How Couchbase built a multi-model AI architecture for Capella iQ with Amazon Bedrock | Amazon Web Services
This post is co-written with Tushar Madaan from Couchbase.
Building an AI-powered developer assistant that can generate database queries, recommend indexes, and support multi-turn conversational workflows requires more than a single large language model (LLM). It demands an inference architecture that is flexible, scalable, and resilient. As enterprise adoption of Capella iQ grew, Couchbase expanded its AI application to support multiple foundation model (FM) providers for greater flexibility, improved operational resilience, and alignment with diverse customer deployment preferences. Couchbase required a model-agnostic inference architecture that could scale through traffic bursts and maintain high availability across AWS Regions without pre-provisioned capacity.
This post describes how Couchbase adopted Amazon Bedrock to power Capella iQ with Anthropic’s Claude family of models, the architectural decisions behind their multi-model approach, and the operational benefits realized in production.
Solution overview
The following diagram illustrates the production architecture for Capella iQ’s integration with Amazon Bedrock.
Figure 1...
Copyright of this story solely belongs to amazon.com. To see the full text click HERE
