5 Concrete Ways to Cut AI Call Cost
Prompt-cache pass-through, tiered model routing, response trimming, real-time token attribution — no token markup, ~1/10 the cost.
What the community actually complains about
The most common cost complaints on Reddit and Zhihu: "the monthly invoice arrived and only then I found out we burned $10k," "60% of requests could have used a cheaper model but all hit GPT-4o," "the relay's markup is opaque." Per-key limits are useless for business decisions — you need real-time, attributable, haltable cost control, not after-the-fact reconciliation.
How GateLLM does it
GateLLM is licensed per 4GB instance (Pro $400/mo) — never a per-token markup, no platform fee. The real savings come from Composition: a top-tier model (Claude Opus) plans while open SOTA models (GLM / Qwen / DeepSeek) execute — same output at ~1/10 the cost. Token Ops gives per-request cost attribution and per-agent budget caps, so month-end bills hold no surprises.
5 levers that actually land
- Upstream prompt-cache pass-through with hit attribution — cache hits save money immediately
- Tiered model routing: lock plan / execute tiers, simple requests go to cheaper models
- Response trimming and stream truncation, cutting tail tokens
- Log retention policy: configurable retention and sampling, lowering storage cost
- Per-team / per-agent cost attribution and budget caps (that actually halt calls)