High Cost Question 220 of 223

How do you control LLM API spend in production?

GenAI / LLM · Speak this in 60–90 seconds · Faridabad & Delhi NCR

The model does not read letters like humans. It reads these pieces, then predicts the next one.

Cache frequent prompts, trim context, pick smaller models for easy tasks, and set budgets with alerts.

LLMs use tokens (not full words) because it helps them:

This is a process

question about Cost.

Panels listen for order,

trade-offs, and what you would actually do on a GenAI / LLM project - not buzzwords.

Stable token IDs

Each piece maps to a number the network can learn.

Fits the model

Fixed pieces are what transformers expect as input.

Before the model can read a sentence, it goes through these steps:

1
Cache frequent prompts, trim
context, pick smaller models for easy tasks, and set budgets with alerts.
2
Token counting per request
is basic hygiene.
3
Close with when you
would choose this approach on a real task.
4
Context mix
Attention looks at nearby tokens together.
5
Next token
The model scores what should come next.
6
Decode
IDs turn back into readable text.

Let's see how a real sentence is tokenized (tokens may vary by model):

Input text

“Token counting per request is basic hygiene.”

Tokenized output

Tokencountingperrequestisbasic

Token IDs (example)

2987408337471632900

Note: Actual tokens and IDs depend on the tokenizer (e.g., GPT, Llama, etc.).

Cache frequent prompts, trim context, pick smaller models for easy tasks, and set budgets with alerts. Token counting per request is basic hygiene.