AWS Bedrock is a fully managed service, meaning it abstracts away all underlying hardware and infrastructure management. You cannot directly control or select specific instance types, GPUs, or hardware configurations for inference. Instead, AWS handles provisioning, scaling, and maintenance of the compute resources required to run the foundation models (FMs) available in Bedrock. This design simplifies deployment but limits customization—you interact with models via APIs, and Bedrock manages the rest. For example, if you use Claude 3 or Jurassic-2, you won’t configure whether it runs on GPU instances or CPU-based servers; AWS optimizes this based on the model’s requirements.
The underlying infrastructure impacts performance in two key ways. First, AWS’s choice of hardware (e.g., Inferentia chips, GPUs, or CPUs) directly affects inference speed and cost. For instance, a model optimized for AWS Inferentia might offer lower latency and cost compared to one running on general-purpose instances. Second, Bedrock’s multi-tenant architecture means performance can vary depending on regional demand and resource allocation. While AWS employs auto-scaling to handle traffic spikes, shared infrastructure in highly utilized regions might lead to occasional latency fluctuations. However, Bedrock’s Provisioned Throughput feature allows you to reserve capacity for consistent performance, which indirectly reflects how AWS allocates dedicated hardware for your workload.
Finally, Bedrock’s performance is also influenced by model-specific optimizations. For example, AWS might deploy larger models like Titan Image Generator on GPU clusters for parallel processing, while smaller text models run on cost-effective CPU instances. Network proximity (via region selection) and payload size (e.g., image generation vs. text completion) further affect latency. While you can’t tweak hardware, selecting the right region, using Provisioned Throughput, or choosing a model optimized for your use case (e.g., Amazon Titan for embeddings vs. Claude for complex reasoning) are the primary levers to influence performance.