Valohai MLOps Platform

Valohai is a Finnish machine learning platform company founded in 2016 in Turku and backed by Superhero Capital, Reaktor Ventures and Business Finland. It removes the infrastructure work behind training and deploying models, so data science teams can run many experiments in parallel on their own cloud or on-premises compute.

The platform orchestrates multi-step pipelines with conditional logic, quality gates and human approvals, versions datasets immutably, tracks experiments and lineage from model back to data and code, and manages a model registry with approval workflows. It supports distributed training with PyTorch Distributed, DeepSpeed and Horovod, deploys HTTP endpoints on Kubernetes, and adds LLM and RAG evaluation. It runs on AWS, Azure, Google Cloud, Oracle, OVHcloud, Scaleway, on-premises or air-gapped. Users include Viator and Arquimea.

• Pipelines: multi-step workflows with conditions, quality gates, parallel steps and human approvals.
• Datasets: immutable versioned datasets with aliases for promotion.
• Lineage and experiment tracking: links each model to its run, data version and deployment.
• Model registry: approval workflows and editable model properties.
• Distributed training and deployment: PyTorch Distributed, DeepSpeed, Horovod and Kubernetes endpoints.
• LLM evaluation: compare models and prompts on quality, latency and cost.

Vietnamese banks can manage credit scoring and fraud model pipelines with full lineage for audits. Manufacturers can retrain visual inspection models on on-premises GPUs as new data arrives. Telecom data teams can run parallel experiments across cloud and local clusters.

Data and compute stay in the customer's own environment while the platform handles orchestration. Viator cut ML pipeline development from two weeks to half a day. Arquimea doubled GPU utilization and avoided 180,000-270,000 EUR in hardware costs. Automatic lineage and approval workflows support audits in regulated industries.

As more companies move machine learning from notebooks to production, they need reproducibility, governance and efficient GPU use without locking into one cloud. Regulated industries in Asia increasingly want MLOps that runs inside their own infrastructure to meet data residency and audit requirements.

Founded in 2016 in Turku, Finland. Raised 1.8 million USD in 2018 led by Superhero Capital with Reaktor Ventures and Business Finland. Listed on Oracle Cloud Marketplace and partners with AWS, Azure, NVIDIA, OVHcloud and Snowflake. Rated 4.9/5 on G2.