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

As of January 30, DeepSeek-R1 fashions turned obtainable in Amazon Bedrock via 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 strong guardrails and complete tooling for secure AI deployment. Immediately, we’re making it even simpler to make use of DeepSeek in Amazon Bedrock via an expanded vary of choices, together with a brand new serverless answer.

The absolutely managed DeepSeek-R1 mannequin is now typically obtainable in Amazon Bedrock. Amazon Net Providers (AWS) is the primary cloud service supplier (CSP) to ship DeepSeek-R1 as a totally managed, typically obtainable mannequin. You possibly can speed up innovation and ship tangible enterprise worth with DeepSeek on AWS with out having to handle infrastructure complexities. You possibly can 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 advantage of its intensive options and tooling.

In accordance with DeepSeek, their mannequin is publicly obtainable below MIT license and presents sturdy capabilities in reasoning, coding, and pure language understanding. These capabilities energy clever choice help, software program growth, mathematical problem-solving, scientific evaluation, information insights, and complete data administration methods.

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

• Information safety – You possibly can entry the enterprise-grade safety, monitoring, and price management options of Amazon Bedrock which can be important for deploying AI responsibly at scale, all whereas retaining full management over your information. Customers’ inputs and mannequin outputs aren’t shared with any mannequin suppliers. You should utilize these key safety features by default, together with information 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 possibly can implement safeguards personalized to your software 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 together with your outlined set of insurance policies by filtering undesirable and dangerous content material in your generative AI functions.
• Mannequin analysis – You possibly can consider and evaluate fashions to determine the optimum mannequin to your use case, together with DeepSeek-R1, in a number of steps via both automated or human evaluations by utilizing Amazon Bedrock mannequin analysis instruments. You possibly can select automated analysis with predefined metrics corresponding to accuracy, robustness, and toxicity. Alternatively, you possibly can select human analysis workflows for subjective or customized metrics corresponding to relevance, fashion, and alignment to model voice. Mannequin analysis offers built-in curated datasets, or you possibly can herald your personal datasets.

We strongly advocate integrating Amazon Bedrock Guardrails and utilizing Amazon Bedrock mannequin analysis options together with your DeepSeek-R1 mannequin so as to add strong safety to your generative AI functions. To be taught 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
When you’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 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 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 avoid wasting for his or her trip subsequent yr. 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 yr, 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 be taught extra about utilization suggestions for prompts, check with the DeepSeek-R1 mannequin immediate information.

By selecting View API request, you can even entry the mannequin utilizing code examples within the AWS Command Line Interface (AWS CLI) and AWS SDK. You should utilize 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 "{"immediate": "Type_Your_Prompt_Heren", "max_tokens": 512, "temperature": 0.5, "top_p": 0.9}" 
       --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 era. To be taught extra, go to DeepSeek mannequin inference parameters and responses within the AWS documentation.

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., DeepSeek-R1 Mannequin.
model_id = "us.deepseek.r1-v1:0"

# Begin a dialog with the consumer message.
user_message = "Type_Your_Prompt_Here"
dialog = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

attempt:
    # Ship the message to the mannequin, utilizing a fundamental inference configuration.
    response = consumer.converse(
        modelId=model_id,
        messages=dialog,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "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}'. Cause: {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, in case you filter for “politics” phrase, your guardrails will acknowledge this phrase within the immediate and present you the blocked message.

You possibly can take a look at the guardrail with totally different inputs to evaluate the guardrail’s efficiency. You possibly can 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 via cross-Area inference. Examine the full Area record 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 at this time and ship suggestions to AWS re:Put up for Amazon Bedrock or via your typical AWS Assist contacts.

— Channy

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

Up to date on March 13, 2025 — Added information hyperlinks of DeepSeek-R1 mannequin prompts and mannequin inference parameters and responses.