Does Valohai Run on Your Own Cloud and Keep Your Data in Place?

Yes — Valohai is designed so your data and compute stay where you need them, including your own cloud environment, rather than being forced onto Valohai's infrastructure. The platform syncs your files with cloud storage, versions every run, and stays out of your code. This matters most if you have data residency requirements, existing cloud commitments, or security constraints that rule out moving data to a third-party platform.

Where your data and compute actually live

Valohai's stated position is that "your data and compute always stay where you need it to be." In practice this means:

• You bring your own cloud. The platform runs against your cloud environment instead of requiring you to migrate data and workloads to Valohai-hosted infrastructure.
• Files sync with your cloud storage. Valohai syncs your files with cloud storage rather than pulling them into a separate platform-side store.
• Every run is versioned. The platform versions each run, which gives you a record of what was executed and against which inputs.

The practical consequence: your raw data and your compute footprint remain under your cloud account and your control, while Valohai provides the orchestration, tracking, and lineage layer on top.

What "staying out of your code" means

Valohai's approach is to avoid locking you into platform-specific code. The conventions it expects are ordinary Python:

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

None of these are Valohai-specific. That has two effects worth noting:

1. Migration is repetitive rather than deep. Moving an existing script onto the platform generally means rewriting file paths, lifting parameters into argparse, and printing metrics as JSON — mechanical changes, not a rewrite.
2. Coding assistants can do the work. Because the conventions are standard Python, tools like Claude Code, Cursor, and Copilot can apply those changes across your scripts. Valohai notes this is why its upskilling path is gentle and why Agent Skills work well with it.

Why this matters for AI products specifically

The page frames the problem this way: "Calling the LLM is the easy part." A working AI product needs a context pipeline, specialized models, and a way to know whether it's giving the right answers. Those specialized models — classifying support tickets, detecting anomalies in real time, predicting churn — run on models trained on your data, alongside the LLM.

That combination creates pressure on where data lives:

• Specialized models need training on your data, which raises the stakes on data residency.
• More models get built than planned, by more people than expected — not just your ML team, since any software engineer with a coding assistant can now build and ship a model.
• AI products aren't shipped once. Models need retraining, data drifts, costs change, and quality can drift down without triggering uptime or latency alerts.

Running on your own cloud keeps that expanding set of models and their training data inside the boundary you already govern, rather than spreading it across a vendor's environment.

What to verify before deciding

The page confirms the own-cloud positioning and the file-sync/versioning behavior, but it does not spell out every operational detail. Before committing, confirm with Valohai directly:

• Which cloud providers are supported for the own-cloud deployment you need.
• How the sync works in your setup — direction, frequency, and what happens to intermediate artifacts.
• Pricing and access terms. Valohai publishes a pricing page, but the specifics of what's included at each tier aren't stated in the material here, so don't assume any particular tier is free or unrestricted.
• Lineage scope. The platform advertises "full lineage from data to deployment" — ask how far back it traces and what it captures for your pipeline shape.

If your requirement is simply "keep data and compute in our cloud, don't force a migration, and don't rewrite our Python," Valohai's design targets exactly that. If you need contractual guarantees about data location or specific compliance certifications, those aren't covered in the material here and should be raised directly.