The full context is sent with every request
AI cost: model, RAG or local deployment
Compare three architectures under the same workload. A well-prepared knowledge base reduces context, enables smaller models and can make local deployment economically viable.
Retrieval sends only relevant passages
Saving against the baseline: 98%Data and compute stay within your environment
Saving against the baseline: 16%Calculation assumptions
30 days per month. Cloud without retrieval: $2 per million input tokens and $8 per million output tokens. Smaller model with RAG: $0.15 and $0.60 respectively. Indexing, storage and implementation are excluded.
Price does not determine quality. Before choosing a model, use a benchmark set to measure accuracy, answer completeness, citation quality and latency.
First measure retrieval and model quality on your own documents. Then compare total cost of ownership, including infrastructure, support, security, knowledge-base updates and answer controls.