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

As of January 30, DeepSeek-R1 fashions grew to become obtainable in Amazon Bedrock by the Amazon Bedrock Market and Amazon Bedrock Customized Mannequin Import. Since then, hundreds of shoppers have deployed these fashions in Amazon Bedrock. Prospects worth the strong guardrails and complete tooling for secure AI deployment. At the moment, we’re making it even simpler to make use of DeepSeek in Amazon Bedrock by an expanded vary of choices, together with a brand new serverless resolution.

The absolutely managed DeepSeek-R1 mannequin is now usually obtainable in Amazon Bedrock. Amazon Net Companies (AWS) is the primary cloud service supplier (CSP) to ship DeepSeek-R1 as a totally 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 purposes with DeepSeek-R1’s capabilities utilizing a single API within the Amazon Bedrock’s absolutely managed service and get the advantage of its in depth options and tooling.

In line with DeepSeek, their mannequin is publicly obtainable underneath MIT license and affords 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 techniques.

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:

• Knowledge safety – You may 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 need to use 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 may implement safeguards custom-made 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 forestall hallucinations utilizing contextual grounding and Automated Reasoning checks. This implies you’ll be able to 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 purposes.
• Mannequin analysis – You may consider and evaluate fashions to determine the optimum mannequin in your use case, together with DeepSeek-R1, in just a few steps by both computerized or human evaluations by utilizing Amazon Bedrock mannequin analysis instruments. You may select computerized analysis with predefined metrics akin to accuracy, robustness, and toxicity. Alternatively, you’ll be able to select human analysis workflows for subjective or customized metrics akin to relevance, model, and alignment to model voice. Mannequin analysis gives built-in curated datasets, or you’ll be able to usher in your personal datasets.

We strongly suggest integrating Amazon Bedrock Guardrails and utilizing Amazon Bedrock mannequin analysis options along with your DeepSeek-R1 mannequin so as to add strong safety in your generative AI purposes. 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 underneath 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 underneath 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 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 throughout 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 study extra about utilization suggestions for prompts, confer with the README of the DeepSeek-R1 mannequin in its GitHub repository.

By selecting View API request, it’s also possible to entry the mannequin utilizing code examples within the AWS Command Line Interface (AWS CLI) and AWS SDK. You need to 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 era.

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 = "Describe the aim of a 'hi there 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 = 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}'. Motive: {e}")
    exit(1)

To allow Amazon Bedrock Guardrails on the DeepSeek-R1 mannequin, choose Guardrails underneath 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 subjects, 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 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 cross-Area inference. Examine the full Area record for future updates. To study 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 the moment and ship suggestions to AWS re:Publish for Amazon Bedrock or by your typical AWS Help contacts.

— Channy

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