If your application requires a feature or capability not currently supported by AWS Bedrock, start by evaluating whether the gap can be addressed through existing Bedrock features, AWS services, or adjustments to your application design. First, review Bedrock’s documentation and release notes to confirm the limitation isn’t already addressed in a recent update. For example, if a specific model like Llama 3 isn’t available, check if Bedrock’s existing models (e.g., Claude, Jurassic, or Titan) can be adapted to your use case with prompt engineering, fine-tuning, or post-processing. If fine-grained control over model behavior is the issue, explore Bedrock’s guardrails, inference parameters, or custom prefixes/suffixes to constrain outputs. For advanced scenarios, combine Bedrock with other AWS services—such as using Step Functions to orchestrate workflows or Lambda functions to preprocess inputs/postprocess outputs.
If Bedrock’s native capabilities are insufficient, consider integrating it with other AWS AI/ML services. For instance, use SageMaker to deploy custom models or open-source alternatives (e.g., Hugging Face models) and invoke them alongside Bedrock via API. If Bedrock’s access controls are too broad, layer AWS IAM policies or resource tags to restrict permissions at the application level. For real-time data processing, pair Bedrock with Kinesis or EventBridge to manage input pipelines. If Bedrock Agents lack specific tools, extend them with Lambda-based APIs or existing AWS service integrations (e.g., DynamoDB for data lookup). Be mindful of trade-offs: custom integrations may increase complexity, latency, or costs, so validate whether the workaround justifies the effort.
Finally, engage AWS support and the Bedrock team to request missing features. AWS often prioritizes updates based on customer feedback, so submit a feature request via the AWS console or your account manager. In parallel, explore temporary solutions—such as using Bedrock’s asynchronous inference API to handle rate limits or combining multiple model invocations to simulate finer control. If the limitation is critical and time-sensitive, evaluate hybrid approaches (e.g., running a subset of workloads on Bedrock and others via third-party APIs or self-hosted models). However, avoid lock-in by abstracting model interactions behind a shared interface, making it easier to migrate if Bedrock adds the feature later. Document your workarounds clearly to simplify future refactoring.