Why chatbots are expensive to run
Chatbots generate the highest token volume of any AI use case. Every conversation turn costs input + output tokens, and multi-turn chats compound quickly. Output tokens (3-5x pricier than input) dominate the bill.
The core challenge
You need flagship quality for user-facing replies but can't afford it on every single turn. System prompts, context summaries, and fallback responses don't need top-tier reasoning.
How hybrid routing solves this
Hybrid routing sends user-facing replies through A-tier (flagship) models while routing context compression, system prompt processing, and fallback responses through value-tier models. Result: 50-65% cost reduction with no visible quality drop in conversations. For chatbot deployments that need top-tier reasoning without flagship costs, see Claude-class alternative routing.
Cost comparison at scale
| Approach | Monthly cost (est.) | Quality |
|---|---|---|
| All-flagship (GPT-4o / Claude Sonnet) | $15,000-22,000 | Highest on every turn |
| All-economy (GPT-4o-mini / Haiku) | Low | Inconsistent on critical turns |
| Token Landing hybrid | $5,000-8,000 | 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.