Progress Agentic RAG Tokenomics

Understand the true economics of Agentic RAG. This datasheet explains why a Progress Agentic RAG token represents a complete managed retrieval pipeline - not just model usage - combining search, orchestration, governance, citations and quality evaluation into a single retrieval-based unit.

Learn how retrieval-time pricing aligns costs with business value while eliminating hidden charges for embeddings, storage and LLM pass-through markups common in traditional RAG architectures.

Key Takeaways

• A token is more than an LLM call — each Progress Agentic RAG token covers hybrid retrieval, context assembly, multi-LLM orchestration, governance, citation tracking and quality scoring.
• Pay for answers, not data uploads — consumption is measured at retrieval time when value is delivered, rather than during ingestion or indexing.
• Simple, consolidated pricing — one billing unit with no embedding fees, no storage fees and no LLM pass-through markup.
• Enterprise-grade retrieval built in — includes governance controls, source-level citations and REMi quality metrics as part of every retrieval workflow.
• Compare total platform cost, not individual token cost — understand how managed RAG platforms bundle infrastructure, retrieval and AI orchestration differently, impacting overall economics.
• Designed for scale — aligns costs with ongoing knowledge consumption rather than one-time content ingestion