Dynamic LLM Routing: How Beekeeper Automates Model Selection with Amazon Bedrock
Coverage of aws-ml-blog
In a recent case study, the AWS Machine Learning Blog explores how Beekeeper implemented a dynamic evaluation system to manage the rapid evolution of Large Language Models (LLMs) and improve user personalization.
In a recent post, the AWS Machine Learning Blog details how Beekeeper, a digital workplace platform connecting frontline workers, has architected a solution to one of the most persistent challenges in Generative AI: the rapid obsolescence of models and prompts. As the pace of LLM development accelerates, organizations often struggle to determine which specific model offers the best balance of performance, cost, and personalization for their specific use cases.
The Context: The Volatility of Model Selection
For engineering teams, the current AI landscape presents a moving target. A model selected today might be outperformed by a cheaper, faster, or more capable alternative next week. Furthermore, a prompt that works exceptionally well on one model may fail on another. For mid-sized companies without vast R&D resources, manually re-evaluating and re-integrating new models for every feature update is operationally unsustainable. This creates a bottleneck where production systems lag behind the state-of-the-art capabilities available in the market.
The Gist: Continuous Evaluation and Dynamic Routing
Beekeeper addressed this by moving away from static model integration. Instead, they utilized Amazon Bedrock to build a system that treats "model + prompt" pairs as competing candidates. Rather than hard-coding a single LLM, the system continuously evaluates various combinations of models and prompts against specific performance metrics.
According to the post, these candidates are ranked on a live leaderboard. When a frontline worker interacts with the Beekeeper app, the system dynamically routes the request to the highest-ranking "model + prompt" combination currently available. This approach allows Beekeeper to swap underlying models or refine prompts without disrupting the user experience or requiring code deployments. It effectively decouples the application logic from the specific AI provider, ensuring that the personalization engine remains optimal even as the underlying technology shifts.
Why This Matters
This architecture represents a mature approach to LLMOps (Large Language Model Operations). By automating the evaluation and selection process, Beekeeper ensures that their personalization features for frontline workers remain robust and cost-effective. For enterprise leaders, this case study offers a blueprint for managing AI infrastructure that is resilient to market volatility.
To understand the technical specifics of their ranking methodology and Bedrock integration, we recommend reading the full analysis.
Read the full post on the AWS Machine Learning Blog
Key Takeaways
- Dynamic Routing Over Static Selection: Beekeeper replaces static model choices with a system that dynamically routes requests to the best performing model at any given time.
- The 'Model + Prompt' Unit: The system evaluates combinations of prompts and models together, acknowledging that prompt performance is highly dependent on the specific model architecture.
- Live Leaderboards: Continuous evaluation creates a ranked list of candidates, allowing the application to automatically utilize the optimal solution without manual intervention.
- Operational Efficiency: This approach allows mid-sized engineering teams to leverage the latest advancements in AI without the overhead of constant manual testing and integration.