Auto-labeling has a pricing problem.
Most platforms meter it with credits: one credit here, half a credit there, and nobody can compute the real cost per thousand images.
There is a simpler architecture: bring your own GPU. You rent the compute with your own account, the job runs on open models, and your only cost is GPU time — at cost price.
This guide explains how BYO-GPU auto-labeling works, when it beats API credits, and how to set it up.
Short answer
BYO-GPU auto-labeling means:
- you connect your own GPU rental account (for example Vast.ai)
- pre-labeling and training jobs run on that GPU under your credentials
- you pay the raw GPU rate — typically a few dollars per hour — not per-image platform credits
- your images and model weights stay under your control
If you label in bulk or train regularly, this is usually the cheapest path. If you label 200 images once, a manual workflow is cheaper than any automation.
Why per-image credits get expensive
Foundation-model inference is genuinely cheap at list prices — auto-labeling 10,000 images with a VLM can cost $70-450 in raw API spend.
But platform credit systems rarely pass that through cleanly:
- credits bundle inference, storage, and platform overhead into one opaque unit
- conversion rates differ per feature, so predicting cost requires a spreadsheet
- burst usage (a big auto-label job) burns through a monthly allowance instantly
Raw GPU time is easier to reason about: a mid-range datacenter GPU rents for roughly $0.20-0.60 per hour on marketplaces like Vast.ai. An auto-label job that takes two hours costs about a dollar — regardless of how the platform would have "creditized" it.
How the BYO-GPU model works
The flow has four steps:
- Connect credentials — you link your own GPU rental account. The platform stores them encrypted and pins the credential version to each job, so a later credential change cannot silently move your workload.
- Pick hardware — browse live offers by price, VRAM, and location, and choose the instance that fits the model and batch size.
- Run the job — pre-labeling or training runs on that instance. Progress, logs, checkpoints, and failure states are tracked like any cloud job.
- Tear down safely — the instance auto-stops, outputs upload, and the GPU is destroyed. You keep the artifacts; the rental stops billing.
Usage, quota, and billing stay between you and the GPU provider. The platform never resells your compute.
Where the cost math favors each side
BYO-GPU wins when:
- you auto-label large batches repeatedly
- you fine-tune models on your own data
- you want predictable, auditable costs (one line item: GPU hours)
- data control matters — images flow to a GPU instance you rent, under your account
API credits or manual labeling win when:
- the dataset is small and one-off
- you need a frontier VLM that is impractical to self-host
- nobody on the team wants to think about instances at all
What about the active learning loop?
Auto-labeling is only half the loop. The full cycle is:
- pre-label with an open model on your GPU
- route low-confidence predictions into a review queue
- correct and approve
- fine-tune on the corrected data — again on your GPU
- use the fine-tuned model for the next pre-label pass
Each cycle raises pre-label acceptance rates and shrinks human effort. For the operational pattern, read active learning for data labeling and human-in-the-loop prelabeling.
Where LabelOp fits
LabelOp is built around the BYO-GPU model:
- Find GPU lets you search live Vast.ai offers by price, VRAM, and location, and check your rental balance
- Training jobs run on your own Vast.ai credentials — encrypted, version-pinned, with no platform fallback to operator credentials
- Checkpoints, metrics, and notifications are tracked in the workspace, with auto-stop and duration warnings so a forgotten job does not burn money
- The free tier includes Vast.ai connectivity with no add-on charge; your usage and billing stay on your Vast.ai account
See the model training feature for the job workflow.
For the AI-assist layer itself, LabelOp's SAM 2 smart annotation runs in-browser with no GPU at all — see in-browser AI annotation.
Setup checklist
Before your first BYO-GPU job:
- create a Vast.ai account and add a small deposit
- connect the credentials in LabelOp and validate them
- run a training or pre-label job on a small slice first
- confirm artifacts (weights, checkpoints) upload and verify
- set a max duration so runaway jobs stop themselves
Ten minutes of setup, then every future job reuses the same credential pin.
Final takeaway
Auto-labeling costs are knowable when you own the compute line item.
If your team labels in volume, bring-your-own-GPU turns "mystery credits" into "dollars of GPU time" — with better data control as a side effect.
FAQ
Do I need my own physical GPU?
No. You rent datacenter GPUs on a marketplace like Vast.ai with your own account. You get on-demand hardware without owning hardware.
Who pays for the GPU time?
You do, directly to the GPU provider. LabelOp does not mark it up or resell it — usage, quota, and billing stay on your account.
Is my training data safe on a rented GPU?
It is your instance under your account, and credential versions are pinned to each job. For strict regimes, check the provider's data policies and destroy instances after jobs — which the workflow does automatically.
What models can I run?
Open-weight models (detection, segmentation, and fine-tuning jobs) are the natural fit. Frontier closed models that only exist behind APIs remain a job for API credits.
How does LabelOp handle GPU jobs?
LabelOp connects your Vast.ai credentials, offers live GPU search, runs training jobs with pinned credentials, tracks checkpoints and metrics, and auto-stops instances — all included from the free tier.