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 by the Amazon Bedrock Marketplace and Amazon Bedrock Custom Model Import. Since then, 1000’s of consumers have deployed these fashions in Amazon Bedrock. Customers worth the sturdy guardrails and complete tooling for protected AI deployment. Today, 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 Web Services (AWS) is the primary cloud service supplier (CSP) to ship DeepSeek-R1 as a totally managed, usually obtainable mannequin. You can speed up innovation and ship tangible enterprise worth with DeepSeek on AWS with out having to handle infrastructure complexities. You can 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 intensive options and tooling.

According to DeepSeek, their mannequin is publicly obtainable underneath MIT license and gives robust capabilities in reasoning, coding, and pure language understanding. These capabilities energy clever determination help, software program growth, mathematical problem-solving, scientific evaluation, knowledge insights, and complete information administration programs.

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 obtainable fashions like DeepSeek-R1, contemplate the next:

• Data safety – You can 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. Users’ inputs and mannequin outputs aren’t shared with any mannequin suppliers. You can 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.
• Responsible AI – You can implement safeguards custom-made to your utility necessities and accountable AI insurance policies with Amazon Bedrock Guardrails. This consists of key options of content material filtering, delicate data filtering, and customizable safety controls to forestall hallucinations utilizing contextual grounding and Automated Reasoning checks. This means 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 purposes.
• Model analysis – You can consider and evaluate fashions to determine the optimum mannequin to your use case, together with DeepSeek-R1, in a number of steps by both automated or human evaluations by utilizing Amazon Bedrock mannequin analysis instruments. You can select automated analysis with predefined metrics similar to accuracy, robustness, and toxicity. Alternatively, you possibly can select human analysis workflows for subjective or customized metrics similar to relevance, model, and alignment to model voice. Model analysis offers built-in curated datasets, or you possibly can herald 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 sturdy safety to your generative AI purposes. To study extra, go to Protect your DeepSeek mannequin deployments with Amazon Bedrock Guardrails and Evaluate the efficiency of Amazon Bedrock sources.

Get began with the DeepSeek-R1 mannequin in Amazon Bedrock
If you’re new to utilizing DeepSeek-R1 fashions, go to the Amazon Bedrock console, select Model 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.

Next, to check the DeepSeek-R1 mannequin in Amazon Bedrock, select Chat/Text underneath Playgrounds within the left menu pane. Then select Select mannequin within the higher left, and choose DeepSeek because the class and DeepSeek-R1 because the mannequin. Then select Apply.

Using 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 can 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. If they want $1,000 for emergency bills in the course of 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 can too entry the mannequin utilizing code examples within the AWS Command Line Interface (AWS CLI) and AWS SDK. You can use us.deepseek.r1-v1:0 because the mannequin ID.

Here is 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 following Python code examples present learn how to 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 Region 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"

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

attempt:
    # Send 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: Can't invoke '{model_id}'. Reason: {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, in the event you filter for “politics” phrase, your guardrails will acknowledge this phrase within the immediate and present you the blocked message.

You can check the guardrail with totally different inputs to evaluate the guardrail’s efficiency. You can 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 Stop 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 Learning Blog channel.

Here’s a demo walkthrough highlighting how one can benefit from 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 Regions by cross-Region inference. Check the full Region listing 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 attempt within the Amazon Bedrock console right this moment and ship suggestions to AWS re:Post for Amazon Bedrock or by your normal AWS Support contacts.

— Channy

Updated on March 10, 2025 — Fixed screenshots of mannequin choice and mannequin ID.