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Solution | Integration Complexity | Fully Managed | Comment |
|---|---|---|---|
Open AIOpenAI | Very High | No | Provide access to separate APIs that would allow us to build a RAG system, but it’s not a managed solution and would require a consistent amount of effort and maintenance to build. Custom GPTs would allow to build an application that integrates external knowledge for example from a search API. This might be worth investigating for “chatting with a project/folder”. |
Azure AI Search | High | No | See https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview. Similar to the Open AI solution, Azure provides all the tools needed (plus the search index management) to build a RAG solution but it would nevertheless be a big effort. |
AWS Bedrock | Medium | Yes | Provides a fully managed solution that is integrated with AWS, provide access to several open source models and does most of the heavy lifting. The knowledge base component is what enables the RAG system managing the storage and retrieval of external data. The default vector database is an AWS OpenSearch index. |
AWS Kendra | High | No | It’s a managed semantic search index (starts from $2k/month for 100K documents) that can potentially be used as part of a RAG system. Kendra can potentially be integrated into Bedrock as the backing document index. |
Vertex AI Search | High | Yes | Provides a (presumably) fully managed solution to deploy a RAG system using their own models. |
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