DeepSeek-R1 now accessible as a completely managed serverless mannequin in Amazon Bedrock

As of January 30, DeepSeek-R1 fashions grew to become accessible in Amazon Bedrock by means of the Amazon Bedrock Market and Amazon Bedrock Customized Mannequin Import. Since then, 1000’s of shoppers have deployed these fashions in Amazon Bedrock. Clients worth the sturdy guardrails and complete tooling for protected AI deployment. At this time, we’re making it even simpler to make use of DeepSeek in Amazon Bedrock by means of an expanded vary of choices, together with a brand new serverless resolution.

The totally managed DeepSeek-R1 mannequin is now usually accessible in Amazon Bedrock. Amazon Internet Providers (AWS) is the primary cloud service supplier (CSP) to ship DeepSeek-R1 as a completely managed, usually accessible mannequin. You may speed up innovation and ship tangible enterprise worth with DeepSeek on AWS with out having to handle infrastructure complexities. You may energy your generative AI functions with DeepSeek-R1’s capabilities utilizing a single API within the Amazon Bedrock’s totally managed service and get the good thing about its in depth options and tooling.

In response to DeepSeek, their mannequin is publicly accessible below MIT license and provides robust capabilities in reasoning, coding, and pure language understanding. These capabilities energy clever resolution help, software program growth, mathematical problem-solving, scientific evaluation, knowledge insights, and complete data administration techniques.

As is the case for all AI options, give cautious consideration to knowledge privateness necessities when implementing in your manufacturing environments, test for bias in output, and monitor your outcomes. When implementing publicly accessible fashions like DeepSeek-R1, contemplate the next:

• Information safety – You may entry the enterprise-grade safety, monitoring, and value management options of Amazon Bedrock which might be important for deploying AI responsibly at scale, all whereas retaining full management over your knowledge. Customers’ inputs and mannequin outputs aren’t shared with any mannequin suppliers. You should use these key security measures by default, together with knowledge encryption at relaxation and in transit, fine-grained entry controls, safe connectivity choices, and obtain numerous compliance certifications whereas speaking with the DeepSeek-R1 mannequin in Amazon Bedrock.
• Accountable AI – You may implement safeguards personalized to your utility necessities and accountable AI insurance policies with Amazon Bedrock Guardrails. This contains key options of content material filtering, delicate data filtering, and customizable safety controls to stop hallucinations utilizing contextual grounding and Automated Reasoning checks. This implies you possibly can management the interplay between customers and the DeepSeek-R1 mannequin in Bedrock along with your outlined set of insurance policies by filtering undesirable and dangerous content material in your generative AI functions.
• Mannequin analysis – You may consider and evaluate fashions to establish the optimum mannequin on your use case, together with DeepSeek-R1, in a number of steps by means of both automated or human evaluations through the use of Amazon Bedrock mannequin analysis instruments. You may select automated analysis with predefined metrics comparable to accuracy, robustness, and toxicity. Alternatively, you possibly can select human analysis workflows for subjective or customized metrics comparable to relevance, fashion, and alignment to model voice. Mannequin analysis gives built-in curated datasets, or you possibly can usher in your individual datasets.

We strongly advocate integrating Amazon Bedrock Guardrails and utilizing Amazon Bedrock mannequin analysis options along with your DeepSeek-R1 mannequin so as to add sturdy safety on your generative AI functions. To study extra, go to Shield your DeepSeek mannequin deployments with Amazon Bedrock Guardrails and Consider the efficiency of Amazon Bedrock sources.

Get began with the DeepSeek-R1 mannequin in Amazon Bedrock
For those who’re new to utilizing DeepSeek-R1 fashions, go to the Amazon Bedrock console, select Mannequin entry below Bedrock configurations within the left navigation pane. To entry the totally managed DeepSeek-R1 mannequin, request entry for DeepSeek-R1 in DeepSeek. You’ll then be granted entry to the mannequin in Amazon Bedrock.

Subsequent, to check the DeepSeek-R1 mannequin in Amazon Bedrock, select Chat/Textual content below Playgrounds within the left menu pane. Then select Choose mannequin within the higher left, and choose DeepSeek because the class and DeepSeek-R1 because the mannequin. Then select Apply.

