Cut Your LLM Costs by ~30% With Prompt Optimization (What Actually Works in Production)

If you’ve shipped an LLM-powered feature to production, you’ve probably experienced this:

• The feature works
• Users like it
• Traffic grows
• Your AI bill quietly explodes

The problem is not usually one big mistake. It’s death by a thousand small inefficiencies: bloated prompts, wrong model choices, missing caching, and zero visibility into what’s actually driving cost.

Over the past months, while working on LLM-heavy applications, we consistently saw 20–40% of spend wasted. Not because models are bad, but because prompts and execution paths aren’t treated as production assets.

This post breaks down where LLM costs really leak and the practical prompt optimization techniques that actually reduce spend in real systems.

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Why LLM Costs Are Hard to Control

LLM pricing is simple on paper: you pay per token.

In practice, token usage is wildly unpredictable.

A single request cost depends on:

• Prompt length
• Context size
• Model choice
• Tool calls and retries
• Hidden system prompts
• User behavior

Once an app scales, these variables compound fast. Without instrumentation, most teams only notice the issue when the invoice lands.

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The “Hidden 30%”: Where the Money Goes

Here are the most common cost leaks we see in production systems.

Overly Verbose Prompts

Prompts tend to grow organically:

• Extra instructions
• Repeated constraints
• Historical context that is no longer relevant

Every additional token is paid every single request.

What works

• Ruthlessly trim instructions
• Remove redundant phrasing
• Prefer clear structure over verbosity
• Measure tokens per prompt version

Even small reductions add up at scale.

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Bloated Context Windows

A common anti-pattern:

“Just pass everything to the model and let it decide.”

Large context windows are expensive and often unnecessary.

What works

• Retrieve only relevant context (RAG with filtering)
• Cap historical messages
• Summarize long threads before reinjecting
• Avoid entire-database prompts

Context relevance matters more than context size.

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Using Frontier Models for Simple Tasks

Many systems default to a single top-tier model for everything:

• Classification
• Extraction
• Formatting
• Simple reasoning

This is convenient, but expensive.

What works

• Route tasks by complexity
• Use cheaper models for deterministic or shallow tasks
• Reserve frontier models for reasoning-heavy paths

def route(task, input):
    if task in SIMPLE_TASKS:
        return call_model("cheap-model", input)
    return call_model("frontier-model", input)

This alone can cut costs dramatically.

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No Caching or Reuse

LLMs are often called for:

• Repeated prompts
• Identical user actions
• Static system instructions

Without caching, you pay repeatedly for the same output.

What works

• Cache deterministic responses
• Cache embeddings
• Reuse prompt fragments instead of regenerating them
• Detect duplicate calls

Caching is one of the highest ROI optimizations.

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No Guardrails or Visibility

Most teams cannot answer:

• Which prompt is the most expensive?
• Which model drives most spend?
• Where retries or failures inflate cost?

Without observability, optimization is guesswork.

What works

• Track cost per request
• Track tokens per prompt
• Alert on abnormal usage
• Compare prompt versions over time

At some point, most teams realize the issue isn’t knowing what to optimize —
it’s not having a single place to see prompts, executions, tokens, models, and cost together.

Once prompts are scattered across code, config files, and notebooks, even simple questions like
“Which prompt is costing us the most this week?” become hard to answer.

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Prompt Optimization Is a Production Discipline

The key mindset shift is this:

Prompts are not static strings.

They are production assets.

That means:

• Version them
• Measure them
• Optimize them
• Roll back when needed

Treat prompt changes like code changes. The cost savings compound as usage grows.

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Where This Breaks Down at Scale

Most of the techniques above are straightforward in isolation.

What becomes difficult at scale is:

• Tracking prompt versions over time
• Correlating prompts with executions and cost
• Comparing models and optimizations objectively
• Enforcing guardrails across teams

This is where teams usually move from ad-hoc scripts and dashboards
to a dedicated LLM observability and cost-control layer.

For context, this is exactly the problem space we’re working on with Dakora —
giving teams a unified view of prompts, executions, models, and cost, so prompt optimization stops being guesswork and becomes measurable.

The goal isn’t more dashboards.
It’s making cost, performance, and prompt changes visible in the same place.

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A Simple Cost-Reduction Checklist

• Measure tokens per request
• Trim prompt verbosity
• Reduce unnecessary context
• Route tasks to cheaper models
• Add caching for repeated calls
• Track cost by prompt and model
• Set basic spend alerts

Most teams that apply these systematically recover ~30% of wasted spend without changing product behavior.

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Final Thoughts

LLMs are powerful, but they are not free.

As usage scales, prompt quality becomes a cost lever, not just a UX concern.

If you’re dealing with LLM cost surprises in production,
I’m happy to compare notes or walk through how other teams are approaching this.

You can explore what we’re building at https://dakora.io

feedback is very welcome.

Originally published on dakora.io