RAG vs CAG vs KAG: A Token Savings Deep Dive on AWS Bedrock

1. 1Run a cost analysis of RAG, CAG, and KAG architectures on AWS Bedrock
2. 2Configure each architecture to optimize token usage
3. 3Test and compare the token savings of each architecture
4. 4Apply the most cost-effective architecture to your LLM deployment
5. 5Monitor and evaluate the cost savings over time

Machine learning engineers and architects can benefit from this comparison to optimize their LLM deployments and reduce costs

💡 RAG, CAG, and KAG architectures have different token usage patterns, affecting LLM costs

💡 Reduce LLM costs by comparing RAG, CAG, and KAG architectures on AWS Bedrock