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

As of January 30, DeepSeek-R1 fashions turned out there 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 strong guardrails and complete tooling for protected AI deployment. At present, 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 resolution.

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

In accordance with DeepSeek, their mannequin is publicly out there underneath MIT license and presents robust capabilities in reasoning, coding, and pure language understanding. These capabilities energy clever resolution 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 out there fashions like DeepSeek-R1, think about the next:

• Knowledge safety – You’ll be able to entry the enterprise-grade safety, monitoring, and price management options of Amazon Bedrock which are 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 use these key security measures by default, together with information encryption at relaxation and in transit, fine-grained entry controls, safe connectivity choices, and obtain varied compliance certifications whereas speaking with the DeepSeek-R1 mannequin in Amazon Bedrock.
• Accountable AI – You’ll be able to implement safeguards personalized 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 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 purposes.
• Mannequin analysis – You’ll be able to consider and examine fashions to determine the optimum mannequin on your use case, together with DeepSeek-R1, in a number of steps by way of both automated or human evaluations by utilizing Amazon Bedrock mannequin analysis instruments. You’ll be able to select automated analysis with predefined metrics akin to accuracy, robustness, and toxicity. Alternatively, you possibly can select human analysis workflows for subjective or customized metrics akin to relevance, type, and alignment to model voice. Mannequin analysis offers built-in curated datasets, or you possibly can usher in 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 strong safety on your generative AI purposes. To be taught extra, go to Defend your DeepSeek mannequin deployments with Amazon Bedrock Guardrails and Consider the efficiency of Amazon Bedrock assets.

Get began with the DeepSeek-R1 mannequin in Amazon Bedrock
In case you’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 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 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 be taught extra about utilization suggestions for prompts, consult 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 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., 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 'hey 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}'. Cause: {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, should you filter for “politics” phrase, your guardrails will acknowledge this phrase within the immediate and present you the blocked message.

You’ll be able to check the guardrail with completely different inputs to evaluate the guardrail’s efficiency. You’ll be able to refine the guardrail by setting denied subjects, 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 reap the benefits of the totally managed DeepSeek-R1 mannequin in Amazon Bedrock:

Now out there
DeepSeek-R1 is now out there totally 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. Examine the full Area listing 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 strive within the Amazon Bedrock console immediately and ship suggestions to AWS re:Publish for Amazon Bedrock or by way of your standard AWS Help contacts.

— Channy

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