# Strata — Cut AI costs using the right model for each job

Strata helps companies cut their AI bills. We find the work that is being sent to an expensive model when a cheaper one would do, and move it onto open models you control. Typically that cuts AI costs 40 to 80 percent 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 will tell you.

- Website: https://heystrata.xyz
- Email: devagarwal076@gmail.com
- Book a call: https://cal.com/dev-agarwal-gpudao/15min

## The problem

Most teams send every request to their most powerful, and most expensive, model, because that is where they started. But most AI work is simple and repetitive. You do not need the most powerful model for it, and you are paying for it anyway.

- The bill grows with usage: every new feature adds requests, and you pay per request, so the bill grows with it.
- Routine work at premium prices: most traffic is simple, high-volume work a much cheaper model could do at the same quality.
- One provider, all the risk: prices change, models get retired and updates happen without warning, on someone else's schedule.
- Your data leaves the building: customer records, contracts and code are processed on third-party infrastructure by default.

### The same workload, routed differently

| Line | 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 |

That is an illustrative saving of 60 percent. The audit replaces these numbers with your real traffic.

## What we look at

If it runs on an AI API today, we can test it and usually send part of it somewhere cheaper:

- 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 and knowledge: answers over your own documents, wikis and databases.
- Sorting and 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 and summaries: meeting notes, report summaries, product copy and translation.
- Coding assistants: code generation, review and documentation inside your tools.
- Voice and calls: call transcription, summaries and voice agents.
- Agents and automation: multi-step workflows that call AI many times per task.

## Case studies: companies that migrated in 2026

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 moved 40 percent of internal AI queries onto open models, heading to 60 to 70 percent. Source: https://about.att.com/blogs/2026/the-tokenomics-equation.html
- Lindy moved 100 percent of its agent traffic from Anthropic to DeepSeek V4 and says it is saving millions. Source: https://thenewstack.io/lindy-deepseek-anthropic-switch/
- Swan replaced Claude across its agent workloads with MiniMax and GLM. A roughly $113K per month bill fell about 80 percent. Source: https://swan-ai.beehiiv.com/p/113k-the-invoice-that-went-viral-and-the-story-nobody-told-correctly
- Coinbase sent everyday work to Kimi and GLM, kept premium models for hard problems, and roughly halved its AI spend. Source: https://fortune.com/2026/07/26/china-moonshot-deepseek-zai-kimi-challenging-us-ai-cost/
- Thomson Reuters built its own model on an open foundation with proprietary legal data, investing about $40M. Source: https://www.thomsonreuters.com/en-us/posts/innovation/how-we-built-thomson/
- Harvey post-trained Kimi K3 into a legal model it controls. Source: https://www.harvey.ai/blog/post-training-update-harvey-tenet
- Cursor built Composer 2 on a Kimi base for top-tier coding performance at a better cost. Source: https://cursor.com/blog/composer-2-technical-report

Also moving, per 2026 reporting: Airbnb runs Alibaba Qwen for customer-service workloads; Siemens is adopting DeepSeek and Alibaba models; Deloitte launched a dedicated Open Model Engineering practice.

Open-weight is not the same as open-source licensing; terms vary by model.

## Services

We do not build random chatbots. We check what you are already running, send each job to the cheapest model that is good enough, run the busy ones on your own machines, and keep improving them with your data.

1. Assess — Local AI Audit (start here) and AI Cost Optimization.
2. Route — Intelligent Model Routing and AI Migration.
3. Run — Private AI Deployment and Inference Infrastructure.
4. Improve — AI Evaluation and Observability, and AI Post-Training.

## 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, quality, typical and worst-case speed, hardware, privacy and scale. You get a cost comparison, 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), and a plan with what you would save in the first 90 days.

No forced migration. If it does not pay off, we will say so.

## How it works

1. Pick one workload. Send the workflow, your provider, rough monthly spend, and where the data lives.
2. We test it. We replay your real prompts and examples through your current model and suitable open models.
3. You get a decision. A one-page answer on cost, quality, speed, hardware, privacy and risk.
4. We route, own, or stop. If it pays, we build the router, migrate the workload and deploy it privately.

## When you should keep renting

We are engineers, not resellers. Moving work only pays off under specific conditions.

Keep renting if your volume is low or spiky; your tests show only the top paid models are good enough; the workload is early and changing; or you would rather spend engineering time on product.

Route or own if you have steady, high-volume use; an open model is good enough on your tests; two or more jobs can share the same machines; or the data cannot leave and you need steady, predictable speed.

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.

## Why Strata

Objective engineering, not a reseller.

- Business first: we start with your cost, quality and constraints, not a model we want to sell.
- Measurement over opinion: every recommendation is backed by tests, benchmarks and a real cost model.
- No lock-in: open models, standard connections, your machines.
- Private by default: your private cloud or your own machines.
- Honest about tradeoffs: sometimes the best answer is to keep your current provider.

## Technology

- Open models: Qwen, DeepSeek, Kimi, GLM, MiniMax, Nemotron, Llama, Mistral.
- Running them: vLLM, SGLang, NVIDIA, Docker, Kubernetes, Hetzner.
- Checking the work: Langfuse, OpenTelemetry, Ragas, a standard model gateway.

## Frequently asked questions

### Will cheaper open models actually match the quality I get today?

For the tasks that make up most of your bill, such as support, sorting, summarising and extracting, usually yes. The newest open models 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.

### What is model routing?

Think of it as a switchboard. Every request is checked and sent to the cheapest model that is good enough: an open model for everyday work, a paid premium model for the hard cases. Most companies get 40 to 80 percent of the savings from this alone.

### What if keeping the API is the right answer?

Then we tell you, in writing, and you keep your setup. If the numbers do not justify a change, we will not invent a reason to sell you one.

### Are open models free to use however I want?

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 cannot leave. Nothing is sent anywhere without your approval.

### 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.

## Contact

Send us one AI job you are paying for. We will look at the cost, the quality and what it takes to change it, and tell you honestly whether it is worth moving.

- Book a 15-min call: https://cal.com/dev-agarwal-gpudao/15min
- Email: devagarwal076@gmail.com