01
The bill grows with usage
Every new feature adds usage, and you pay per request, so the bill grows with it — even as unit prices fall.
AI cost & ownership engineering
Own the AI that pays off. Rent the rest.
Strata finds the AI work you're overpaying for and moves it onto cheaper open models you control — typically cutting your AI bill 40–80% without lowering quality.
Start with one real job. You get a clear answer: keep paying as you are, do a mix, or run it yourself. If staying put is cheaper, we'll tell you.
- One workflow to start
- An answer, not a pitch
- You own what we build
Local AI Audit · verdict
Cost −68%
Quality 98% of baseline
Verdict Own it
Why now
- 80–90%
- cheaper to run on open models MIT, 2026
- 1 in 8
- big companies already run open models in production Vercel, 2026
- 75×
- cheaper for Shopify after moving one job to its own model Shopify, 2026
- $60M
- saved in a year by one AI support assistant Klarna, 2025
The problem
You're paying premium prices for routine work.
Most teams send every request to their most powerful — and most expensive — model, because that's where they started. But most AI work is simple and repetitive. You don't need the most powerful model for it, and you're paying for it anyway.
The same workload, routed differently
Save 60%| Today | After Strata | |
|---|---|---|
| Routine work 70% | $5,600 | $840 open model |
| Hard cases 30% | $2,400 | $2,400 premium model |
| Monthly total | $8,000 | $3,240 |
Illustrative example. The audit replaces these numbers with your real traffic.
02
Routine work at premium prices
Most of your traffic is simple, high-volume work that a much cheaper model could do at the same quality.
03
One provider, all the risk
Prices change, models get retired and updates happen without warning — on someone else's schedule, not yours.
04
Your data leaves the building
Customer records, contracts and code are processed on third-party infrastructure by default.
What we look at
Any AI workload you already pay for.
If it runs on an AI API today, we can test it and usually send part of it somewhere cheaper. These are the most common ones.
Customer support
Chat, email and ticket replies — with a human taking over the hard ones.
Documents
Invoices, contracts, forms and reports turned into clean, usable data.
Search & knowledge
Answers over your own documents, wikis and databases.
Sorting & routing
Classification of tickets, emails and leads, sent to the right place.
Data extraction
Pulling structured fields out of messy text, PDFs and images.
Writing & summaries
Meeting notes, report summaries, product copy and translation.
Coding assistants
Code generation, review and documentation inside your tools.
Voice & calls
Call transcription, summaries and voice agents.
Agents & automation
Multi-step workflows that call AI many times per task.
The 2026 record
The smartest teams don't pick one model. They route.
Open models now match premium quality on most everyday business work. These are 2026 migrations and routing shifts, with the numbers the companies reported.
AT&T Telecom · 100,000 employees Moved 40% of internal AI queries onto open models — heading to 60–70%.
AT&T had been using OpenAI and Anthropic for coding, support and internal workflows. In 2026 it moved to open models, including NVIDIA Nemotron, behind a system that picks a model by cost and performance. Its AI use tops a trillion tokens a month.
Source: AT&T · 2026 40% of AI queries now on open models
60–70% target share for open models
1T+ tokens / month
Lindy AI agents · Startup 100% of agent traffic migrated
Moved all of its agent traffic from Anthropic to DeepSeek V4 — and says it's saving millions.
Source: The New Stack
Swan AI agents · Startup ~80% lower model cost
Replaced Claude across its agent workloads with MiniMax and GLM. A ~$113K/month bill fell about 80%.
Source: Swan AI · 2026
Coinbase Crypto · Enterprise ≈50% cut in AI spend
Sent everyday work to Kimi and GLM, kept premium models for hard problems — and roughly halved its AI spend.
Source: Fortune
Thomson Reuters Legal · Enterprise $40M invested in an owned model
Open base Owned model
Built its own model on an open foundation with proprietary legal data — owning the intelligence instead of renting it.
Source: Thomson Reuters
Harvey Legal AI · Startup Kimi K3 open-model base for a legal model
Kimi K3 Harvey Tenet
Post-trained Kimi K3 into a legal model it controls, built on an open foundation.
