
      model: sagemaker_nova/my-nova-micro-endpoint
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      aws_region_name: us-east-1
```

### Supported Parameters

All standard OpenAI parameters are supported, plus these Nova-specific parameters:

| Parameter | Type | Description |
|-----------|------|-------------|
| `top_k` | integer | Limits token selection to top K most likely tokens |
| `reasoning_effort` | `"low"` \| `"high"` | Reasoning effort level (Nova 2 Lite custom models only) |
| `allowed_token_ids` | array[int] | Restrict output to specified token IDs |
| `truncate_prompt_tokens` | integer | Truncate prompt to N tokens if it exceeds limit |

```python
response = litellm.completion(
    model="sagemaker_nova/my-nova-endpoint",
    messages=[{"role": "user", "content": "Think step by step: what is 2+2?"}],
    top_k=40,
    reasoning_effort="low",
    logprobs=True,
    top_logprobs=2,
)
```

## Related pages

- [Preserved Thinking Prefix Stability](https://docs.litellm.ai/docs/providers/anthropic_preserved_thinking.md)
- [AWS Bedrock](https://docs.litellm.ai/docs/providers/bedrock.md)