AWS Bedrock vs Self-Hosted LLMs: When to Choose What

The shift toward Generative AI has forced cloud architects to move beyond traditional CRUD applications and grapple with a fundamental "Buy vs. Build" dilemma: should we leverage a managed service like AWS Bedrock, or should we self-host Large Language Models (LLMs) on infrastructure like Amazon SageMaker or Amazon EKS? This decision is no longer just about developer convenience; it is a complex trade-off involving latency requirements, data residency constraints, and long-term total cost of ownership (TCO).

In a production environment, the stakes are high. Choosing Bedrock offers a serverless velocity that can get a prototype to market in days, but as throughput scales to millions of tokens per hour, the pricing model might become a liability. Conversely, self-hosting provides the ultimate "knobs and dials" for performance tuning—such as model quantization and custom inference kernels—but requires a dedicated platform team to manage GPU orchestration, patching, and auto-scaling.

Architecture: Managed Abstraction vs. Infrastructure Control

The architectural difference between Bedrock and self-hosting lies in the "Control Plane." AWS Bedrock abstracts away the underlying GPU clusters, providing a standardized API for multiple foundation models (FMs) like Claude, Llama, and Mistral. Self-hosting, typically via Amazon SageMaker JumpStart or EKS with NVIDIA Triton/vLLM, requires you to manage the inference server, the model weights, and the scaling logic.

In the Bedrock model, your data stays within the AWS ecosystem but travels to a service-managed environment. In the self-hosted model, the model weights are loaded into your own VPC, providing a higher degree of isolation which is often a hard requirement for highly regulated industries like FinTech or Healthcare.

Implementation: Invoking Models at Scale

Interacting with AWS Bedrock is straightforward using the boto3 SDK. The complexity is shifted to prompt engineering and orchestration. Below is a production-ready example of invoking a model via Bedrock compared to the logic required for a self-hosted SageMaker endpoint.

AWS Bedrock Implementation (Python)

import boto3
import json

def invoke_bedrock_model(prompt_data):
    client = boto3.client(service_name='bedrock-runtime', region_name='us-east-1')
    
    # Example using Anthropic Claude 3 Sonnet
    body = json.dumps({
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 1024,
        "messages": [
            {
                "role": "user",
                "content": [{"type": "text", "text": prompt_data}]
            }
        ],
        "temperature": 0.5,
    })

    response = client.invoke_model(
        body=body,
        modelId="anthropic.claude-3-sonnet-20240229-v1:0"
    )
    
    response_body = json.loads(response.get('body').read())
    return response_body['content'][0]['text']

Self-Hosted SageMaker Implementation

When self-hosting, you aren't just sending a prompt; you are often managing the serialization of inputs for a specific inference server like vLLM.

import boto3
import json

def invoke_sagemaker_endpoint(endpoint_name, payload):
    runtime = boto3.client('runtime.sagemaker')
    
    # Self-hosted models often require specific tensor shapes or formats
    response = runtime.invoke_endpoint(
        EndpointName=endpoint_name,
        ContentType='application/json',
        Body=json.dumps(payload)
    )
    
    result = json.loads(response['Body'].read().decode())
    return result['generated_text']

Best Practices and Decision Matrix

Choosing between these paths requires evaluating specific operational pillars. The following table highlights the trade-offs based on real-world production deployments.

Performance and Cost Optimization

The cost structure of LLMs is often counter-intuitive. Bedrock is significantly cheaper for sporadic workloads or applications with low request volumes because you do not pay for idle GPU time. However, for a steady-state workload requiring high throughput (e.g., a 24/7 customer support bot), a self-hosted g5.2xlarge instance running a quantized Llama-3-8B model can be 40-60% more cost-effective than token-based billing.

To optimize Bedrock, utilize Provisioned Throughput for predictable workloads to ensure consistent latency. For self-hosting, leverage AWS Inferentia2 (inf2 instances) which offer better price-performance than standard NVIDIA GPUs for specific model architectures.

Monitoring and Production Patterns

Monitoring LLMs requires moving beyond CPU/Memory metrics into "LLM-native" observability. You must track Time to First Token (TTFT), tokens per second, and model hallucinations.

For Bedrock, AWS provides "Guardrails for Bedrock," which allows you to implement content filtering and PII masking at the service level. In a self-hosted environment, you must build these into your inference pipeline.

When self-hosting on EKS, it is critical to implement "Horizontal Pod Autoscaling" (HPA) based on custom metrics like request_latency or gpu_utilization rather than just CPU. Tools like KEDA (Kubernetes Event-driven Autoscaling) are essential here to handle the bursty nature of LLM traffic.

Conclusion

The decision to use AWS Bedrock versus self-hosting is rarely permanent. A common "Senior Architect" pattern is to start with Bedrock to validate the product-market fit and minimize engineering "undifferentiated heavy lifting." Once the model requirements stabilize and the token volume reaches a threshold where instance-based pricing becomes advantageous, the workload can be migrated to a self-hosted SageMaker endpoint or EKS cluster.

Choose Bedrock if your priority is speed to market, diverse model experimentation, and serverless operations. Choose self-hosting if you require deep model customization, strict VPC-only data isolation, or if your sustained throughput justifies the operational cost of managing GPU infrastructure.

References:

• https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html
• https://aws.amazon.com/sagemaker/jumpstart/
• https://aws.amazon.com/ec2/instance-types/g5/
• https://aws.amazon.com/bedrock/guardrails/