valohai.com

Calling the LLM is the easy part. An AI product still needs a context pipeline, specialized models, and a way to know whether it is giving the right answers. Specialized models are the components that do the work deciding the answer — classifying tickets, detecting anomalies, predicting churn — while the LLM stays in front to talk and route. This matters most when your product needs precision on domain data, when request volume makes LLM-only inference expensive, or when you need consistent, repeatable outputs rather than plausible-sounding guesses.

The division of labor: LLM talks, specialized models decide

Valohai frames the split directly: LLMs do the talking. Specialized models do the work that decides the answer.

• The LLM handles conversation, routing, and language-facing tasks.
• Specialized models run on models trained on your data and bring the precision the domain needs.
• The LLM stays in front; the specialized models sit behind it.

A concrete illustration from the site: a user asks "Projected yield?" An LLM-only setup guesses from training data and returns something like "Exactly 14,847 units" — hallucinated, expensive, inconsistent. With specialized models, the LLM routes the question to a forecast model trained on your data, which returns "12,400 units, ±8%" — faster, cheaper, more accurate, consistent.

Where specialized models earn their place

The work that decides the answer is often high-volume and repetitive, which is exactly where LLM-only approaches break down:

• Classifying 100,000 support tickets a day
• Detecting anomalies in real time
• Predicting churn before renewal

These tasks need models trained on your data, working alongside the LLM rather than being replaced by it.

Why this becomes a management problem, not just a modeling one

Valohai's argument is that you will end up with more models than you planned, built by more people than you expected — not just your ML team. A year ago, training a model meant a dedicated ML engineer. Today, any software engineer with a coding assistant can build and ship one. More features and use cases mean more models, all of which need to be evaluated and managed alongside the LLMs they support.

AI products also aren't shipped once:

• Models need retraining.
• Data drifts.
• Costs change, and cloud bills can climb quietly when nobody tracks what each request costs.
• Quality can drift down without triggering uptime or latency alerts.
• 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.

What Valohai provides for this

Valohai is an MLOps platform for AI products that combine LLMs and specialized models. Per the site, it lets teams compare, evaluate, and ship configurations against real data, on their own cloud, with full lineage from data to deployment. Your data and compute stay where you need them to be.

The platform is also designed to stay out of your code: files are read and written in a local directory, parameters go through argparse, and metrics are printed as JSON — common Python conventions, nothing Valohai-specific. The platform syncs your files with cloud storage, versions every run, and works with coding agents including Claude Code, Cursor, Copilot, Codex, Gemini, and Zencoder. Migration is described as rinse and repeat: rewrite file paths, lift parameters into argparse, print metrics as JSON.

When this approach fits — and when it may not

Consider specialized models alongside an LLM when:

• Your answers must be precise and consistent, not just fluent.
• You process high volumes where per-request LLM cost matters.
• Your decisions depend on data the base LLM was never trained on.
• Multiple teams are shipping models that need shared evaluation and lineage.

An LLM-only approach may be sufficient when your task is genuinely open-ended language work, volume is low, and occasional inconsistency is acceptable.

Valohai has been building this since 2016 and offers a free start and a demo booking, plus a free course ("LLMs Applied," 6 modules) and a 2-minute executive summary for teams evaluating the problem.

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.

Valohai is designed to stay out of your code, so the required changes are small and mechanical rather than a rewrite. According to Valohai's own description, the platform "syncs your files with cloud storage, versions every run, and stays out of your code." What it expects is not a Valohai-specific library or SDK but common Python conventions: reading and writing files in a local directory, passing parameters via argparse, and printing metrics as JSON. If your scripts already follow those conventions, there may be little to change at all.

What Valohai expects from your code

The platform's stated contract with your scripts comes down to three ordinary patterns:

• Files: read and write data in a local directory. Valohai syncs that directory with cloud storage, so you work with local paths rather than cloud APIs.
• Parameters: expose tunable values through argparse, the standard Python command-line argument parser.
• Metrics: print metrics as JSON so the platform can capture them from the run output.

None of these are Valohai-specific. They are "common Python conventions, nothing Valohai-specific," which is why the platform can version every run without you instrumenting your code with proprietary calls.

What migration actually involves

For an existing script, Valohai describes migration as "rinse and repeat" with three recurring edits:

1. Rewrite file paths so inputs and outputs point at the local directory the platform syncs.
2. Lift parameters into argparse so values that were hardcoded or set some other way become command-line arguments.
3. Print metrics as JSON instead of (or in addition to) whatever logging format you use now.

The repetitive nature matters: once you have done these three edits for one script, the same pattern applies to the next. There is no separate Valohai configuration language to learn for each job.