Source: Harvey
Cursor Developer tools Kimi K2.5 base for Composer 2
Kimi K2.5 Composer 2
Built Composer 2 on a Kimi base — top-tier coding performance at a better cost.
Source: CursorAlso moving, per 2026 reporting
- Airbnb — runs Alibaba's Qwen for customer-service workloads
- Siemens — adopting DeepSeek and Alibaba models in its enterprise AI strategy
- Deloitte — launched a dedicated Open Model Engineering practice
Figures as reported by each company, its engineering team or the cited 2026 reporting. “Open-weight” is not the same as open-source licensing — terms vary by model.
Services
The ownership stack.
We don't build random chatbots. We check what you're already running, send each job to the cheapest model that's good enough, run the busy ones on your own machines, and keep improving them with your data.
Local AI Audit
Your real traffic, tested against open models on your data. Cost, quality, speed, hardware, privacy, scale — and which requests are on the wrong model.
→ A one-page verdict: keep renting, go hybrid, or own it.
AI Cost Optimization
We map what you spend by model and by job, then take the easy wins — sending each request to a cheaper model, reusing repeat answers — before moving anything to your own machines.
→ Lower recurring spend without changing your quality bar.
Intelligent Model Routing
A switchboard that sends each request to the cheapest model that's good enough — open models for everyday work, premium models for the hard cases.
→ Immediate savings, with premium quality kept for the hard cases.
AI Migration
We move the right jobs onto open models, testing quietly alongside your current setup so nothing breaks.
→ A drop-in swap your application barely notices.
Private AI Deployment
Run the models on your own machines or private cloud when privacy and steady costs matter.
→ AI that fits your infrastructure, not a vendor's.
Inference Infrastructure
We set up and tune the machines that run the models, for speed and cost.
→ Steady speed and capacity you control.
AI Evaluation & Observability
We test the models on your real cases and keep watching quality, cost and speed.
→ No surprises, and a defensible quality bar.
AI Post-Training
Fine-tune open models on your data and feedback, then measure whether the improvement paid off. No benchmark theatre.
→ Models that get better at your business.
Start here
The Local AI Audit.
Give us one AI job you already run. We test it against cheaper open models using your own examples — then tell you, in plain numbers, whether moving it makes sense.
We compare
- Cost what you pay now, and what you could
- Quality tested on your own examples
- Latency typical and worst-case speed
- Hardware what it takes to run it
- Privacy where the data can live
- Scalability your cost if you grow 10×
You get
- A cost comparison: what you pay today vs a cheaper setup
- A quality check on your own examples
- Speed tests
- What it takes to run, and the monthly cost
- A clear answer: stay, mix, or move
- A plan, and what you'd save in the first 90 days
A clear answer
- 01 Keep your current setup
- 02 Go hybrid
- 03 Bring it in-house
No forced migration. If it doesn't pay off, we'll say so.
How it works
From one workflow to a decision in days, not quarters.
No discovery theatre. No eight-week strategy deck. One workload, measured properly.
- 01
Pick one workload
Send the workflow, your provider, rough monthly spend, and where the data lives. That's enough to start.
- 02
We benchmark it
We replay your real prompts and examples through your current model and suitable open models — judged by the same standard.
- 03
You get a decision
A one-page answer on cost, quality, speed, hardware, privacy and risk. Stay, mix, or move — with the numbers behind it.
- 04
We route, own, or stop
If it pays, we build the router, migrate the workload and deploy it privately — then measure the savings. If it's a no, you've lost a few days.
The honest part
Sometimes you should keep renting.
We're engineers, not resellers. Moving work only pays off under specific conditions — and pretending otherwise would make us just another vendor.
Keep renting if…
- Your volume is low or spiky — serverless APIs are built for that.
- Your tests show only the top paid models are good enough.
- The workload is early and its shape is still changing.
- You'd rather spend engineering time on product than on serving infra.
Route or own if…
- You have steady, high-volume use, so a fixed setup pays off.
- An open model is good enough on your tests.
- Two or more jobs can share the same machines.
