Valohai

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

Claim this tool

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

DevOps AI & Automation Cloud & Infrastructure Workflow Automation

Visit Website

4.9(34 across the web)

PaidVisit Website

Reviews onG2Capterra

34 reviews tracked

The Bottom Line

Entry price

Paid plans only

Biggest pro

Ensures full reproducibility of ML experiments and models

Biggest con

Pricing details are not transparently listed and require a custom quote

TL;DR - Valohai

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.

Pricing: Paid only

Best for: Enterprises & pros

4.9/5 across review platforms

What is Valohai?

Editorial review

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

LCLouis CorneloupUpdated May 26, 2026 · how we evaluateSourcevalohai.com ↗

Pros & Cons

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

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 March 19, 2026.

Ratings aggregated from independent review platforms. Learn more

Preview

Key Features

Automatic versioning with complete lineage of ML experiments, datasets, and modelsHybrid and multi-cloud support for AI workload managementSmart orchestration of ML workloads on any infrastructure (cloud or on-premise)Framework and language agnostic development environmentCI/CD pipelines for ML automationModel deployment for batch and real-time inferenceHyperparameter optimizationHuman-in-the-loop workflows

Pricing Plans

Free Trial

Pricing checked Aug 19, 2026

Per-User License

Unlimited Projects
Unlimited Experiments
Unlimited Pipelines
Unlimited Deployments
Auto-scaling Compute
On-premises or Managed Cloud deployment options
GPU Optimization
Advanced Security and Access Control

Calculate your cost View full pricing

Is Valohai worth the price?

65/100

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

While the features are comprehensive, the lack of transparent pricing makes it difficult to assess fairness against the market without direct engagement. This model is best suited for large enterprises and organizations with significant MLOps needs and custom requirements.

Hidden Costs & Gotchas

Per-user license cost unknown

Potential minimum seat requirements

Custom integration costs likely

On-premise infrastructure expenses

How Valohai Compares to Competitors

Compared to platforms like DataRobot, which can also have 'contact us' pricing but often targets similar enterprise clients, Valohai's feature set is competitive. However, without public pricing, it's hard to compare directly to more transparent platforms like Weights & Biases, which offers tiered pricing starting from free up to custom enterprise plans.

Reviews

Improve Your Thinking Patterns Using ChatGPT cover

$99Free with your review

Review Valohai, get a free AI guide

Share your experience and we will send you Improve Your Thinking Patterns Using ChatGPT, free.

Write a review

4.9/5

Across 34 verified user reviews on G2, Capterra

Add your hands-on experience using the offer above to help the next buyer.

Best Valohai Alternatives

Top alternatives based on features, pricing, and user needs.

DatabricksPaid

Unified analytics for data engineering, science, and ML

4.6

MLflowFree

Manage your ML lifecycle: track, register, and deploy models

4.1

Weights & BiasesFreemium

Track, compare, and share ML experiments and models

4.6

KubeflowFree

The open-source foundation for building and deploying AI platforms on Kubernetes.

4.5

Comet MLPaid

Machine learning experiment tracking platform

4.3

See all DevOps tools →

Still deciding?

Most buyers shortlist 2 or 3 tools before committing. Pull a side-by-side comparison or browse the full alternatives shortlist below.

Valohai vs DatabricksHead-to-head: features, pricing, who wins Valohai vs MLflowHead-to-head: features, pricing, who wins Valohai vs Weights & BiasesHead-to-head: features, pricing, who wins

Explore More

Best DevOps Tools Best AI & Automation Tools Best Cloud & Infrastructure Tools Best Workflow Automation Tools

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

Guides & Articles

Best MLOps Tools in 2026

Features Valohai

Best Observability Platforms in 2026

Expert guide

Best Incident Management Software in 2026

Expert guide