Why rag applications are expensive to run
RAG applications have unique cost profiles: large input tokens (retrieved documents stuffed into context) plus generation output. A single RAG query can consume 4,000-8,000 input tokens from retrieved chunks alone.
The core challenge
The generation step needs quality — users judge the final answer. But document summarization, chunk ranking, and re-ranking are preprocessing steps where economy models perform equally well.
How hybrid routing solves this
Route final answer generation through A-tier. Route document summarization, chunk scoring, and query expansion through value-tier. Embedding generation uses specialized models regardless. Typical savings: 50-65%. RAG synthesis benefits from Claude-class reasoning on the answer generation step while using value-tier for retrieval processing.
Cost comparison at scale
| Approach | Monthly cost (est.) | Quality |
|---|---|---|
| All-flagship (GPT-4o / Claude Sonnet) | $12,000-18,000 | Highest on every turn |
| All-economy (GPT-4o-mini / Haiku) | Low | Inconsistent on critical turns |
| Token Landing hybrid | $4,500-7,500 | High where users notice |
See full pricing comparison table for per-token costs across providers.
Getting started
Token Landing's API is OpenAI-compatible — migration is a base-URL swap. Define your routing policy (which endpoints get A-tier vs value-tier), set a quality floor, and start saving.