Why coding assistants can carry most of the work

Because the changes are conventional Python rather than platform-specific syntax, general-purpose coding assistants can apply them. Valohai notes that Claude Code, Cursor, and Copilot "apply those changes across your scripts," and that this is part of why "the upskilling path is gentle, and why Agent Skills work so well." In practice, you can point an assistant at a script and ask it to convert file paths, move parameters into argparse, and emit metrics as JSON — the same transformation for every script.

How to judge the effort for your own code

The honest answer is that the size of the change depends on how far your code is from those three conventions. If you already write scripts that take arguments, touch local files, and emit structured output, Valohai asks for very little. If your code is tightly coupled to a specific cloud storage client or a bespoke parameter system, expect a pass of mechanical edits — but still edits, not a rewrite, since the platform deliberately avoids requiring its own code inside your scripts.

To estimate your own effort, pick one representative training or inference script and try the three edits by hand or with an assistant. If that single script converts cleanly, the rest will follow the same pattern.

Valohai is an MLOps platform for AI products that combine LLMs with specialized models. It addresses the operational reality that AI products are not shipped once: models need retraining, data drifts, costs change, and quality can slip without triggering uptime or latency alerts. Valohai's answer is to let you compare, evaluate, and ship configurations against your real data, on your own cloud, with full lineage from data to deployment. This page is for teams whose AI product has grown past a single model and who need a way to keep many models — and their changes — manageable.

Why one model is rarely enough

The platform's framing is blunt: calling the LLM is the easy part. An AI product needs a context pipeline, specialized models, and a way to know whether it is giving the right answers.

The pattern Valohai describes:

• LLMs do the talking. They stay in front, handling language and routing.
• Specialized models do the work that decides the answer. 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 contrast the site draws is between asking an LLM directly and routing to a trained model. Asked for a projected yield, an LLM may guess from training data and return a confident, hallucinated, expensive, inconsistent number. Routing the same question to a forecast model trained on your data returns a faster, cheaper, more accurate, consistent answer with an uncertainty range.

The management problem: more models than planned

Valohai's second point is organizational. You will end up with more models than you planned, built by more people than you expected — not just your ML team. A year ago, training a model meant a dedicated ML engineer; today, any software engineer with a coding assistant can build and ship one. More features and use cases mean more models, and all of them need to be evaluated and managed alongside the LLMs they support.

What actually drifts, and why it goes unnoticed

The platform lists the ongoing changes that make AI products hard to operate:

• Models need retraining.
• Data drifts.
• Costs change — cloud bills can climb quietly when nobody tracks what each request costs.
• Quality can drift down without triggering uptime or latency alerts.
• A model can be retrained by another team and deployed without a clear changelog.

The last point is the one most teams feel as a surprise rather than a metric. The infrastructure around your AI determines whether these stay manageable or become surprises.

How Valohai addresses it

Full lineage from data to deployment

Valohai's stated capability is full lineage from data to deployment, which is what makes a changelog possible when a model is retrained and redeployed by a different team. The platform versions every run, so a change has a traceable origin rather than appearing as an unexplained shift in behavior.

Evaluate and compare configurations against real data

Rather than evaluating in the abstract, Valohai is built to compare and ship configurations against your real data. That is the mechanism for catching quality drift that uptime and latency monitoring will not surface.

Your data and compute stay where you need them

Valohai runs on your own cloud. Data and compute stay where you need them to be, which matters when the models are trained on your data and the cost profile of each request is something you need to track.

One platform for every model

The platform positions itself as one place for LLMs and the specialized models supporting them, so evaluation and management happen together instead of in separate toolchains per model type.

A low-friction path for the engineers building those models

Valohai's migration story is deliberately unglamorous: files are read and written in a local directory, parameters come via argparse, metrics are printed as JSON — common Python conventions, nothing Valohai-specific. The platform syncs your files with cloud storage, versions every run, and stays out of your code.

That design is also why coding agents work with it. Claude Code, Cursor, Copilot, Codex, Gemini, and Zencoder are listed as agent skills that can apply the migration changes across your scripts, because the required changes are mechanical: rewrite file paths, lift parameters into argparse, print metrics as JSON. The site calls migrating "rinse and repeat."

What teams say

The page includes a customer-quote section ("What teams running Valohai say"), but the excerpt provided does not include the quotes themselves, so no specific claims from those teams can be reported here.

Practical takeaways

If your AI product has moved past a single LLM call, the questions Valohai's framing suggests asking your team are:

1. Which specialized models decide our answers, and who owns each one?
2. Can we trace a deployed model back to the data and run that produced it?
3. Do we know what each request costs, and would we notice if it changed?
4. Would we detect a quality drop that never shows up as an outage or latency spike?
5. If another team retrains and redeploys a model, is there a changelog?

Valohai's pitch is that a platform with versioned runs, real-data evaluation, and full lineage is what keeps those answers available. The site offers a free start and a demo booking, plus a free course ("LLMs Applied," six modules) and a two-minute executive summary for teams that want the short version before talking to their engineers.

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.