What is Valohai and what does it do?

Valohai is an MLOps platform for AI products that combine LLMs with specialized models. It is built for teams that need to compare, evaluate, and ship model configurations against real data, on their own cloud, with full lineage from data to deployment. If your product is more than a single LLM call — for example, an LLM that routes to forecasting, classification, or anomaly-detection models trained on your data — Valohai is aimed at that setup. It has been operating since 2016 and offers a free start plus a demo option.

The problem it addresses

The platform's own framing is blunt: "Calling the LLM is the easy part." The harder parts are the context pipeline, the specialized models that produce the actual answer, and knowing whether the system is giving correct answers.

A concrete example from the site: a user asks "Projected yield?" A pure LLM may guess from training data and return a confident but hallucinated number like "Exactly 14,847 units." With specialized models, the LLM routes the question to a forecast model trained on your data, which returns something like "12,400 units, ±8%" — faster, cheaper, more accurate, and consistent.

What shipping an AI product actually involves

The platform highlights three shifts:

• LLMs talk; specialized models do the work. Classifying 100,000 support tickets a day, detecting anomalies in real time, predicting churn before renewal — these run on models trained on your data, alongside the LLM. The LLM stays in front; the specialized models supply domain precision.
• You will end up with more models than planned, built by more people than expected. Not just the ML team. With coding assistants, any software engineer can build and ship a model, so more features and use cases mean more models that all need evaluation and management alongside the LLMs they support.
• AI products are not shipped once. Models need retraining, data drifts, costs change, and cloud bills can climb quietly when nobody tracks per-request cost. Quality can drift down without triggering uptime or latency alerts, and a model can be retrained by another team and deployed without a clear changelog. The infrastructure around your AI determines whether these stay manageable or become surprises.

How it fits into existing workflows

Valohai is designed to stay out of your code. The site describes a workflow built on common Python conventions:

• Files are read and written in a local directory.
• Parameters are passed via argparse.
• Metrics are printed as JSON.

Nothing is Valohai-specific. The platform syncs your files with cloud storage, versions every run, and leaves your code alone. That is also why Agent Skills work well with it: tools like Claude Code, Cursor, Copilot, Codex, Gemini, and Zencoder can apply the migration changes — rewriting file paths, lifting parameters into argparse, printing metrics as JSON — across your scripts, making the upskilling path gentle and migration "rinse and repeat."

Where your data and compute live

A core positioning point is that your data and compute always stay where you need them to be. The platform runs on your own cloud, and it maintains full lineage from data to deployment. This matters for teams with data-residency or security constraints, and for anyone who needs to trace which data and configuration produced a given deployed model.

Who it is for

Valohai fits teams that:

• Build AI products combining LLMs with specialized, domain-trained models.
• Need to compare and evaluate configurations against real data before shipping.
• Must manage many models from many contributors, with retraining and drift as ongoing concerns.
• Want to track cost and quality per request, not just uptime and latency.
• Require their data and compute to remain in their own cloud with clear lineage.

If your product is a single LLM call with no specialized models, evaluation pipeline, or retraining loop, the platform's core value proposition is less relevant. If you are heading toward the "more models than you planned" situation, it is built for exactly that.

Pricing details are not covered here; the site links to a dedicated pricing page at valohai.com/pricing for current terms.