Valohai

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.