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Tensovy
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EARLY ACCESS SIGNUPS OPEN

Train, own and deploy custom AI models. Without the pipeline work.

Tensovy is an autonomous MLOps agent. Describe what you want in plain English. It picks the architecture, provisions the GPU on managed infrastructure or your own cloud account, smoke tests and repairs its own training code before the long run, then hands you the downloadable weights and a registered model.

Get early accessSee how it works

Tell us the task you have in mind. Takes about a minute.

You keep the weightsManaged or your own cloudText and vision models
tensovy / invoice-extractor
Fine tune a model that pulls structured line items out of messy scanned invoices.
Recommending Qwen2.5 VL 7B with a LoRA adapter. Estimated VRAM 21 GB, so 1x RTX 4090 at about $0.44 per hour, on managed infrastructure or your own cloud account. Dataset profiled: 1,000 invoice scans with line item labels, ready to train.
LoRA r=16bf163 epochs
TRAINING LOSSstep 412 / 900
loss 0.318lr 1.4e-4eta 22mspend $0.31
> "extract line items from these invoices and return strict JSON"
  • RunPod
  • Hugging Face
  • Kaggle
  • Unsloth
  • LibreYOLO
  • PyTorch
The problem

Everyone wants a custom model. Almost nobody can afford the pipeline behind it.

01

The pipeline is the bottleneck

Getting from a good idea to a trained model means provisioning, dependency pinning, config plumbing and checkpoint wrangling. Most of that work is procedural, and most projects stall in it.

02

Runs die on the environment, not the idea

A dependency mismatch or an out of memory error kills the first fine tune. You lose the run, the checkpoint and the GPU hour you already paid for.

03

Platforms keep the model

Hosted training services hand back an endpoint, not weights. The thing you paid to create stays behind someone else's API, and leaving means starting again.

04

Last month's run is not reproducible

The config behind your best checkpoint lives in someone's terminal history. With no record of the run, the data and the metrics, every project starts from zero.

How it works

Five steps from a sentence to your own trained weights.

You stay in a chat window. The agent handles architecture selection, VRAM math, provisioning, smoke testing, training and export, and shows you its work at every step.

  1. 1

    Describe the outcome you want

    MODEL DISCOVERY

    Say what the model should do in a sentence. The agent picks a base architecture, estimates VRAM, and recommends the cheapest GPU tier that will actually hold the run.

  2. 2

    Bring a labelled dataset, or find one

    DATASET

    Upload JSONL, CSV or a labelled image folder, or search Hugging Face and Kaggle from inside the workspace. Every dataset is previewed and profiled so you see exactly what the trainer will see.

  3. 3

    Managed, or your own cloud

    PROVISIONING

    Run on Tensovy managed infrastructure, or connect your own cloud account and keep the provider relationship. Either way you see the hardware plan and the hourly cost first, and nothing starts until you approve it.

  4. 4

    Train with a safety net

    TRAINING LOOP

    Before the long run, the agent dry runs the package on the GPU. If it fails, it reads the traceback, fixes the code and reruns until it passes. Then you approve the launch and loss curves stream to your dashboard.

  5. 5

    Weights plus a receipt

    EXPORT

    Weights are pulled off the instance into your model registry with the config, dataset and metrics that produced them. Download them whenever you want. Shutting the instance down is one click, with the meter in view.

Want the detail on every stage?Read the full walkthrough
Why Tensovy

Four things that make custom models actually shippable.

$

Managed, or bring your own key

Run training on Tensovy managed infrastructure and get one bill, or connect your own cloud account and let your provider bill you directly with nothing added by us. Same agent, same workflow, same weights either way.

⟲

Self healing training code

Before you pay for a long run, the agent dry runs the package on the GPU. When it fails, it reads the traceback, patches the code and reruns until the smoke test passes. You do not debug CUDA at midnight.

◱

Text and vision in one place

Most tools pick a lane. Tensovy covers both in one interface, from a LoRA tuned Llama that emits strict JSON to a YOLO detector trained on your own labelled images.

⌗

Dataset workspace

Bring a labelled dataset and the workspace previews and profiles it, or search Hugging Face and Kaggle and import one in a step. You see every row the trainer will see before a GPU starts.

Self healing in practice

The bug that would have killed the run at 3am gets fixed before it starts.

