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

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

The absolutely managed DeepSeek-R1 mannequin is now usually obtainable in Amazon Bedrock. Amazon Internet Providers (AWS) is the primary cloud service supplier (CSP) to ship DeepSeek-R1 as a completely managed, usually obtainable 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 absolutely managed service and get the good thing about its in depth options and tooling.

In response to DeepSeek, their mannequin is publicly obtainable beneath MIT license and gives 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 information administration techniques.

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

• Knowledge safety – You may entry the enterprise-grade safety, monitoring, and value management options of Amazon Bedrock which are 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 safety features 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 custom-made 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 forestall 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 together 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 examine fashions to determine the optimum mannequin in your use case, together with DeepSeek-R1, in a couple of steps by way 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, model, and alignment to model voice. Mannequin analysis gives built-in curated datasets, or you possibly can herald your individual datasets.

We strongly suggest integrating Amazon Bedrock Guardrails and utilizing Amazon Bedrock mannequin analysis options together with your DeepSeek-R1 mannequin so as to add sturdy safety in your generative AI functions. To be taught extra, go to Defend 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 beneath Bedrock configurations within the left navigation pane. To entry the absolutely 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 beneath 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 avoid wasting for his or her trip subsequent 12 months. They will 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 through the 12 months, how ought to they divide their cash between the 2 choices to maximise their trip fund?

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

To be taught extra about utilization suggestions for prompts, seek advice from 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 how one can ship a textual content message to the DeepSeek-R1 mannequin utilizing the Amazon Bedrock Converse API for textual content era.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime shopper within the AWS Area you need to use.
shopper = boto3.shopper("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}],
    }
]

attempt:
    # Ship the message to the mannequin, utilizing a fundamental inference configuration.
    response = shopper.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 beneath Safeguards within the left navigation pane, and create a guardrail by configuring as many filters as you want. For instance, in the event you filter for “politics” phrase, your guardrails will acknowledge this phrase within the immediate and present you the blocked message.

You may check 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 be taught 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 make the most of the absolutely managed DeepSeek-R1 mannequin in Amazon Bedrock:

Now obtainable
DeepSeek-R1 is now obtainable absolutely managed in Amazon Bedrock within the US East (N. Virginia), US East (Ohio), and US West (Oregon) AWS Areas by way of cross-Area inference. Test the full Area checklist for future updates. To be taught extra, take a look at the DeepSeek in Amazon Bedrock product web page and the Amazon Bedrock pricing web page.

Give the DeepSeek-R1 mannequin a attempt within the Amazon Bedrock console immediately and ship suggestions to AWS re:Submit for Amazon Bedrock or by way of your ordinary AWS Help contacts.

— Channy

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