LLM Engineering / LLM Cost Optimization
LLM Cost Optimization
LLM costs grow with every user, token, and retry — and often much faster than revenue. We find where tokens are wasted and reduce costs through model routing, caching, prompt trimming, batching, and smaller or self-hosted models, while measuring that quality holds.
This Is For You If...
Your OpenAI, Anthropic, or Gemini bill is growing faster than your users
You use the most capable model for every request
You don't know which features or customers drive token usage
AI costs make your pricing or margins hard to sustain
Which Area Matches Your Problem?
Ask Yourself | Points Toward |
Do you know where tokens go? | Usage Analysis |
Does every request use the largest model? | Model Routing |
Are similar requests repeated? | Caching |
Are prompts and contexts very long? | Token Reduction |
LLM Cost Services We Offer
Usage Analysis
Token and cost tracking by feature, customer, and model, so spend is visible and attributable.
Model Routing
Routing simpler requests to smaller, cheaper models and reserving large models for complex ones.
Caching
Provider prompt caching, response caching, and semantic caching where answers repeat.
Token Reduction
Shorter prompts, better retrieval, trimmed context, and batch APIs for non-urgent work.
What We Need From You
What to Share | Why It Helps |
Recent AI provider usage and bills | Shows where money goes |
Your AI features and prompts | Shows where savings are possible |
Quality expectations | Sets limits for savings |
Traffic patterns | Affects caching and batching |
Common Mistakes to Avoid
Instinct | Why It Doesn't Help | Do This Instead |
Switching to a cheaper model everywhere | Quality drops where it matters | Route by task difficulty |
Cutting cost without measuring quality | Savings hide regressions | Evaluate before and after |
Ignoring retries and failures | Wasted tokens add up | Fix causes of retries |
No per-feature tracking | Can't tell what's worth its cost | Attribute spend by feature |
How It Works
1. Measure
Token use by feature and model
2. Identify savings
Ranked by size and quality risk
3. Implement
Routing, caching, and prompt changes
4. Verify quality
Evaluations confirm results hold
5. Monitor
Cost dashboards and alerts
Why Codersarts
We cut costs and measure quality in the same process, so savings never come from quietly worse answers.
Turnaround Time
A cost analysis with ranked savings is typically delivered within a week. Implementation is scoped from the analysis.
FAQ
Should we self-host a model?
Sometimes, at high, steady volume. We compare total cost before recommending it.
Do you reduce cloud costs too?
Yes — see Cloud Cost Optimization.
How do you protect quality?
With evaluations — see LLM Evaluation.
Is our project kept confidential?
Yes. Your data, code, and results stay confidential, and an NDA is available on request.
Related Services
Is Your AI Bill Outgrowing Your Revenue?
Cut token spend while keeping answers just as good.