Utilizing the chosen DeepSeek-R1 mannequin, I run the next immediate instance:

A household has $5,000 to save lots of for his or her trip subsequent 12 months. They'll place the cash in a financial savings account incomes 2% curiosity yearly or in a certificates of deposit incomes 4% curiosity yearly however with no entry to the funds till the holiday. In the event that they want $1,000 for emergency bills throughout the 12 months, how ought to they divide their cash between the 2 choices to maximise their trip fund?

This immediate requires a posh chain of thought and produces very exact reasoning outcomes.

To study extra about utilization suggestions for prompts, discuss with the README of the DeepSeek-R1 mannequin in its GitHub repository.

By selecting View API request, you may also entry the mannequin utilizing code examples within the AWS Command Line Interface (AWS CLI) and AWS SDK. You should use us.deepseek.r1-v1:0 because the mannequin ID.

Here’s a pattern of the AWS CLI command:

aws bedrock-runtime invoke-model 
     --model-id us.deepseek-r1-v1:0 
     --body "{"messages":[{"role":"user","content":[{"type":"text","text":"[n"}]}],max_tokens":2000,"temperature":0.6,"top_k":250,"top_p":0.9,"stop_sequences":["nnHuman:"]}" 
     --cli-binary-format raw-in-base64-out 
     --region us-west-2 
     invoke-model-output.txt

The mannequin helps each the InvokeModel and Converse API. The next Python code examples present ship a textual content message to the DeepSeek-R1 mannequin utilizing the Amazon Bedrock Converse API for textual content technology.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime consumer within the AWS Area you need to use.
consumer = boto3.consumer("bedrock-runtime", region_name="us-west-2")

# Set the mannequin ID, e.g., Llama 3 8b Instruct.
model_id = "us.deepseek.r1-v1:0"

# Begin a dialog with the person message.
user_message = "Describe the aim of a 'hiya world' program in a single line."
dialog = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

strive:
    # Ship the message to the mannequin, utilizing a fundamental inference configuration.
    response = consumer.converse(
        modelId=model_id,
        messages=dialog,
        inferenceConfig={"maxTokens": 2000, "temperature": 0.6, "topP": 0.9},
    )

    # Extract and print the response textual content.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

besides (ClientError, Exception) as e:
    print(f"ERROR: Cannot invoke '{model_id}'. Purpose: {e}")
    exit(1)

To allow Amazon Bedrock Guardrails on the DeepSeek-R1 mannequin, choose Guardrails below Safeguards within the left navigation pane, and create a guardrail by configuring as many filters as you want. For instance, if you happen to filter for “politics” phrase, your guardrails will acknowledge this phrase within the immediate and present you the blocked message.

You may take a look at the guardrail with totally different inputs to evaluate the guardrail’s efficiency. You may refine the guardrail by setting denied matters, phrase filters, delicate data filters, and blocked messaging till it matches your wants.

To study extra about Amazon Bedrock Guardrails, go to Cease dangerous content material in fashions utilizing Amazon Bedrock Guardrails within the AWS documentation or different deep dive weblog posts about Amazon Bedrock Guardrails on the AWS Machine Studying Weblog channel.

Right here’s a demo walkthrough highlighting how one can reap the benefits of the totally managed DeepSeek-R1 mannequin in Amazon Bedrock:

Now accessible
DeepSeek-R1 is now accessible totally managed in Amazon Bedrock within the US East (N. Virginia), US East (Ohio), and US West (Oregon) AWS Areas by means of cross-Area inference. Test the full Area checklist for future updates. To study extra, try the DeepSeek in Amazon Bedrock product web page and the Amazon Bedrock pricing web page.

Give the DeepSeek-R1 mannequin a strive within the Amazon Bedrock console immediately and ship suggestions to AWS re:Put up for Amazon Bedrock or by means of your ordinary AWS Assist contacts.

— Channy

Up to date on March 10, 2025 — Fastened screenshots of mannequin choice and mannequin ID.