The data platform problem
nobody talks about

Every growing business reaches the same wall. The data is there, in the CRM, the DMS, the ad platforms, the spreadsheets, but nobody can actually use it. Not reliably. Not quickly. Not without a significant investment in time, people and money.

So the business either waits, or it compromises.

The problem, on the data side

When a business decides it needs a data platform, the first instinct is to go with a name it recognises. Snowflake. Databricks. Microsoft Fabric. Salesforce Data Cloud. These platforms are polished, well marketed and genuinely capable. But the true cost rarely appears in the sales deck.

Licensing starts high and scales higher. Compute is billed per query, per processing unit, per hour. A dashboard that refreshes every 30 minutes runs up costs around the clock whether anyone is looking at it or not. User seats are charged separately, at different tiers, often with contract minimums. Governance, access control, AI features and real-time ingestion sit behind premium tiers that cost significantly more than the base. Moving data out of the platform costs money per GB in egress fees. Implementation, migration and training add a further block of professional services on top of the licence. And annual contracts lock you in for two to three years at a time, with committed spend floors that charge you whether you hit your usage or not.

Beyond the bill, there is a strategic cost that compounds quietly over time. Your data lives in their proprietary format, on their terms, queryable only through their engine. Every future use case gets shaped by what they choose to build, when they choose to build it, and at what additional price. You are handing the architecture of your most strategic asset to a vendor whose incentives are not aligned with yours.

The alternative is open source. There are hundreds of best-in-class open-source tools that collectively deliver everything the proprietary platforms offer and more. The catch is integration. Picking the right tools, connecting them correctly, deploying them reliably and governing them properly requires deep expertise that most organisations do not have sitting idle. Building from scratch takes the better part of a year and several senior hires before the first analyst can run a clean query.

And the same problem again, on the reporting side

Almost nobody budgets for this one twice, but it is the same shape. Having landed the data, the business now buys a BI tool, and buys it per seat. Power BI, Tableau, Looker. The licence climbs every year, and it climbs with headcount rather than with value, which means reporting quietly gets rationed to the people who can justify a seat. The store manager who would benefit most from a weekly number is the first person cut.

Then the second cost arrives: every new dashboard needs somebody who knows the tool. A request becomes a ticket, a ticket becomes a queue, and a queue becomes a spreadsheet somebody maintains by hand because it was faster than waiting. That spreadsheet is where the next argument about the numbers comes from.

And after all of that, the tool still cannot answer the only question the business actually asked. A dashboard says gross is down eleven percent in the Northeast. Somebody calls a meeting, a change gets made, and six months later nobody can say whether it worked, because the number moved for four reasons at once and none of them were written down.

The market has created a false choice twice over: pay a premium for convenience, or spend months building it yourself. And whichever you pick, you still cannot prove what any of it was worth.

The solution

Antvia is a managed open-source data lakehouse and decision platform built by Woodfrog. It ships as two products under one roof, and it eliminates the false choice on both sides.

Antvia Data is built on hundreds of best-in-class open-source tools, integrated, governed and production-ready. All data is stored in open formats in your own cloud account. Nothing is proprietary. Nothing is locked. Every future use case, whether new sources, new AI features, new consumers or new governance requirements, is something you build on your own terms, on your own timeline, without asking a vendor for permission.

What it delivers is the integration layer that makes open source work out of the box. A bronze-to-silver-to-gold data quality pipeline. Column-level access control and PII masking enforced on every query. A governed upload workflow for business users. Lineage on every hop, and a catalog where every table has an owner. All of it deployed into your own infrastructure, under your own control, in 2 to 4 weeks.

Antvia Intelligence is the half that answers the reporting problem. Every metric is defined once, in one place, and every dashboard, every question and every decision reads that same definition, so two dashboards cannot disagree. People reach it three ways: Lens for the dashboard built for their role, Echo for a question in plain English, and Explore for picking measures and dimensions without SQL. Reports arrive on a schedule for the people who will never log in, and Alerts arrive because something actually moved.

Then it does the thing no BI tool ships. Arc records a proposal against the metric it is meant to move and the units it applies to, and refuses to admit one that cannot name both.Leap measures what happened after the change went live, against what you predicted and against the units the change never touched, and reports the effects nobody predicted next to the ones they did. When two changes overlap and the effects cannot be separated, it says so and claims nothing.

Because Intelligence reads the same governed layer, it is onboarded to Antvia Data the way an AI agent is: a named system identity, a role, namespaces and column masks. There is no private back door between the two products, which is the first thing an auditor asks about. It also runs on Snowflake, Databricks and BigQuery if the warehouse is already bought.

What it costs

Two things are worth separating. The infrastructure is yours and billed at cost by your cloud provider, so it is visible, predictable and not a margin we are taking. The platform is a subscription, priced on how many sources you maintain and how many metrics you publish, and never on how many people read a number or how much data you keep.

That last part is the difference worth understanding before any figure is discussed. A warehouse bills you when a dashboard refreshes overnight. A BI tool bills you when somebody new needs to see a number. Both meters run faster precisely as the platform starts working, which is the wrong incentive to build a business on.

We meter neither. What that means for your organisation depends on your source count, your metric count and the shape of your current bill, so it is a conversation with numbers in it rather than a number on a page.

How Antvia compares

Snowflake, Databricks and Microsoft Fabric are built for enterprises with large budgets, dedicated data engineering teams and tolerance for multi-year vendor relationships. But you are paying for their convenience with your autonomy, and for their brand with your margin.

Databricks charges per compute unit, with costs that scale unpredictably with query volume and data size, reaching well into six figures annually for mid-market organisations. Microsoft Fabric bundles data engineering with the Microsoft ecosystem, which works well if you are already fully committed to that stack and is an expensive detour if you are not. Snowflake's separation of storage and compute is elegant, but every query runs through their engine in their format, and annual contracts reflect their market position.

On the reporting side the comparison is simpler, because the category has settled. Power BI, Tableau and Looker all sell dashboards, all sell them per seat, and none of them will tell you what a decision was worth. That last part is not a feature they have not got round to. It requires a metric definition the whole business shares and a record of what was expected before the change went live, and a dashboard tool has neither.

Antvia's infrastructure runs at a fraction of what these platforms charge at equivalent volumes, and it is billed to you at cost by your own cloud provider rather than marked up by us. There is no per-query charge, no per-user fee and no feature gating. What you pay for the platform is a subscription on sources and metrics, predictable and entirely under your control.

The deeper difference is ownership. With Databricks, Fabric or Snowflake, you are a tenant. With Antvia, you own the platform. The data is yours, the infrastructure is yours and the roadmap is yours. When AI capabilities mature, when new tools emerge, when your use cases evolve, you adopt them on your terms and not when a vendor releases a new pricing tier.

Antvia is the right choice for businesses that want enterprise data platform performance, the flexibility of open source and infrastructure economics, without the cost and compromise of vendor dependency, and that would like to find out whether any of it worked.