To optimize the cost-performance ratio when using AWS Bedrock, focus on three key areas: model selection, configuration tuning, and usage patterns. Start by matching the model to your use case. For example, if you need basic text generation for a chatbot, Claude Instant (from Anthropic) costs less per token than Claude 2 but still delivers adequate performance. For complex reasoning tasks like code analysis, investing in a larger model like Claude 2 or Jurassic-2 Ultra (AI21 Labs) might yield better results with fewer retries, offsetting the higher per-token cost. Benchmark models using Bedrock's On-Demand mode first to compare accuracy and token usage before committing to Provisioned Throughput.
Next, adjust generation parameters to reduce unnecessary token consumption. Lower the max_tokens limit to cap response length—for example, setting it to 500 instead of 800 prevents overlong outputs. Use a lower temperature (e.g., 0.2-0.5) for deterministic tasks like data extraction to minimize retries, reserving higher values (0.7-1.0) for creative use cases. Implement stop_sequences to truncate responses at logical endpoints (e.g., stopping at "###" in Markdown). For JSON output, enable constrained generation features if available to reduce parsing errors and retry costs.
Finally, optimize usage patterns. Cache frequent queries (e.g., common customer support responses) to avoid reprocessing. Batch multiple requests where possible—some models process parallel inputs more efficiently. Monitor token usage with CloudWatch metrics to identify cost outliers. For high-volume workloads, consider Provisioned Throughput for discounted rates, but validate consistency first using On-Demand. Implement fallback logic to cheaper models when acceptable—for example, use Titan Text for simple summarization but switch to Claude for complex Q&A.