Enterprise AI Infrastructure · Private Beta
Stop paying frontier prices for narrow AI workflows
TuneLLM runs inside your infrastructure and automatically distills your recurring Claude & GPT workflows into small fine-tuned models — the same quality on your benchmarks, at 10–20× lower inference cost.
Limited design-partner seats · No spam, just early access
↑ One workflow, distilled. The quality holds — the bill doesn't.
The problem
You're paying the frontier-model tax.
Frontier models are astonishing generalists — and that's exactly why they're the wrong tool for the narrow, high-volume workflows most enterprises actually run them on.
Narrow jobs, frontier prices
Translation, document parsing, tagging, creative generation — repetitive, well-defined work routed to trillion-parameter generalists priced for open-ended reasoning.
Bills that scale with your success
High-volume, recurring workflows quietly compound into $100k–$1M+ a month. Every new customer makes the model bill bigger — forever.
Fixing it takes an ML team
Fine-tuning your own models means data pipelines, GPU infrastructure, and eval harnesses — months of specialist work. So most teams just keep overpaying.
How it works
From system prompt to your own model — on autopilot.
No data pipelines, no GPU wrangling, no eval harnesses to build. If your team can write a system prompt, it can ship a distilled model.
Create a project
Paste the system prompt you already use and pick the metric that matters — accuracy, BLEU, structured-output validity. Your workflow, your yardstick.
Swap one API key
Point your existing calls at TuneLLM. We proxy to your frontier model exactly as before — zero disruption — while every request and response becomes training data.
Distillation runs itself
Once enough traffic accumulates, TuneLLM automatically fine-tunes a ~10× smaller model on your workflow, inside your infrastructure. No GPUs to babysit, no notebooks.
Switch when the numbers agree
You get a side-by-side benchmark against the frontier model, on your metric. Flip the route when it matches — the bill drops 10–20× overnight.
Why TuneLLM
Built for enterprises that can't ship data out.
Your infra, your weights
Deploys on-premise or in your private cloud. Prompts, data, and the models we train never leave your network — and the weights belong to you.
Benchmark-gated, never silently worse
Every distilled model ships with an eval report against the frontier model you use today. You switch on evidence, not vibes — and you can fall back instantly.
One platform, every narrow workflow
Spin up a project per workflow — translation, document parsing, classification, creative generation. Each one gets its own right-sized model.
No ML team required
If you can write a system prompt, you can run TuneLLM. Data capture, training, evaluation, serving — the whole pipeline is automated behind one interface.
FAQ
The questions every buyer asks.
Can a 10× smaller model really match Claude- or GPT-level quality?
On a narrow, well-defined workflow — yes. Frontier models are generalists; your workflow uses a thin slice of what you're paying for. Knowledge distillation transfers exactly that slice into a small model trained on your real traffic. And the switch is benchmark-gated: if the distilled model doesn't match the frontier model on your metric, you never move.
Where does it run? What about our data?
TuneLLM deploys as a self-contained platform inside your cloud account or data center. Prompts, responses, training data, and model weights all stay inside your network. Nothing is sent to us.
Which workflows are the best fit?
High-volume, recurring, well-defined ones: language translation, document parsing and extraction, classification and tagging, summarization, template-driven creative generation. Rule of thumb — if it runs thousands of times a day with the same system prompt, it's a fit.
Will this disrupt our existing setup?
No. From day one TuneLLM simply proxies to your current provider, so behavior is identical. Switching a workflow to its distilled model is a routing change you control — and you can fall back to the frontier model instantly at any time.
What does it cost?
We're onboarding early design partners first. Pricing scales with the inference savings we unlock — if your bill doesn't drop, we don't win. Book a demo and we'll walk you through it.
Your AI bill doesn't have to scale with your success.
We're onboarding a small group of design partners first. Book a demo and we'll reach out with early access, in order.
Book a Demo