To effectively load test a Bedrock-powered API, start by defining clear performance goals and selecting the right tools. Establish metrics like requests per second (RPS), latency (p50, p95, p99), error rates, and concurrency limits. Use load-testing tools such as k6, JMeter, or Locust to simulate traffic, and integrate them with AWS CloudWatch to monitor Bedrock-specific metrics like ModelInvocationErrors or ThrottledEvents. For example, configure k6 to ramp up virtual users gradually (e.g., from 10 to 1,000 users over 10 minutes) while tracking how Bedrock’s API responds. Ensure your test scripts mimic real-world usage patterns, including variations in input size (e.g., short prompts vs. long documents) and model parameters (temperature, max tokens).
Next, design realistic test scenarios that mirror production traffic. If your API handles multiple Bedrock models (e.g., Claude and Titan), test each model separately to identify bottlenecks. Include edge cases like sudden traffic spikes or sustained high loads. For instance, use a script that alternates between synchronous and asynchronous Bedrock API calls, and validate if your system handles retries properly when Bedrock returns 429 (throttling) errors. Parameterize inputs to avoid repetitive prompts, as Bedrock’s performance may vary with input complexity. If your application uses Bedrock’s streaming responses, test how the system behaves under prolonged connections. Run tests in a staging environment that mirrors production AWS configurations (region, VPC, IAM roles) to avoid skewed results.
Finally, analyze results and iterate. Compare observed latency and error rates against your targets. If Bedrock throttles requests, work with AWS support to adjust service quotas or consider provisioning dedicated throughput. Look for infrastructure bottlenecks (e.g., EC2 instances or Lambda functions hitting resource limits) that could indirectly affect Bedrock API performance. For example, if your API gateway adds significant overhead, optimize its configuration or caching rules. Run multiple test cycles after adjustments, and document degradation points (e.g., “latency exceeds 2s at 500 RPS”). Share findings with your team to refine auto-scaling policies, error handling, or fallback mechanisms (e.g., queueing requests during peak loads).