Tensovy runs a smoke test on the GPU before the real run. It reads the traceback, patches the training package and reruns until the test passes, so the GPU hours you pay for go to training instead of debugging.

Dependency and code failures fixed by the agent, not by you
Live loss, VRAM and spend while the run is going
Every infrastructure action approved by you, shutdown in one click
smoke test logs, live
  1. [04:12:07] smoke test: python train.py --dry-run
  2. [04:12:19] ImportError: cannot import name 'SwizzleType' from 'torchao'
  3. [04:12:19] agent: traceback classified. pinning torchao<0.17, reinstalling
  4. [04:12:58] agent: rerunning smoke test
  5. [04:13:41] dry run passed. loss 0.213 vram 7.9/24.0 GB
  6. [04:13:42] hardware plan approved. launching training
  7. [04:13:44] step 1 loss 1.842, streaming to dashboard
Architecture

One pipeline, six stages, zero handoffs.

Every stage below runs inside the agent loop. The only things that wait on a human are the approvals that spend your money.

STAGE 01

Chat with the agent

Natural language goal in, architecture and budget plan out.

STAGE 02

Dataset workspace

Uploads, Hugging Face and Kaggle datasets previewed, profiled and staged for the run.

STAGE 03

Autonomous training

Config generation, smoke test self repair and live loss streaming.

STAGE 04

Weight export

Adapters and merged models as safetensors, detectors as PyTorch weights.

STAGE 05

Model registry

Every run recorded with its config, dataset and metrics.

STAGE 06

Serverless endpoint, roadmap

An OpenAI compatible endpoint served from your registered weights. Next on the list.

Compute

Run it on our infrastructure, or on your own.

MANAGED BY TENSOVY
We provision and run the GPU, so there is no cloud account to set up
One bill, with the hourly cost shown before anything starts
The weights are still yours, in the same registry, in standard formats
BRING YOUR OWN KEY
Connect your own cloud account and keep the provider relationship
Your provider bills you directly and Tensovy adds nothing to that rate
Existing credits and negotiated rates apply exactly as they already do

Both routes are in early access, and moving between them changes nothing about how you train or what you get back. Any hourly rate shown on this site is an illustration; real pricing depends on the GPU tier and, on bring your own key, on your provider.

For engineers

An agent that does the pipeline work, not the thinking.

Describe the task in chat and Tensovy writes the Unsloth or LibreYOLO training package, pushes your data, smoke tests it on the GPU, fixes what breaks and runs the job. Architecture, data and evaluation judgement stay with you, and every config, dataset and log stays inspectable.

Look inside the platform
For founders and operators

A custom model without a six month project.

Retrieval pipelines hit accuracy and latency walls, and a custom model usually means a long infrastructure detour before anything ships. Tensovy compresses that into a conversation, a review and an approval, so a first model is a week of work rather than a quarter.

Browse use cases
What you walk away with

Deliverables, not a dashboard you rent.

A registered model

Every run lands in your model registry with its metrics, so the weights you download are the weights you can trace.

runs / model_artifacts

The raw weights

Download the LoRA adapter or the merged model as safetensors. Run it on your own hardware if you ever leave.

.safetensors / .pt

The training package

The full trainer the agent wrote: config, data loader, model, callbacks and export. Readable, and yours to modify.

train.py / config.yaml

The training receipt

Config, dataset snapshot, loss history and total compute spend for every run you launch.

Reproducible
Common questions

Answers before you sign up.

Read all questions

Do I need to know PyTorch?

No. If you can describe the task and the output format you want, the agent handles config generation, tokenization and the training loop. If you do know PyTorch, the package it writes is yours to read and change.

Who pays for the GPUs?

That depends on the route. On managed infrastructure the compute is billed through Tensovy on a single invoice. With bring your own key, your provider bills your account directly and Tensovy adds nothing to that rate.

Can I take my model and leave?

Yes. Weights are downloadable at any time in standard formats. There is no proprietary wrapper around your artifacts.

Text models only?

No. Language models and computer vision models are both first class, including YOLO detectors trained on your own labelled images.

Early access

Get your first custom model shipped this quarter.

Tensovy is opening early access now. Tell us the task you want a custom model to do and we will tell you honestly whether it is a fit.

Get early access

Prefer email? hello@tensovy.com