We train the model.
You bring the data.
Custom LLM fine-tunes and ML models, trained on rented enterprise GPUs and billed as a fixed engineering fee plus a compute deposit — no racks to buy, no idle-hardware markup passed to you.
From open-weight LLM to production endpoint
Six capabilities that take “we have data and a use case” to a trained, evaluated, deployable model.
LLM Fine-Tuning
LoRA, QLoRA, or full fine-tunes of open models — Llama, Mistral, Qwen — trained on your data until it reliably does the one job you hired it for. Every run is tracked against a held-out eval set, not just vibes.
Talk to us about thisCustom ML Models
Classic ML done right — gradient-boosted trees, forecasting, anomaly detection, and NLP classifiers — for problems where a language model is slower, pricier, and less accurate than the right-sized model.
Talk to us about thisComputer Vision
Object detection, defect inspection, and classification models fine-tuned on your labeled imagery, benchmarked against your accuracy and latency targets before anything ships.
Talk to us about thisEmbeddings & Retrieval
Off-the-shelf embedding models miss domain vocabulary. We fine-tune retrieval encoders on your corpus so search, RAG, and semantic matching return the right result — not just a plausible one.
Talk to us about thisEvaluation & Benchmarking
We build the eval harness before we build the model: task-specific test sets, scoring rubrics, and regression checks, so 'good enough to ship' is a measured answer, not a guess.
Talk to us about thisDeployment & Serving
We package the trained model behind an inference endpoint — batched or real-time, autoscaled or fixed — with latency, cost, and drift monitoring wired in from day one.
Talk to us about thisWe rent the GPU. The cloud bills the second it starts.
Cloud GPU providers charge by the minute from launch to termination and don't extend us credit — so we can't front a training run without knowing it's funded. The deposit covers exactly that, nothing more.
- 1
Scope & estimate
A short technical call sizes the model, the data, and the method — LoRA, QLoRA, or full fine-tune — and turns it into a GPU-hour estimate and a fixed engineering fee.
- 2
Fund the deposit
You pay the engineering fee plus a compute deposit sized to that estimate. Nothing starts until it's funded — the same rule the GPU providers hold us to.
- 3
Train on rented GPUs
The job runs on cloud GPU capacity we rent for it — H100 or A100 class, chosen for the model size, released the moment training ends.
- 4
Deliver & reconcile
You get the weights, an eval report against your held-out set, and a reconciled invoice — unused compute refunded, any approved overage billed at cost.
You see the compute cost. You pay for the engineering.
We rent GPU capacity by the hour from cloud providers — we don't own racks, so there's no idle-hardware cost to pass on to you. A project starts with a fixed engineering fee plus a compute deposit sized to the job; unused compute is refunded, and any pre-approved overage is billed at pass-through cost. No markup on GPU time, ever.
Job class selected — figures are current market GPU rates, not a quote. Real projects budget for several training runs, not one perfect pass.
What we'd actually pay the cloud provider for one training run at that class — before engineering, data prep, or evals.
Pilot Fine-Tune
One model, one use case — proves the approach before you commit further.
- Classical ML, computer vision, or a ≤13B LoRA/QLoRA fine-tune
- Data audit + prep guidance
- Up to 3 training runs
- Held-out eval report
- Compute pass-through included up to $75
- Model weights delivered — yours to keep
Production Model
A model built to run in production, not just a promising notebook.
- 14B–70B LLM fine-tune, or a full classical ML pipeline
- Eval harness scored against your metrics
- Iterative tuning across multiple runs
- Compute pass-through included up to $400
- Deployed to a monitored inference endpoint
- 30-day tuning window after first delivery
Enterprise Program
Full fine-tunes, multi-model programs, or regulated/air-gapped delivery.
- 70B+ or multi-GPU full fine-tunes
- Reserved GPU capacity for predictable throughput
- Dedicated engineer + SLA
- On-prem / VPC / air-gapped delivery option
- Multi-model programs across teams
- Ongoing retrain & drift-monitoring retainer
Compute deposits are held against actual cloud GPU spend and reconciled after training — unused funds are refunded. Nonprofit and education discounts available. Talk to us.
A security company's take on model training
Most training shops rent the same GPUs we do. The difference is what happens to your data and your money while the job runs.
Ephemeral by design
Training runs on a rented GPU instance that's wiped the moment the job ends. Your data doesn't linger on hardware we don't own.
Never shared, never reused
Your data trains your model — full stop. No pooling across customers, no using your fine-tune to improve someone else's.
Compute you can audit
The GPU-hours a job burns are visible to you as it runs, billed at the rate the cloud provider actually charged — not a marked-up estimate.
Yours to keep
Model weights, training code, and eval harness are handed over at delivery. No lock-in to our infrastructure to keep using what you paid for.
One model, scoped and priced before we touch a GPU.
We start with a short scoping call: what the model needs to do, what data you actually have, and what “good enough to ship” measures as. That turns into a fixed engineering fee and a compute-deposit estimate — no open-ended retainer before you know the number.
From there we train against a held-out eval set you helped define, iterate within the funded budget, and hand over the weights, the eval report, and — if you want it — a monitored endpoint to call. Unused compute comes back to you; nothing about the bill is a surprise.
Find your weak points before attackers do.
Book a scoping call and get a threat-model of your environment within 48 hours — no obligation, no jargon.