Trainy
Trainy provides Konduktor, a Kubernetes-native GPU orchestration platform enabling AI/ML teams to run large-scale training and inference workloads across cloud providers via YAML configuration, with usage-based and reserved pricing, plus Pluto experiment tracking software.
- Company typePrivate
- Founded2023
- HeadquartersSan Francisco, United States
- Headcount1–10
- GTM typeB2B
- OfferingSoftware
What Trainy does
Trainy is a San Francisco-based private company founded in 2023 that builds Konduktor, a Kubernetes-native GPU orchestration platform for large-scale ML training and inference workloads. The platform enables AI teams to submit jobs via simple YAML configuration files without modifying existing training code, handling cross-cloud deployment (GCP, AWS), multi-node training with high-bandwidth Infiniband networking, preemptive priority queuing, and automated GPU fault detection and recovery. Trainy serves AI/ML engineering teams, foundation model builders, and early-stage AI startups requiring on-demand access to high-performance GPU clusters, with named customers spanning Linum AI, Casual Labs, Diffuse Bio, Whitefiber, Digital Ocean, and Paperspace.
The company monetizes through three primary streams: usage-based on-demand GPU compute at $3.60 per GPU hour plus cloud provider costs; annual Reserved GPU allocations starting at $50,000 per year with enterprise SLA; and Pluto, a separate experiment tracking SaaS product priced at $250 per seat per month, built on a ClickHouse and Postgres architecture with Neptune and Weights & Biases compatibility layers for migration. Go-to-market is hybrid, combining self-serve CLI onboarding (pip install konduktor) with enterprise field sales motion supported by prominent "Book a demo" CTAs. Marketing emphasizes developer relations through documentation, Discord community, GitHub open-source contributions, and technical blog content.
Trainy is backed by Y Combinator (which led a $500K seed round in September 2023), Z Venture Capital, and Lux Capital. The product portfolio has expanded rapidly from the core Konduktor platform launched in 2024 to include Konduktor Serve (vLLM-based LLM inference), Pluto experiment tracking (positioned as a Neptune replacement for the March 2026 shutdown), and developer tools including neptune-exporter and Konduktor Skills for AI coding assistants. The company operates with 1-10 employees and has not publicly disclosed revenue.
Trainy firmographics
Firmographics- Name
- Trainy
- Legal name
- Trainy
- Website
- https://trainy.ai
- Company type
- Private
- Founded year
- 2023
- Operating status
- Operating
- Headcount range
- 1–10 employees
- Short description
- Trainy provides Konduktor, a Kubernetes-native GPU orchestration platform enabling AI/ML teams to run large-scale training and inference workloads across cloud providers via YAML configuration, with usage-based and reserved pricing, plus Pluto experiment tracking software.
- Ownership category
- akta.pro rank
Trainy industry classification
Industry- Product category
- AI/ML Infrastructure Software
- NAICS
- Computer Systems Design and Related Services (5415)
- SIC
- Services-Computer Programming Services (7371)
- akta.pro primary industry
- AI Compute Virtualization & Scheduling (GPU virtualization, cluster schedulers) (HDAAAAAG)
- akta.pro secondary industries
- AI Compute Cloud & GPU-as-a-Service (HDAAAAAK), DevOps, SRE & Platform Engineering (CI/CD, IaC, Containers) (EDABAFAC)
Keywords
Where Trainy is headquartered
LocationHeadquarters
- HQ city
- San Francisco
- HQ country
- United States
- HQ region
- North America
Offices1 record
Markets served
Trainy business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Technology or R&D, Infrastructure, Personnel, Operations, Marketing or Sales
Revenue model
- On-Demand GPU Compute: Pay-per-use GPU rental model where customers only pay when their code is running. Zero costs when GPUs are idle. Provides access to high-performance clusters with 8xH100 GPUs, 80GB memory each (SXM5), and 3.2 TB/s Infiniband connectivity. Billed at $3.60 per GPU per hour plus cloud provider costs.
- Reserved GPU Allocation: Dedicated GPU allocation with annual contract pricing starting at $50,000/year. Includes dedicated GPU resources, advanced monitoring, cluster utilization insights, and enterprise SLA. Best for inference servers, dev boxes, and teams requiring guaranteed capacity.
- Pluto Experiment Tracking: Experiment tracking platform for ML teams. Hosted plan is $250/seat/month, matching Neptune's pricing. Also available as open-source with self-hosting option.
