Amazon Bedrock handles scaling automatically by default, leveraging AWS’s cloud infrastructure to adjust resources based on real-time demand. As a managed service, Bedrock abstracts away the underlying infrastructure, allowing users to focus on building applications without configuring servers or managing capacity. When traffic increases, Bedrock dynamically allocates compute resources to maintain performance, ensuring low-latency responses even during usage spikes. This serverless approach is similar to other AWS services like Lambda or API Gateway, where scaling is handled transparently behind the scenes. For example, if an application built on Bedrock suddenly experiences a surge in user requests—such as during a product launch or viral event—the service scales up to accommodate the load without requiring manual intervention.
While Bedrock’s default behavior is automatic scaling, users can optionally configure provisioned throughput for specific foundation models. Provisioned throughput guarantees a baseline level of capacity for high-priority workloads, which is useful for applications requiring predictable performance or compliance with strict SLAs. This is a paid feature that reserves compute resources for models like Claude or Jurassic-2, ensuring consistent response times even during regional outages or extreme demand. For instance, an enterprise running a customer service chatbot might use provisioned throughput to maintain reliable performance during peak hours, avoiding latency spikes that could impact user experience. However, most users rely on the default on-demand scaling, which bills based on actual usage without upfront commitments.
The choice between automatic scaling and provisioned throughput depends on workload requirements. Startups or experimental projects typically benefit from the hands-off, pay-as-you-go model, while large-scale production systems might combine both approaches. Bedrock also enforces service quotas (e.g., API rate limits) to prevent accidental overuse, though these can be adjusted via AWS support. For example, a developer building a prototype could start with automatic scaling and later add provisioned throughput as their application matures. This flexibility allows teams to optimize costs and performance as their needs evolve, without rearchitecting their implementation.