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
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 another $50k to $150k in professional services on top of the licence. And annual contracts lock you in for 2 to 3 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 6 to 9 months and $300k to $500k before the first analyst can run a clean query.
The market has created a false choice: pay a premium for convenience, or spend months building it yourself.
The Solution
antvia is a managed open-source data lakehouse accelerator built by Woodfrog. It eliminates the false choice entirely.
The platform 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 antvia 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. A plain-English AI analyst interface that lets non-technical teams get answers without writing a line of code. All of it deployed into your own infrastructure, under your own control, in 2 weeks.
The base configuration supports 50 to 150 concurrent users and 1 to 5 TB of analytical data at $638 per month in infrastructure costs. A new data source is connected in under 30 minutes. A new dashboard goes live in under 2 hours. As the business grows, the platform scales horizontally with no migration projects, no re-platforming decisions, and no architecture redesigns.
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, often reaching $50k to $200k 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 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.
antvia's infrastructure cost at scale sits between $1,200 and $1,800 per month, a fraction of what any of these platforms charge at equivalent data volumes. There is no per-query charge, no per-user fee, and no feature gating. The cost model is infrastructure only, predictable, linear, 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.