Cost-Governed RAG Enables Unified Per-Tenant Cost Attribution
Summary
A new architecture called Cost-Governed RAG addresses the critical gap in enterprise LLM deployments by providing unified per-tenant cost attribution across both retrieval and generation layers. It integrates a codebook-oblivious vector index (TurboVec) with a multi-tenant LLM governance gateway, achieving high accuracy and significant cost reduction.
Why it matters
For organizations deploying multi-tenant RAG systems, this solution offers unprecedented transparency into operational costs, enabling fair billing, optimized resource allocation, and improved governance for AI services.
How to implement this in your domain
- 1Assess current RAG deployment costs, specifically identifying unattributed retrieval expenses.
- 2Investigate integrating a codebook-oblivious vector index like TurboVec for precise cost metering.
- 3Implement a multi-tenant LLM governance gateway to centralize cost attribution.
- 4Develop a unified observability stack to monitor and report per-tenant RAG costs.
- 5Evaluate the potential for retrieval infrastructure cost reduction by adopting this architecture.
Who benefits
Key takeaways
- Enterprise RAG deployments lack unified per-tenant cost attribution for retrieval and generation.
- Cost-Governed RAG integrates TurboVec and an LLM governance gateway to solve this.
- TurboVec's deterministic memory formula enables near-exact retrieval cost calculation.
- The system achieves high cost attribution accuracy and significant infrastructure cost reduction.
Original post by Navnit Shukla
"arXiv:2607.12188v1 Announce Type: new Abstract: Enterprise Retrieval-Augmented Generation (RAG) deployments face a critical governance gap: while LLM generation cost is metered per token, the retrieval layer - vector memory, similarity compute, and embedding API calls - remains a…"
View on XOriginally posted by Navnit Shukla on X · view source
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