Your Data Stack Just Got More Expensive. Your BI Doesn't Have To.
On June 1, 2026, Fivetran and dbt Labs completed their merger — creating a $4.4 billion combined entity that now controls both data ingestion and transformation for over 100,000 data teams worldwide.
This is a significant moment for the data industry. And if you're a data leader budgeting for next year, it's a moment worth thinking about carefully.
The data stack just got more consolidated, more bundled, and for many teams, more expensive. The question now is whether every layer of your stack will follow that trajectory.
Consolidation Raises Costs
Before the merger, the modern data stack had a clean separation of concerns: Fivetran moves data in, dbt transforms it, and a business intelligence (BI) tool visualizes it. Each layer was independently priced, evaluated, and replaceable.
That modularity was a feature. It gave data teams flexibility to mix and match, to pick the best tool for each job, negotiate pricing independently, and avoid single-vendor lock-in across the stack.
The Fivetran-dbt merger changes that equation. When one company controls both ingestion and transformation, representing roughly two-thirds of a typical data stack by cost, the incentives shift toward bundled pricing, integrated workflows, and ecosystem lock-in.
We've already seen early signals. Some Fivetran customers reported 4-8x price increases in the quarters preceding the merger. Whether those increases represent fair repricing for usage or aggressive rent-seeking before consolidation depends on your perspective. Either way, they're real, and they're compressing the budgets that data teams have left for everything else.
Prices Are Rising Across the Data Stack
The Fivetran-dbt merger isn't happening in a vacuum. Across the BI and data analytics landscape, prices have been shifting in one direction:
Salesforce increased prices by 9% across all products, including Tableau, in August 2025, with 5-7% annual escalators built into new contracts. A Tableau customer who signed a $100,000 contract in 2024 is now looking at $115,000-$117,000 at renewal. Over a three-year term, that's a 15-20% cumulative increase before you've added a single user.
ThoughtSpot's Enterprise tier typically starts at $100,000-$500,000 per year. Sigma Computing's median contract is $61,000/year according to Vendr data from 117 deals. Looker averages $150,000/year across 355 Vendr contracts.
None of these numbers are unreasonable in isolation. Enterprise software costs money to build and maintain. But when your ingestion layer, transformation layer, and visualization layer are all getting more expensive simultaneously, the total cost of your data stack can increase 20-40% in a single renewal cycle.
Something has to give.
The BI Layer Is Where You Have Room to Push Back
Not every layer of the data stack carries the same switching cost.
Changing your ingestion tool is painful: you're rewiring pipelines, recertifying connectors, and revalidating data freshness guarantees. Changing your transformation layer means rewriting models, re-certifying metric definitions, and retraining your team. These layers are sticky by design.
Changing your BI tool is a different story. That's a visualization and access layer. Your data stays in the warehouse. Your models stay in dbt. Your metrics stay in your semantic layer. The BI tool renders it and makes it accessible. Switching costs exist (dashboards need to be rebuilt, users need to be retrained), but they're an order of magnitude lower than ripping out your ingestion or transformation layer.
That makes BI the natural place to optimize when data stack costs rise. You don't have to accept a $60,000-$150,000 BI bill just because your ingestion and transformation costs went up too. And a vendor-agnostic, warehouse-native BI tool does more than lower today's bill; it preserves the freedom to swap out other layers of your stack later without re-platforming your analytics, a point we'll come back to.
What Preset Costs
Preset Professional is $20 per user per month, billed annually. For a team of 50 users, that's $12,000 per year.
Here's how that compares, with actual numbers:
| Solution | 50 Users, Annual | Source |
|---|---|---|
| Preset Professional | $12,000 | preset.io/pricing |
| Tableau Cloud Standard (mixed roles) | $42,000+ | tableau.com/pricing |
| Looker | $84,000-$120,000 | Vendr / Mammoth analysis |
| Sigma Computing | ~$61,000 (median) | Vendr, 117 contracts |
| ThoughtSpot Pro | $30,000 | thoughtspot.com/pricing |
| Power BI Pro | $8,400 (no AI) | microsoft.com |
| Power BI Pro + Fabric F2 (for Copilot) | $11,544 | Pro + $262/mo capacity |
Power BI Pro at $8,400 is cheaper on sticker price, but that number doesn't include Copilot, which requires Fabric capacity. Add the minimum F2 tier and you're at $11,544. And if your data isn't in Azure, Power BI's 75+ connector support narrows considerably.