- Academic/Startup Promotions: Pluto PRO promotional codes available for academic users and early-stage startups via email to [email protected]. Free PRO plan for users with .edu email addresses.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Usage-based | Pay-as-you-go | On-Demand GPU compute with flexible usage-based pricing |
| Subscription | Annual | Reserved dedicated GPU allocation with annual contract |
| Per seat | Monthly | Pluto hosted experiment tracking per seat per month |
Go-to-market motion2 records
Distribution channels4 records
Marketing channels8 records
Trainy product offering
Product offeringCore offering
Trainy sells a Kubernetes-native GPU orchestration platform (Konduktor) that lets AI/ML teams launch large-scale batch training and inference workloads across cloud providers via simple YAML configuration files, with automatic networking, fault detection, and recovery. It complements this with Pluto, an experiment tracking platform positioned as a high-performance Neptune.ai replacement with Weights & Biases compatibility. Revenue is generated from usage-based GPU compute ($3.60/GPU/hour), annual reserved GPU contracts ($50,000+/year), and per-seat Pluto subscriptions ($250/seat/month).
Product overview
Trainy offers a GPU orchestration platform (Konduktor) for ML training and an experiment tracking platform (Pluto). Konduktor handles GPU job scheduling, health monitoring, and multi-node training with YAML-based configuration across cloud providers. Pluto provides experiment tracking with Neptune Scale and wandb compatibility for migration, plus Claude AI MCP integration. The product portfolio includes on-demand ($3.60/GPU hour) and reserved (starting $50,000/year) pricing tiers, developer tools (CLI, neptune-exporter, Konduktor Skills), and serving capabilities via Konduktor Serve for vLLM deployments.
Differentiator
Problem solved
Functional benefit
Brands
- Pluto: Experiment tracking platform built on a fork of MLOp, designed as a Neptune alternative with fast performance at scale.
- Konduktor
Products and services
- Konduktor Kubernetes-native ML/AI GPU platform for high-performance batch jobs that enables AI/ML teams to run large-scale training and inference workloads across cloud providers via simple YAML configuration, with automatic handling of networking, scaling, fault detection, and recovery.
- On-Demand GPU Compute Usage-based GPU rental product for AI/ML teams needing flexible, pay-as-you-go access to high-performance GPU clusters (8xH100 with 80GB SXM5, 3.2 TB/s Infiniband) at $3.60 per GPU per hour plus cloud provider costs, with no annual commitment.
- Reserved GPU Allocation Annual subscription product providing dedicated GPU allocation, advanced monitoring, cluster utilization insights, and 99.5% uptime SLA with 2-3 day setup and 24x7 customer support, starting at $50,000/year for inference servers, dev boxes, and teams requiring guaranteed GPU capacity.
- Pluto Experiment Tracking Hosted experiment tracking platform built on ClickHouse (OLAP) and Postgres (OLTP) that offers Neptune Scale and Weights & Biases compatibility layers for zero-code migration, with Linear issue tracking integration and Claude AI MCP natural language querying for ML teams.
- Konduktor CLI Open-source command-line interface (pip install konduktor) for managing GPU workloads on the Konduktor platform, supporting YAML-based job configuration and environment variable management for ML engineers and developers.
- Konduktor Serve Model serving deployment feature on Konduktor supporting vLLM deployments for LLM inference with OpenAI-compatible API endpoints, tensor parallelism, and automatic horizontal scaling, plus general container deployments with health checks.
Quantifiable outcome
- 50% reduction in infrastructure costs
- +4 more outcomes
Companies that use Trainy
Customer profileNamed customers6 records
Segments4 records
Ideal customer profiles4 records
Trainy technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- Yes
- API docs
- API detail
Core technology
AI maturity
App detail
Integration17 records
AI capability6 records
Feature12 records
Trainy partnerships and signals
Strategic signalScale indicators8 records
Recent moves6 records
Expansion highlights5 records
Trainy competitors and assessment
Company assessmentEmerging players
- Anyscale: Managed Ray platform for distributed AI workloads. Competes with Trainy's multi-node orchestration value proposition, particularly for teams already standardizing on Ray as a framework.
- Modal Labs: Developer-focused GPU compute platform for AI/ML workloads with a serverless, code-first UX. Comparable in target audience (ML engineers) and emphasis on developer experience, though more focused on inference than multi-node training.
