Why am I seeing higher costs than expected on my AWS bill for Bedrock usage, and how can I identify which requests or settings are causing it?

Higher AWS Bedrock costs than expected typically stem from usage patterns, model selection, or configuration settings. Bedrock charges are based on factors like the number of input/output tokens processed, specific model tiers used, and optional features like Provisioned Throughput. For example, using a large model like Claude-2 for simple tasks or processing high volumes of text without optimizing token limits can inflate costs. Additionally, Provisioned Throughput commitments (prepaid capacity) might be underutilized if your workload fluctuates, leading to wasted spend. Misconfigured retry logic in your code could also trigger unnecessary API calls, compounding expenses.

To identify the root cause, start with AWS Cost Explorer and filter by the Bedrock service. Break down costs by usage type (e.g., BedrockModelInvocationInputTokens or BedrockModelInvocationOutputTokens) to see which models or token categories dominate. Enable AWS CloudWatch Metrics for Bedrock to track invocation counts, token volumes, and errors over time. For granular debugging, enable Bedrock request logging via AWS CloudTrail. This lets you audit individual API calls, including model IDs, input sizes, and timestamps. For example, you might discover a background job using the expensive amazon.titan-text-premier model instead of the cheaper amazon.titan-text-express for non-critical tasks. Tagging resources (e.g., Environment=Production) in Bedrock API requests can also help segment costs by team or project.

To optimize costs, first review model selection. Use smaller models like Titan Embeddings for basic tasks instead of Claude-3 Opus for general text processing. Implement token limits in API requests—for instance, cap maxTokens to 500 instead of the default 4,096 if shorter responses suffice. Use caching for repetitive queries (e.g., product descriptions) to reduce API calls. If using Provisioned Throughput, align commitments with steady-state workloads and pair it with on-demand pricing for spikes. Set AWS Budgets alerts to trigger when daily Bedrock costs exceed thresholds. Finally, audit code for redundant API calls—a common issue is retrying failed requests without backoff logic, which can accidentally flood Bedrock with duplicate tasks.