If Amazon BedRock experiences an outage or performance issues, the primary source for status updates is the AWS Service Health Dashboard (https://status.aws.amazon.com/). This dashboard provides real-time information about the operational status of AWS services, including Bedrock. Look for the “Machine Learning Services” section to check Bedrock-specific updates. Additionally, the AWS Personal Health Dashboard (available via the AWS Management Console) offers personalized alerts if your account is directly impacted. AWS also communicates service disruptions through their Twitter account (@AWSSupport) and enterprise support channels. If you have a support plan, you can open a case for detailed updates or mitigation guidance.
During an outage, your application should prioritize graceful degradation to minimize user impact. Start by implementing retry logic with exponential backoff for Bedrock API calls. For example, if a request fails with a 5xx error (like 503 Service Unavailable), wait 1 second before retrying, then 2 seconds, then 4 seconds, and so on, up to a maximum number of attempts. This avoids overwhelming the service during partial outages. If Bedrock remains unavailable, temporarily disable non-critical features that depend on it. For instance, if your app uses Bedrock for generative text tasks, you might fall back to a simpler rule-based response system or display cached results. Log errors aggressively to diagnose issues once the service recovers.
For long-term resilience, design your application to handle dependency failures. Use circuit breakers (e.g., via libraries like AWS’s SDK or frameworks like Hystrix) to halt requests to Bedrock after repeated failures, reducing latency and resource waste. Consider multi-region deployment if your use case allows it, though note that Bedrock’s model availability varies by region. If possible, cache frequent or predictable Bedrock outputs (e.g., common customer support responses) to serve during outages. Monitor Bedrock’s performance with Amazon CloudWatch metrics (e.g., ModelInvocationErrors) and set alarms to trigger automated fallback workflows. Finally, test failure scenarios using tools like AWS Fault Injection Simulator to validate your application’s behavior under stress.