Broad incumbents
- Paperspace (DigitalOcean): GPU cloud platform owned by DigitalOcean (both are listed as 'trusted customers' of Trainy). Overlaps directly with Trainy's H100 offering but is part of a broader cloud portfolio targeting SMB and startups.
- Weights & Biases: ML experiment tracking and model management platform. Trainy ships dual-logging compatibility with W&B to capture migrations; comparable product scope but Wandb is significantly broader and more established.
Direct peers
- Neptune.ai: Experiment tracking platform being sunset in March 2026 — Trainy's Pluto is explicitly positioned as a migration target with Neptune compatibility. Direct overlap in ML experiment tracking market.
- Foundry (Foundry Cloud / Voltage Park): Cloud provider focused on bare-metal and reserved GPU clusters for AI workloads. Named by Linum AI as a prior provider, indicating direct competition for multi-node H100 training deals.
- CoreWeave: Specialized GPU cloud provider offering large-scale NVIDIA GPU clusters (H100, H200, etc.) for AI training and inference. Direct competitor to Trainy's On-Demand and Reserved tiers, with significantly larger scale and capital.
- Together AI: GPU cloud for AI training and inference with reserved/spot pricing models. Named by Linum AI as a prior solution; competes head-to-head with Trainy's Reserved tier for AI teams needing multi-node clusters.
- Lambda Labs: GPU cloud and on-demand H100/H200 clusters for AI/ML workloads. Explicitly named by Trainy's customer Linum AI as a prior solution, confirming direct overlap in target customer and use case.
- MosaicML (Databricks): Platform for large-scale model training on managed GPU clusters; now part of Databricks. Direct overlap with Trainy's training orchestration value proposition, with significantly more capital and integration.
Market position
Strengths5 records
Weaknesses5 records
Competitive moat5 records
Key risks6 records
Key highlights6 records
Customer concentration
Trainy social profiles
Digital presenceTrainy financial estimates
Financial estimateRevenue estimate
Valuation estimate
Trainy leadership team
Management profileNumber of profiles
Profiles2 records
Trainy funding detail
Funding detailFunding overview
Funding rounds1 record
Investors1 record
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
Trainy M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about Trainy
What does Trainy do?
Trainy sells a Kubernetes-native GPU orchestration platform (Konduktor) that lets AI/ML teams launch large-scale batch training and inference workloads across cloud providers via simple YAML configuration files, with automatic networking, fault detection, and recovery. It complements this with Pluto, an experiment tracking platform positioned as a high-performance Neptune.ai replacement with Weights & Biases compatibility. Revenue is generated from usage-based GPU compute ($3.60/GPU/hour), annual reserved GPU contracts ($50,000+/year), and per-seat Pluto subscriptions ($250/seat/month).
Is Trainy a public or private company?
Trainy is a private company. It is classified as venture growth investor backed and is currently operating.
When was Trainy founded?
Trainy was founded in 2023. It employs 1 to 10 people.
Where is Trainy based?
Trainy is headquartered in San Francisco, United States, in the North America region.
How does Trainy make money?
Four revenue lines are on record. On-Demand GPU Compute is the primary driver. The others are reserved GPU Allocation, pluto Experiment Tracking and academic/Startup Promotions.
Who are Trainy's main competitors?
Emerging players on record are Anyscale and Modal Labs. Broad incumbents are Paperspace (DigitalOcean) and Weights & Biases. Direct peers are Neptune.ai, Foundry (Foundry Cloud / Voltage Park), CoreWeave, Together AI, Lambda Labs and MosaicML (Databricks).
Does Trainy have an API?
Yes. REST API for Pluto experiment tracking platform. Base URL: https://pluto-api.trainy.ai. Enables programmatic creation of runs, logging metrics, managing tags and notes, querying experiment data, and more. Authentication via Bearer token in Authorization header. Full OpenAPI specification available at https://pluto-api.trainy.ai/api/openapi.json. Developer documentation is at docs.trainy.ai/pluto/api-reference/introduction.
What industry is Trainy in?
Trainy's product category is AI/ML Infrastructure Software. Its primary akta.pro industry code is HDAAAAAG, AI Compute Virtualization & Scheduling (GPU virtualization, cluster schedulers), with a secondary code of HDAAAAAK, AI Compute Cloud & GPU-as-a-Service. Its NAICS code is 5415 and its SIC code is 7371.