- The data can't leave, or you need steady, predictable speed.
| Pay as you go | A mix | Your own machines | |
|---|---|---|---|
| Cost at scale | Grows linearly | Lower | Mostly fixed |
| Control over the model | None | Partial | Full |
| Data privacy | Third-party | Mixed | In your walls |
| Worst-case speed | Variable | Handled | Predictable |
| Operational burden | None | Low | Yours |
| Time to first result | Hours | Days | Weeks |
| Best when | Low / spiky volume | Most workloads | Sustained, sensitive, high-volume |
Most teams end up with a mix: the bulk goes to a cheap open model, the hard cases go to a premium API, and you always keep a way out.
Do the rough math
What could you be saving?
Move the two sliders. The estimate assumes a mix: everyday work runs on cheaper open models, hard cases stay with your current provider.
$500$60k+
mostly hard reasoningmostly routine
Routine work is classification, extraction, summarisation, RAG and support. Those are exactly the tasks where open models now clear the bar — the hard cases stay on your current provider.
Estimated savings
$3,360–$4,760
per month, once routine volume runs on open models
Up to $57,120 a year
Get the exact numberIllustrative only. Real savings depend on your workload, quality bar and volume — the audit replaces this guess with your numbers.
Why Strata
Objective engineering, not a reseller.
We measure first, then recommend. The tools are the easy part — the decision is what's valuable.
- 01
Business first
We start with your cost, quality and constraints — not with a model we want to sell you.
- 02
Measurement over opinion
Every recommendation is backed by tests, benchmarks and a real cost model for your workload.
- 03
No lock-in
Open models, standard connections, your machines. You can leave, and that's the point.
- 04
Private by default
Your private cloud or your own machines. We design for the data that can never leave.
- 05
Honest about tradeoffs
Sometimes the best answer is to keep your current provider. We'll tell you that, in writing.
Technology
What we build with.
Open models · Your own machines · Tested, not assumed
Open models
The models that replace expensive API calls on routine work.
Qwen 
DeepSeek Kimi GLM MiniMax 
Nemotron 
Llama 
Mistral Running them
Fast, cheap and private, on your machines or ours.
vLLM 
SGLang NVIDIA 
Docker 
Kubernetes 
Hetzner Checking the work
How every request picks the right model, and how we know it works.
Langfuse OpenTelemetry Ragas Model gateway
Technology changes fast. Your business shouldn't have to change with it.
Questions
Straight answers.
Will cheaper open models actually match the quality I get today?
For the tasks that make up most of your bill — support, sorting, summarising, extracting — usually yes. The newest open models (DeepSeek, Kimi, GLM, Qwen, MiniMax) are close to the best paid ones, and the gap keeps closing. The top paid models still win on the hardest work, so the usual answer is a mix of both rather than swapping everything.
What is model routing, and why does it matter?
Think of it as a switchboard. Every request is checked and sent to the cheapest model that's good enough: an open model for everyday work, a paid premium model for the hard cases. Most companies get 40–80% of the savings from this alone, before moving anything to their own machines.
What if keeping the API is the right answer?
Then we tell you, in writing, and you keep your setup. The audit is designed to survive contact with reality: if the numbers don't justify routing or a migration, we won't invent a reason to sell you one.
Are open models free to use however I want?
No. “Open-weight” means you can download and run the model yourself. Not all of them have a fully open licence, so we check the terms and your use case before recommending one.
Is my data safe during the audit?
Yes. We work under NDA, ask for as little as possible, and can run the whole test inside your own environment if the data can't leave. Nothing is sent anywhere without your approval.
How long does this take?
A few days once we have your examples. If a change is worth making, the move itself usually takes weeks — not the six months a rebuild would take, because we're changing which model does the work, not rewriting your product.
Do you lock us into anything?
No. We use open models and standard connections, on machines you control. If you want to take it all in-house, you can. Lock-in is the problem we exist to solve.
How are you different from an AI agency?
We don't sell demos. We start by measuring the AI you already pay for and telling you the truth about it. If nothing should change, we say so — and then you're not a customer.
Next step
Find out what you can own.
Send us one AI job you're paying for. We'll look at the cost, the quality and what it takes to change it — and tell you honestly whether it's worth moving.