Amazon Bedrock manages model updates and new versions by allowing providers to release updated models while giving users control over when to adopt them. When a provider like Anthropic or AI21 Labs releases a new model version, Bedrock makes it available as a separate version alongside existing ones. Each model is assigned a version identifier (e.g., anthropic.claude-v2:1), and users explicitly specify this identifier in API calls. This approach ensures existing applications continue using the older version without disruption, while new workloads can opt into the updated model. AWS handles backend integration, security validation, and compliance checks for new versions, ensuring they meet Bedrock’s standards before release.
To manage transitions, Bedrock provides flexibility for testing and gradual adoption. Users can evaluate new versions in staging environments by switching the model version in API requests, allowing performance comparisons without impacting production systems. For example, a team might test Claude 2.1 against Claude 2.0 by routing a subset of requests to the new version and analyzing accuracy or latency differences. Bedrock’s metrics and CloudWatch integration help track key performance indicators. AWS also communicates version deprecation timelines in advance, giving users months to migrate. This avoids forced upgrades and lets teams update code, retest, and deploy changes at their own pace.
Backward compatibility and documentation are prioritized to minimize friction. Older model versions remain accessible until deprecated, and AWS provides detailed release notes explaining changes (e.g., improved accuracy, new features, or input format adjustments). If a breaking change occurs, such as a modified API response format, Bedrock maintains the older version’s behavior until its deprecation date. For instance, if a new Cohere model version alters output schemas, users can delay adoption until they adjust their parsing logic. This balance between seamless updates and user control allows organizations to leverage improvements without sacrificing stability.