Valohai

MLOps platform for versioned pipelines, distributed training, and LLM evaluation across any cloud.

Enterprise· Free trial; contact sales for pricingAgentsMulti-model

Best for

Pick Valohai if you're an ML platform team that needs reproducible pipelines and training across multiple clouds or on-prem without committing to one hyperscaler's stack.

Skip if

Skip it if you're a solo developer, a hobbyist, or a team whose workloads fit comfortably inside SageMaker, Vertex AI, or a single notebook.

Valohai is a Finnish MLOps platform that has been around since 2016, built for teams that need to run reproducible machine learning pipelines across multiple clouds or on their own hardware. It handles the unglamorous infrastructure work around AI products: dataset versioning, experiment tracking, distributed training on multi-GPU/multi-node clusters, model deployment, and lineage from raw data through to production endpoints. More recently it has added LLM-specific tooling, including systematic evaluation of models across cost, latency, and quality, plus pipelines with conditional logic and human-in-the-loop approval steps.

Where Valohai differentiates itself from Databricks, SageMaker, or Vertex AI is its cloud-agnostic stance. It runs on AWS, Azure, GCP, Oracle Cloud, OVHcloud, and on-prem, and is pitched at regulated or sovereignty-conscious teams (aerospace, medical imaging, finance) that don't want to be locked into one hyperscaler. Datasets up to 18TB can be cached and versioned without duplication. Pricing isn't published; it's a sales-led enterprise product with a no-credit-card free trial at app.valohai.com.

Integrations cover the standard Python ML stack (PyTorch, transformers, scikit-learn), and there's a `valohai-llm` Python package plus a documented API for wiring it into existing CI/CD. It's not open source, and it's overkill for solo practitioners or anyone happy in a notebook.

Editor's take

Valohai is the quiet, competent choice in MLOps: less buzz than Databricks but a much cleaner story if multi-cloud or on-prem matters to you. The recent LLM evaluation tooling is a sensible bolt-on rather than a pivot, and the platform's lineage discipline pays off the first time a regulator asks how a model was trained.

— The AI Tool Bible editorial team