To effectively load test a Bedrock-powered API, start by defining realistic performance goals and creating test scenarios that mimic actual usage patterns. Begin by identifying key metrics like requests per second (RPS), error rates, and response times under varying loads. Use tools like Apache JMeter, k6, or Locust to simulate traffic, ensuring your test scripts include common API operations such as model inference requests, data preprocessing, or post-processing steps. For example, if your API handles text generation, design tests that send prompts of varying lengths and complexities to assess how Bedrock’s backend models scale. Include ramp-up periods to gradually increase load and identify breaking points, and run tests in environments that mirror production (e.g., same AWS region and instance types).
Next, focus on distributed load testing to avoid bottlenecks from single-machine limitations. Tools like AWS Distributed Load Testing or Locust with worker nodes can spread traffic across multiple sources, simulating real-world user distribution. Monitor Bedrock-specific metrics via Amazon CloudWatch, such as ModelInvocationCount or ModelLatency, to track backend performance. For instance, if testing a high-concurrency chat application, measure how Bedrock handles simultaneous sessions while maintaining response consistency. Include error scenarios, like sudden traffic spikes or invalid input formats, to test resiliency. Capture logs and traces (using AWS X-Ray) to pinpoint issues—such as throttling due to service quotas or latency from inefficient model configurations—and validate retry mechanisms for failed requests.
Finally, analyze results iteratively and optimize based on findings. If response times degrade beyond acceptable thresholds, consider scaling Bedrock’s provisioned throughput or adjusting batching strategies for inference requests. For example, if a test reveals that 1,000 RPS causes 10% errors, investigate whether the errors stem from Bedrock’s rate limits or backend infrastructure. Use auto-scaling groups or AWS Lambda to dynamically adjust resources during peak loads. Re-run tests after optimizations to verify improvements, and document baselines for future comparisons. Share findings with your team to align on performance budgets, and automate load testing in CI/CD pipelines to catch regressions early. This structured approach ensures your API remains reliable under stress while leveraging Bedrock’s capabilities efficiently.