How do I determine if an issue is on the Amazon Bedrock service side (for example, a service outage) versus an issue in my own implementation?

To determine whether an issue stems from Amazon Bedrock or your own implementation, start by isolating the problem through systematic checks. First, verify the status of Amazon Bedrock using the AWS Service Health Dashboard or the Personal Health Dashboard in your AWS account. These tools provide real-time updates on service outages or degraded performance. For example, if Bedrock’s API endpoints are experiencing high latency or errors, AWS typically flags this in the dashboard. Additionally, test Bedrock’s core functionality independently of your code. Use the AWS CLI or SDK to run a basic API call, such as listing available foundation models (bedrock list-foundation-models). If this fails, it suggests a service-side problem. Check CloudWatch metrics for Bedrock-specific errors (e.g., 5xx HTTP status codes) and cross-reference these with your application logs to identify patterns.

Next, rule out issues in your implementation by validating your code and configuration. For instance, authentication errors (e.g., AccessDeniedException) often indicate misconfigured IAM roles or permissions rather than a Bedrock outage. Temporarily hardcode credentials or simplify your code to eliminate variables like dynamic parameter generation. Test retries with exponential backoff: if transient errors persist despite retries, this could point to a service issue. Validate your API requests against Bedrock’s documentation—common mistakes include incorrect modelId values, malformed input formats (e.g., invalid JSON in inference requests), or exceeding rate limits. For example, a typo in the region parameter when initializing the Bedrock client will cause connection failures unrelated to the service itself. Reproduce the issue in an isolated environment, such as a minimal script that invokes Bedrock without your application’s business logic.

Finally, leverage community resources and AWS support. Check AWS’s official forums, GitHub issues, or sites like Downdetector for reports of similar problems. If other users confirm outages, the issue is likely on AWS’s side. If not, gather evidence like AWS request IDs, timestamps, and error messages to open a support case. For example, a ThrottlingException might be caused by your account’s rate limits, but AWS support can confirm if the limit is enforced correctly. If you’ve ruled out configuration, code, and account-specific issues, escalate to AWS with detailed logs. Remember, service-side problems are rare but possible—methodically eliminating your own code as the source ensures you address the root cause efficiently.