We're not claiming we're the cheapest BI tool in every scenario. We're claiming that at $20/user/month with AI features included, Preset offers the most competitive price-to-capability ratio in the market, especially for organizations that don't want their BI tool locked to a single cloud vendor.
But It's Not Just About Sticker Price
The pricing table above only shares half the picture. Total cost of ownership includes implementation, training, ongoing administration, and support:
| Cost Component | Preset | Tableau Enterprise | Power BI + Fabric | Looker |
|---|---|---|---|---|
| License (50 users, annual) | $12,000 | $42,000-$63,600 | $11,544 | $60,000-$84,000 |
| Implementation | Low (managed) | $25,000-$150,000 | $10,000-$50,000 | $30,000-$100,000 |
| Infrastructure | $0 (included) | $0 (Cloud) | $0 (SaaS) | $0 (Cloud) |
| Ongoing admin | Minimal | $10,000-$30,000/yr | $5,000-$15,000/yr | $10,000-$25,000/yr |
| Training | Low | $24,000-$40,000 | $5,000-$15,000 | $15,000-$30,000 |
| Year 1 Total | $12,000-$15,000 | $101,000-$284,000 | $32,000-$92,000 | $115,000-$239,000 |
Sources: Digital Mass Tableau TCO Analysis, Power BI Pricing, Mammoth Looker Pricing
These aren't hypothetical numbers. They're drawn from published pricing, analyst reports, and Vendr contract data. Your specific deployment will vary, but the ratios stand.
The Open Source Advantage in a Bundled World
As data infrastructure consolidates into bundled platforms (Salesforce + Tableau + Data Cloud, Microsoft + Power BI + Fabric, Google + Looker + BigQuery) the risk of ecosystem lock-in increases. When your BI tool is tightly coupled to a specific cloud vendor's data platform, switching any layer means switching all of them.
Preset is built on Apache Superset, the most widely adopted open-source BI platform in the world, with 72,700+ GitHub stars and over 17,200 forks. This matters for three reasons:
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No lock-in. Your dashboards, datasets, and configurations aren't trapped in a proprietary format. If your needs change, your investment in Superset is portable.
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Any cloud, any warehouse. Preset connects to 75+ databases across AWS, GCP, Azure, and on-premise environments. Your BI tool works wherever your data lives, not the other way around.
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Community as moat. 136 contributors shipped the latest major Superset release (69 of them first-time contributors). The community builds connectors, visualizations, and integrations that the platform benefits from, and that benefit flows to every Preset customer.
In a world where data stack vendors are consolidating and raising prices, the independence of your BI layer isn't a philosophical preference. It's a procurement strategy.
The Uncomfortable Truth About "Free"
We also hear the question: "If Superset is open-source, why pay for Preset at all?"
Fair question. Here's what we have seen among our users: running Superset at production scale (with enterprise security, managed upgrades, SLA-backed uptime, and zero infrastructure management) costs real money in engineering time if you do it yourself. Our estimates, validated with customers who've switched from self-hosted: 0.25-0.5 of a senior engineer's time, worth $50,000-$150,000 per year, plus $6,000-$24,000 in infrastructure costs.
Preset at $12,000/year for 50 users costs less than the engineer time alone. That engineer can go back to building your product instead of patching your BI deployment.
Read our full TCO breakdown for detailed analysis by company size.
What We Tell Every CFO
Your data platform costs are rising across ingestion, transformation, warehousing, and BI. Some of those increases are unavoidable. But the BI layer is the one where you have the most room to act: the lowest switching costs, the most competitive alternatives, and the clearest TCO advantage from open-source foundations.
Preset gives you enterprise BI with AI capabilities, running on the most widely adopted open-source platform in the category, at a fraction of what the proprietary alternatives charge. No cloud lock-in. No annual escalators. No per-viewer fees for embedded analytics.
Your data stack just got more expensive. Your BI doesn't have to.
Go Deeper
- Open Source BI Cost Breakdown: TCO and ROI by Company Size — the full TCO math by company size, with infra, ops, and engineering overhead broken out
- When Your BI Tool Tells You Who It's For — companion thought piece on what BI vendor positioning reveals about cost structure
- Why Preset is the Most Open Data Analytics Platform — the open-source independence argument in more detail
