There's a common assumption in AI projects: if LLM costs are high, the model must be too expensive.
In practice, that’s rarely the real problem.
What we've seen (and what many teams discover the hard way) is that LLM costs don't explode because of model pricing. They explode because of architectural decisions.
Demos are cheap.
Production is not.
And the gap between those two is where most cost surprises happen.
The real drivers of LLM cost
When you move from experimentation to production, three things start to matter a lot:
1. How often you call the model
It sounds obvious, but frequency compounds quickly.
An extra call inside a loop, an unnecessary validation pass, or an agent making multiple internal calls can multiply your monthly cost without anyone noticing at first.
One clean architecture decision can mean the difference between 1 call and 5 per user action.
2. How much context you send
Tokens are the silent budget killer.
Sending full conversation history every time.
Passing entire documents when only a fragment is needed.
Appending system prompts that keep growing.
Context size directly impacts cost and in production systems, context tends to grow over time unless it’s intentionally controlled.
3. Whether you cache, route, or retrieve smarter
Not every request needs your most expensive model.
Not every request needs a model call at all.
- Can you cache repeated answers?
- Can you route simple queries to a smaller model?
- Can you retrieve first and only send the relevant chunks?
Cost optimization in LLM systems is rarely about negotiating model pricing.
It’s about designing smarter flows.
Why demos feel cheap (and production doesn’t)
In demos:
- You test with short prompts.
- You make a few manual calls.
- There’s no real traffic.
- There’s no retry logic.
- There are no edge cases.
In production:
- Users behave unpredictably.
- Prompts grow.
- Agents call other agents.
- Retries and fallbacks multiply usage.
- Traffic scales.
The model didn’t suddenly get expensive.
Your system just got real.
We recently summarized this idea in a short video as part of an ongoing series about LLM cost optimization and production architecture.
If you’re curious, here’s the reference.
How are you thinking about cost control in your LLM deployments? Are you measuring token usage per feature?
Would love to hear how others are approaching this.
Top comments (0)