Trainy GTM brain

Trainy sells GPU cluster scheduling, inference and experiment tracking tools to teams training and serving AI models.

category

GPU cluster orchestration

market profile

  • text: AI teams must keep expensive GPU clusters utilized while managing distributed jobs and unreliable hardware.
  • text: Trainy combines a managed scheduling layer with experiment tracking and model serving, while allowing tools to be used individually.

personas

  • title: Head of ML infrastructure
    pains:
    • Hardware faults interrupt expensive distributed training runs.
    • Supporting multiple clouds and maintaining Kubernetes diverts scarce infrastructure engineers.
  • title: Machine learning researcher
    pains:
    • Needs to launch multi-node training without debugging cluster setup.
    • Slow experiment tracking and lost metrics delay model iteration.
  • title: AI engineering leader
    pains:
    • Cannot easily attribute GPU spend by team or prioritize jobs across a shared cluster.

icp

  • text: AI startups and labs training models across multi-node GPU clusters, especially with reserved capacity or cloud credits.
  • text: Teams running GPUs across cloud providers or on bare metal that need a consistent scheduler.
  • text: Organizations with high-volume experiment tracking requirements and distributed training runs.

message pillars

  • text: Launch distributed training from an existing command.
  • text: Recover automatically from faulty GPU infrastructure.
  • text: Unify GPU scheduling across environments.
  • text: Expose compute allocation and cost to managers.

objections

  • objection: Why switch from Slurm?
    response: Compare isolation, observability and automatic fault recovery on an actual distributed workload.
  • objection: Will this require changing cloud or training code?
    response: Konduktor deploys in the customer's infrastructure and accepts existing torchrun commands; validate compatibility in a pilot.
  • objection: Is experiment data trapped in a vendor service?
    response: Pluto is described as open source and self-hostable, with compatible APIs and dual logging for migration.

competitors

  • name: NVIDIA Run:ai
    why: GPU orchestration and resource allocation.
  • name: SchedMD
    why: Developer of Slurm, a common workload scheduling alternative.
  • name: Anyscale
    why: Platform for distributed AI workloads.
  • name: Weights & Biases
    why: Competes with Pluto on experiment tracking.

influencers

  • name: Roanak Baviskar
    url: https://foundertrace.com/companies/trainy/
    why: Trainy co-founder and CEO focused on GPU infrastructure.

keywords

  • GPU cluster scheduler
  • Slurm alternative GPU
  • distributed training orchestration
  • GPU fault detection
  • multi cloud GPU scheduling
  • ML experiment tracking

voice

  • Direct and technical
  • Practical and developer-oriented
  • Quantified efficiency claims

signals

  • text: A team is expanding multi-node GPU training or moving beyond a single machine.
  • text: Repeated GPU failures or idle reserved capacity are driving training cost.
  • text: The team wants to migrate experiment tracking without interrupting existing logging.

stack

  • Kubernetes
  • PyTorch
  • Prometheus
  • Grafana
  • MCP

pricing

model: contact_sales

motion

hybrid

channels

  • GitHub
  • LinkedIn
  • X
  • Discord
  • content marketing

confidence

high

Founders

  • name: Roanak Baviskar
    title: Founder
    role: Founder
    bio: Studied CS & Mathematics at UC Santa Cruz. Led Audio team at Hive AI, where we trained and deployed 500M parameter-scale models to production.
  • name: Andrew Aikawa
    title: Founder
    role: Founder
    bio: Co-founder and CTO at Trainy building a training platform to make deep learning go faster. Previously a lead Machine Learning Engineer for Hive AI's object detection products. I completed my Physics Ph.D. UC Berkeley '22 where my thesis focused on applying computer vision and deep learning on nanoscience. Physics & Computer Science B.A. UC Berkeley '17.

Back to YC companies