# Cloud and AI Cost Optimization | Nadya Construction Studio

> Lower AWS, Google Cloud, Azure and LLM bills safely: idle resources, rightsizing, commitments, storage tiers, data transfer, prompt caching and model routing.

Canonical: https://nadiaconstructionstudio.com/cost-optimization

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Cost optimization

# Smaller bills, *nothing broken.*

We find where your cloud and AI money goes, cut what is wasted, and leave budgets and alarms in place so it stays down.

[Start a project](#start)[See our work](https://nadiaconstructionstudio.com/#work)

## Where does cloud money usually go to waste?

- **Forgotten resources:** old environments, unattached volumes, idle load balancers, stale snapshots and test databases nobody turned off.
- **Oversized everything:** instances, databases and Kubernetes requests sized for a peak that never comes.
- **On demand for steady load:** paying list price instead of committed use discounts, Savings Plans or reservations.
- **No autoscaling:** servers running at full size overnight and on weekends; spot or preemptible capacity unused for batch work.
- **Data transfer:** NAT gateway charges, cross zone and cross region traffic, and egress that a CDN or a private endpoint would remove.
- **Storage:** everything kept in the most expensive tier forever, with no lifecycle rules.
- **Observability bills:** logs kept forever, high cardinality metrics and verbose tracing, often a surprise line on Datadog, CloudWatch or New Relic.
- **Old architecture:** always on servers for spiky work that would cost less as serverless, or the reverse.
- **No ownership:** untagged resources, so nobody knows which team or product a cost belongs to.

## How do you cut AI and LLM costs?

How much you save depends on your traffic, so we measure it before promising a number. These are the levers, roughly in the order we try them:

- **Send less:** trim prompts, retrieve only the passages that matter instead of whole documents, summarize long histories, and use long term memory so the same context is not resent on every call.
- **Cache:** prompt caching for repeated instructions and context, and response caching for questions that come up again and again.
- **Right size the model:** route simple steps to smaller, cheaper models and keep the large model only where evals show it earns its cost, with cascades that escalate only when the small model is unsure.
- **Distill and fine tune:** train a small model on the outputs of a large one for a narrow, high volume task, so it matches quality at a fraction of the price and latency.
- **Open models:** when volume is high and steady, run quantized open models on your own GPUs or a managed endpoint instead of paying per token.
- **Pay less per token:** batch APIs for work that can wait, and provisioned throughput or committed spend when usage is predictable.
- **Write less:** cap output length and use structured outputs so the model does not ramble.
- **Fewer steps:** cut agent loops, redundant tool calls and retries, which multiply every other cost.
- **Watch it:** meter every call by feature and customer, with budgets, alerts and a kill switch.

Every change is checked against evals, so quality is proven to hold before the cheaper path goes live.

## Will it break anything?

Not if it is done carefully. Every change is measured before and after, rolled out gradually, and reversible. For AI, quality is checked with evals before a cheaper model replaces an expensive one.

## How does it work?

We start with a read only review of your bill and usage, rank the savings by size and risk, and then implement them with you, biggest and safest first. Budgets, alarms and tagging stay in place so costs stay down.

Start a project

## Tell us what you want *built.*

A few lines is enough. The person who reads it is the person who builds it.

- A reply within one business day
- A written scope with price and date before any work
- Code, infrastructure and accounts in your name

Prefer email? [nam@nadiaconstructionstudio.com](mailto:nam@nadiaconstructionstudio.com)