Valohai

Name: Valohai
Brand: Valohai
Rating: 4.9 (34 reviews)

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The scalable MLOps platform enabling CI/CD for ML and pipeline automation on-prem and any-cloud.

Paid plans only, free trial

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4.9 (34 across the web)

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Reviews on G2 Capterra

34 reviews tracked

The Bottom Line

Updated May 26, 2026

Price: Paid plans only, free trial. Compare plans

Key facts

Automates ML workflows with CI/CD principles for reproducibility and scalability.
Supports hybrid and multi-cloud deployments, including on-premises infrastructure.
Provides automatic versioning for code, data, experiments, and models with full lineage.

Pros

Ensures full reproducibility of ML experiments and models
Offers flexibility to run ML workloads on any cloud or on-premises infrastructure
Simplifies MLOps by abstracting infrastructure management
Supports any ML framework, language, or library via Docker containers
Provides unlimited projects, experiments, pipelines, and deployments with per-user pricing

Cons

Pricing details are not transparently listed and require a custom quote
Requires integration with existing systems, which might involve initial setup efforts

What is Valohai?

Valohai is a comprehensive MLOps platform designed to streamline complex machine learning workflows from experimentation to deployment. It provides end-to-end automation and reproducibility for ML projects, functioning as a CI/CD system specifically tailored for machine learning. The platform supports hybrid and multi-cloud environments, allowing users to manage AI workloads across various cloud providers and on-premises data centers with ease. Valohai is built for ML pioneers, data scientists, and MLOps teams seeking to accelerate ML development and deployment. It abstracts away the complexities of managing ML infrastructure, enabling users to focus on building and deploying models. Key benefits include automatic versioning of experiments, datasets, and models, smart orchestration of ML workloads on diverse infrastructures, and a developer-centric core that supports any language or framework.

Available on: Web

Louis Corneloup Updated May 26, 2026 · how we evaluate Source valohai.com ↗

Ratings Across the Web

4.9 (34 reviews)

Valohai holds an aggregate rating of 4.9 out of 5 from 34 reviews across G2 and Capterra, last checked September 17, 2026.

Ratings aggregated from independent review platforms. Learn more

Preview

Key Features

Automatic versioning with complete lineage of ML experiments, datasets, and models
Hybrid and multi-cloud support for AI workload management
Smart orchestration of ML workloads on any infrastructure (cloud or on-premise)
Framework and language agnostic development environment
CI/CD pipelines for ML automation
Model deployment for batch and real-time inference
Hyperparameter optimization
Human-in-the-loop workflows

Pricing Plans

Free Trial

Pricing checked Sep 29, 2026

Valohai plans and prices, checked September 2026
Plan	Price	Details
Per-User License	Contact us	Unlimited Projects Unlimited Experiments Unlimited Pipelines +7 more Show less Unlimited Deployments Auto-scaling Compute On-premises or Managed Cloud deployment options GPU Optimization Advanced Security and Access Control Technical Support Training and Onboarding

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Is Valohai worth the price?

Fair value

Valohai's 'Contact us' pricing for its single 'Per-User License' tier suggests an enterprise-focused, high-value offering.

This model is best suited for large enterprises and organizations with significant MLOps needs and custom requirements.

Hidden Costs & Gotchas

Potential minimum seat requirements

Full Valohai pricing: every plan, hidden costs and alternatives

Reviews

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4.9/5

Across 34 verified user reviews on G2, Capterra

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Valohai FAQ

How does Valohai ensure the reproducibility of ML experiments across different environments?

Valohai automatically versions every aspect of an ML run, including code, data, logs, hyperparameters, and the environment. This complete lineage tracking ensures that any experiment can be systematically reviewed and reproduced, even months later, regardless of the underlying infrastructure.

Can Valohai manage ML workloads on a combination of private cloud and on-premises GPU clusters?

Yes, Valohai is designed for hybrid and multi-cloud environments. It can orchestrate ML workloads seamlessly across various cloud providers, private clouds, and on-premises data centers, including optimizing GPU allocation on your existing hardware.

What mechanisms does Valohai provide for integrating with existing CI/CD systems or custom tools?

Valohai offers robust APIs and webhooks that allow for deep integration with existing CI/CD pipelines and any other internal systems. This enables triggering pipelines, managing resources, and automating workflows programmatically, ensuring flexibility in your development ecosystem.

How does Valohai handle data versioning and curation without duplicating large datasets?

Valohai allows users to curate and version datasets efficiently. While it tracks changes and lineage for datasets, it focuses on smart management to avoid unnecessary duplication, ensuring that data scientists can collaborate on and compare different versions of data without excessive storage overhead.

Does Valohai support specific machine learning frameworks or is it truly agnostic?

Valohai is framework and language agnostic. It can run anything you can put into a Docker container, meaning you can use any ML framework (TensorFlow, PyTorch, scikit-learn, etc.), any programming language (Python, R), and any external libraries without restriction.

What kind of cost optimization features are included for managing compute resources?

Valohai includes auto-scaling compute resources that dynamically adjust based on workload needs to optimize costs. It also provides tools to track costs and usage in real-time, including underutilization alerts, to help manage spending effectively across different infrastructures.

Source: valohai.com

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