If your application requires a feature not currently supported by Amazon Bedrock, start by evaluating whether you can achieve the desired outcome using Bedrock’s existing tools in a creative way. For example, if Bedrock lacks a specific model architecture, explore whether combining multiple models or using custom prompt engineering could bridge the gap. Suppose you need fine-grained control over model outputs—like enforcing strict formatting rules—you might layer Bedrock’s API calls with post-processing logic. A developer could use Claude for text generation and then apply a separate script to validate or reformat the output. Similarly, if Bedrock doesn’t support a specialized task like document summarization with data extraction, you could chain multiple model calls: first summarize the text with Claude, then extract structured data using Titan’s embeddings or a custom regex-based parser. Always check AWS’s documentation and release notes for updates, as Bedrock frequently adds new models and features.
If workarounds within Bedrock aren’t sufficient, consider integrating complementary AWS services. For instance, if you need a model not available in Bedrock, deploy it separately using Amazon SageMaker and call it alongside Bedrock through an API gateway. Suppose your application requires fine-tuning a foundation model with custom data—a feature Bedrock doesn’t fully support today. You could train a smaller, task-specific model in SageMaker using Bedrock’s outputs as training data, then use both systems in tandem. Another approach is to use AWS Lambda for preprocessing data (e.g., breaking down complex queries) before sending requests to Bedrock, or post-processing results to meet specific requirements. For control over inference parameters like temperature or token limits—which Bedrock already supports for some models—ensure you’re leveraging all available API options before assuming limitations exist.
If neither Bedrock nor AWS services can address the gap, provide feedback to AWS and explore hybrid solutions. Use Bedrock’s feedback channels or AWS Support to request missing features, citing your specific use case. While waiting for updates, build a temporary architecture that combines Bedrock with third-party tools. For example, if Bedrock lacks a multilingual model you need, route non-supported languages to another provider’s API (like Azure or OpenAI) while using Bedrock for other tasks. Use an abstraction layer in your code to manage multiple providers, ensuring easy migration when Bedrock adds support. Always document these workarounds as technical debt to revisit later. If the limitation is critical, evaluate whether Bedrock remains the right long-term fit, but prioritize incremental improvements over full replatforming unless unavoidable.