Guppy

    Cut GPU costs, with engineers by your side

    We pinpoint where your GPU spend is leaking, then design and implement the fix — together.

    Where is your GPU budget leaking?

    Most AI teams can cut GPU costs by 30–70%.

    Over-provisioned training instances
    Inference workers left running with no traffic
    Spot and low-cost GPUs left unused due to ops overhead
    70%

    Your GPU budget is leaking — for nothing

    OVERPROVISION35%
    IDLE WORKER28%
    UNUSED SPOT22%
    Effective use15%
    OVERPROVISION35%

    Over-provisioned training instances

    IDLE WORKER28%

    Inference workers left running with no traffic

    UNUSED SPOT22%

    Spot and low-cost GPUs left unused due to ops overhead

    Training and inference that survive spot interruptions

    Keep spot's price advantage — Guppy absorbs the interruption risk.

    Spot instances are cheap but can be reclaimed without warning
    Auto-resume via checkpointing and instant re-allocation
    Tune the recovery strategy per workload

    Inference workers scale automatically with traffic

    Workers spin up and down with request volume.

    Traffic-based autoscaling
    Auto downscale when idle
    Pay only for what you use
    Requests (QPS)
    180 req/s
    GPU Workers
    2 / 6 active
    worker-01running
    worker-02running
    worker-03idle
    worker-04idle
    worker-05idle
    worker-06idle
    Workers scale up with traffic and are reclaimed when idle — you pay only for what you use.

    Run with one line — no complex infra

    Engineers handle setup; you run with a single CLI command.

    1

    Run with one command

    No yaml or network configuration required.

    2

    Local environment auto-replicated

    Datasets and env vars synced to the remote.

    guppy — bash
    ~$ guppy run train.py
    → Detecting local environment...
    Python 3.11 · PyTorch 2.3 · CUDA 12.4

    Only what you need, only when you need it

    Training reclaims when it finishes; inference scales down when idle.

    Training: debug and test locally, run only the training itself on remote GPUs
    Auto-reclaim when training ends
    Env setup
    Env setup
    Local
    Coding
    Coding
    Local
    Debugging
    Debugging
    Local
    Training
    Training
    Remote GPU
    Review results
    Review results
    Local
    Local (no billing)
    Billed only during GPU use

    Source the world's cheapest GPUs

    Unified comparison across CSPs, neoclouds, and regional DCs — auto-matched to the lowest price.

    Unified comparison across 155 providers
    Preset-based auto-matching
    Compare trust class (A–C) and price together
    Connected Providers
    155+
    AWS
    GCP
    Azure
    Lambda
    CoreWeave
    RunPod
    Vast.ai
    Crusoe
    지역 DC
    ~$ guppy lease --preset a100x4→ matched $1.92/hr (RunPod)

    How to get started

    Step 01
    Sales
    contact@x10lab.team
    After a sales meeting, your Guppy Console account is provisioned
    Account provisioned

    Get a free diagnosis of where your GPU budget is leaking

    We review your current GPU usage together and deliver a report on where you can save.