Quick win
Model routing
Simple tasks go to small, fast models; only the hard ones reach the frontier model. Usually the biggest single saving.
AT20 for AI Cost
Cut your AI bill. Keep the quality.
Most companies overspend on AI because every request goes to the most expensive model at full price. AT20's engineers route each task to the right model, stop you paying twice for the same text, and prove with tests that answers stay just as good. When data can't leave your walls, we run private models you own.
Right model for each taskQuality checked by testsPrivate AI when you need it
Where it goes
Most AI spend pays for waste, not work.
The bill grows with every new agent and every new user. Usually the cause isn't how much AI you use but how it's wired: one model for everything, the same text sent again and again, and no one watching the meter.
One model for everything
A frontier model sorting emails or pulling a date out of an invoice is like hiring a senior partner to do data entry.
Paying twice
Long instructions, policies and documents are sent, and billed, on every single call.
Real time, all the time
Overnight reports and bulk jobs run at full real-time prices when they could wait an hour for half the cost.
No meter
Nobody knows which team, agent or workflow is spending what, so nothing gets fixed.
Cheaper AI, same answers.
Model prices now differ by 10 to 100 times between the largest models and small ones that handle routine work just as well. The savings come from matching each task to the right model, reusing what you've already paid for, and proving with tests that quality holds before anything changes.
- TodayEvery request to the biggest model, at full price, unmeasured
- With AT20The right model for each task, cached and batched, with spend tracked per workflow
What we do
From quick wins to private AI.
We start with the changes that save money in weeks without new infrastructure, then go deeper only where your volume or your data rules justify it.
Quick wins: weeks, no new infrastructure
Build: smaller models trained for your tasks
Private AI: models you own and host
Quick win
Prompt caching
Instructions, policies and documents that repeat on every call are cached, so you pay full price for them once.
Quick win
Prompt and context trimming
Shorter instructions, only the relevant document passages, and sensible limits on answer length.
Quick win
Batch processing
Work that can wait, like overnight reports and bulk document runs, moves to discounted batch processing.
Quick win
Answer caching
Common questions are answered from a store of past answers instead of being generated again from scratch.
Quick win
Spend tracking and budgets
Cost per team, agent and workflow on one dashboard, with alerts and limits, so the CFO sees the savings.
Build
Better retrieval (RAG)
Accurate search over your documents lets a smaller model answer correctly instead of a large one reading everything.
Build
Fine-tuning
A small model trained on your examples learns your formats, terms and tone, and matches a large model on that task.
Build
Distillation
A large model teaches a small one to do a specific job, then the small one does it at a fraction of the cost.
Build
Quality tests
Automated test sets from your real work prove a cheaper model is good enough before anything is switched.
Private AI
Private LLM deployment
Open-weight models running in your own cloud account or data centre. Your data never leaves, and high volume costs less.
Private AI
Model compression
Quantization, pruning and distillation shrink models to run on smaller, cheaper GPUs or even ordinary servers.
Private AI
Inference tuning
Efficient serving, request batching and autoscaling, so GPUs work when there's demand and switch off when there isn't.
Private AI
GPU cost management
Right-sized instances, reserved and spot capacity, and the right provider for your workload and region.
Private AI
Edge and on-device AI
Small models on laptops, phones or branch servers for work that must be fast, offline or fully local.
Savings calculator
What could you save on your AI bill?
A rough estimate from the quick wins alone, using typical provider discounts. We measure your real numbers on the audit.
Estimated savings per year
$0
0% lower bill.
New monthly AI bill$0
AT20 Base plan, for comparison$12,000 / yr
Assumes small models cost about 80% less for routed tasks, cached text about 60% less, and batch work 50% less. Fine-tuning and private models can add more savings at high volume.
Book an AI cost audit →How it works
Measure first, change one thing at a time, and prove it.
- Audit your spend
We break down your AI bill by model, team and workflow, and find where the money goes.
- Quick wins
Routing, caching, trimming and batch processing go live first, usually within the first weeks.
- Prove quality
Test sets built from your real work confirm answers stay as good. Nothing switches without passing.
- Go deeper
Where volume or data rules justify it: fine-tuned, compressed or private models you own.
FAQ
Questions about cutting AI costs.
Will the quality of answers drop?
Not if it's done properly. We build test sets from your real work and only switch a task to a cheaper model once it passes. Tasks that need the strongest model keep it.
Do we have to change AI providers?
No. Most savings come from how you use your current providers. We work with the major model providers and with open-weight models, and can mix them.
When is a private LLM worth it?
When you run high, steady volume, or when data must stay inside your own systems, as in banking, healthcare and government work. Below that, well-tuned hosted models are usually cheaper.
What is fine-tuning, and do we need it?
Training a model on your own examples so it learns your formats and terms. It pays off for repeated, well-defined tasks at volume. We recommend it only when the numbers show it.
What does model compression do?
It makes a model smaller and faster, for example by storing it at lower precision, so it runs on cheaper hardware with little or no loss in quality for your tasks.
How do we know what we saved?
You get a spend dashboard by team, agent and workflow, compared with your starting point from the audit. Your engineer reviews it with you every week.
Get started
Book a 15-minute AI cost audit.
Tell us roughly what you spend on AI and what it's used for. We'll show you where the biggest savings are. Next, you'll pick a time right here on the page.