Next live sessionThu, Nov 5 · 11 am ET · 8 am PT · 16:00 UKStarts soon
Spend less on AI tokens and get the same or better answers.
Cut your AI token spend, with better results is a free, one-hour live workshop. We run real requests on several models side by side, quality next to cost, and show where routing, caching and right-sizing save money while the answers hold.
What you'll get
- What one request really costs, task by task
- Which tasks a smaller model does as well
- The saving from routing, caching and right-sizing
11 am ET · 8 am PT · 16:00 UK · online, one hour, free
Teams we've built for
- Comic Relief
AWS Premier Tier Services Partner, one of about 300 among more than 130,000 AWS partners.
The most expensive model is rarely the best answer for every task.
A classification, a summary and a hard reasoning question don't need the same model. Most tasks have a smaller one that does as well, and you only know which by testing on your own requests.
Cutting the bill properly means quality measured next to cost, then routing, caching and right-sizing where the quality holds, and a test set that catches any change that quietly gets worse.
What you'll take away from the hour
- 01
Quality next to cost
We run your real requests on several models side by side and score each answer, so the choice of model rests on evidence.
- 02
Less spent per answer
Requests are routed, repeats come from a cache, and a smaller model takes a task wherever it's good enough, which keeps the big models for the work that needs them.
- 03
Results that hold
A test set built from your own questions keeps checking the cheaper choices, so none of them can get worse without anyone noticing.
Where the money goes, and the levers that move it
We work these out from your own usage export instead of a price list.
- 1
The workload's shape
Calls per prompt type, how long the context runs, tool-call fan-out, the model mix and the peaks.
- 2
Cost per request and per outcome
By team, with on-demand against provisioned throughput over twelve months and the crossover marked.
- 3
What a good answer is
For each task, a first test set from your own cases, and which tasks a smaller model would do as well.
- 4
The levers
Routing each task to the smallest model that passes, prompt caching, batching, shorter context and provisioned throughput.
- 5
The owner
Who owns the AI line, and budgets and caps by team.
The result is a cost per task table: what each task costs today, what it would cost at the same quality, and which model to use for it.
What we bring
The routing, the budgets and the cost by team already exist.
AK Run's Agent ControlPlane
Every AI agent's model calls go through one gate: only the models you approve, each call tied to the person who asked, cost tagged by team, and limits when you want them.
Runs in your own AWS account, on Amazon Bedrock AgentCore.
One hour, run by the engineers who do the work
See it
50 real requests run head to head on several models with Bedrock Evaluations, quality beside cost per task, then a router sending each request to the cheapest model that passes.
Where the saving is
Routing, caching, batching and right-sizing, and what each typically saves.
Your questions
Bring last month's AI bill.
The private workshop: a few hours on your own requests
We score 50 of your typical requests on several models, with quality next to cost for each, and list the changes worth making first.
Who comes
The engineering lead for the AI features, whoever owns the AI bill, and a product owner who knows what “good” looks like.
What to bring
Last month's usage export and bill, and 50 typical requests with personal data taken out.
Your data
Requests with personal data taken out; nothing else from your systems.
What you leave with
- The cost per task table
- What you'd save at the same quality
- Which model for which task
- A first test set to guard quality
What clients say about working with us
Armakuni's availability, ease of communication, cost-effectiveness, and technical skills were outstanding.
The Armakuni team has provided both technical and product consulting on AI & automation initiatives keeping us ahead of the curve in terms of technology and optimal architecture.
What sets them apart is their ability to move between technical execution and business-context problem-solving.
- Under 0.25%of AWS partners are Premier Tier. We're one of about 300.
- 500+AWS customer launches
- 1,300+engineers across the UK, US, UAE, India and Canada
- 70%of the proofs of concept we scope reach production
Before you sign up
What does it cost?
Nothing for the hour or the private session on your 50 requests.
What if we're already on the right models?
Then the cost table will show it, task by task, and you'll know the bill is right instead of guessing.
Will cheaper models make our answers worse?
Not if quality is measured first. Every change is scored against a test set of your own cases before it ships.
Do we have to move to Amazon Bedrock?
Not for the live session. The comparison runs on Bedrock, and if moving doesn't pay, we'll say so.
What happens after?
A benchmark on your own requests over two to three weeks, then live at the new price in about 90 days.
Cut your AI token spend, with better results
Thursday, November 5, 11 am ET · 8 am PT · 16:00 UK. Starts soon.
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