The Hidden Tax of Data Feeds
The data industry has operated on a fundamentally flawed model for decades. Organizations pay massive upfront fees for entire data feeds, consuming perhaps 5% of what they actually need while shouldering 100% of the cost. A hedge fund analyzing 50 companies pays the same price as one tracking 5,000. A startup exploring market entry bears the same data burden as an established enterprise with unlimited resources. This isn’t just inefficient—it’s economically irrational.
Traditional data vendors have built their business models around this inefficiency, creating artificial scarcity and forcing customers into expensive, all-or-nothing subscriptions. The result? Innovation stifled, exploration curtailed, and data budgets that favor incumbents over disruptors.
Introducing the Carbon Arc Framework: A New Paradigm
Carbon Arc’s framework-based consumption pricing fundamentally reimagines how data should be accessed and priced. At its core, our approach recognizes that every data interaction follows the same elegant structure:
Entity + Insight + Filters = Value
- Entity: The subject of analysis (company, brand, person, location, commodity)
- Insight: The specific data point or analytical output you need
- Filters: The dimensions that matter to your use case (space, time, industry, size, etc.)
This framework becomes the foundation for rational pricing. Instead of paying for access to entire data universes, you pay only for the specific combinations of entities, insights, and filters that deliver value to your organization.
An example framework being built
The Economics of Precision: $/MB Pricing That Makes Sense
Our pricing model operates on a simple $/MB basis that transparently incorporates three critical cost components:
Data Acquisition Costs
The actual cost of sourcing, licensing, and maintaining access to underlying datasets. Unlike traditional vendors who amortize these costs across forced subscriptions, we pass through only the marginal cost of the specific data you consume.
Compute Resources
Processing raw data into actionable insights requires sophisticated computation—from entity resolution and semantic mapping to real-time analytics and cross-dataset correlation. Our pricing reflects the actual computational resources required for your specific queries, not subsidized overhead from unused capacity.
Storage Infrastructure
Maintaining high-performance access to vast datasets requires significant storage infrastructure. Rather than spreading these costs across all customers equally, we allocate storage costs based on actual data retrieval patterns, ensuring you pay only for what you access.
This transparent, marginal cost approach ensures that vendors receive fair compensation for their data assets while providing users with unprecedented cost efficiency. The pricing scales with value—complex, computation-intensive insights cost more than simple lookups, and rare, high-value entities carry premium pricing while commodity data remains highly accessible.
Not All Data Is Created Equal: Personalized Value Recognition
Here’s the revolutionary insight traditional data vendors miss: data value is deeply contextual and personal to each use case. A satellite imagery feed showing agricultural patterns is invaluable to a commodities trader but irrelevant to a retail analyst. Social sentiment data about luxury brands matters enormously to a consumer goods company but provides little value to an infrastructure investor.
Yet current pricing models treat all data consumption equally. A one-size-fits-all subscription forces every customer to subsidize data streams that provide them zero value while overpaying for the specific insights that drive their business.
Carbon Arc’s framework-based approach recognizes this reality. Our pricing engine understands that:
- Depth: More granular data means more depth in the research
- Breadth: Simple broad strokes analyses must be efficient
This personalized approach to data valuation ensures that heavy users of specific data types receive volume efficiencies while occasional users aren’t penalized with fixed costs they can’t justify.
From CAPEX to OPEX: Transforming Data Economics
Perhaps the most transformative aspect of framework-based pricing is how it fundamentally changes data from a capital expenditure to an operational one. This shift has profound implications for how organizations approach data strategy, innovation, and resource allocation.
Traditional CAPEX Model: Risk and Rigidity
Under the conventional data feed model, organizations make large upfront commitments—often six or seven figures annually—for data access. This creates several problematic dynamics:
- Innovation paralysis: Teams become reluctant to experiment with new data sources due to high switching costs
- Budget rigidity: Data expenses are locked in regardless of changing business needs or market conditions
- Opportunity cost: Capital tied up in underutilized data feeds can’t be deployed for other strategic initiatives
- Risk concentration: Big bets on specific data vendors create vendor lock-in and strategic vulnerability
Framework-Based OPEX Model: Agility and Innovation
Carbon Arc’s consumption-based approach transforms data into a variable cost that scales with business value:
- Hypothesis-driven exploration: Teams can test new data sources or analytical approaches with minimal upfront commitment
- Resource optimization: Data spending automatically aligns with business results—successful projects scale up, unsuccessful ones wind down naturally
- Portfolio diversification: Organizations can explore multiple data sources simultaneously without massive capital allocation decisions
- Adaptive budgeting: Data costs flex with business cycles, providing natural cost control during downturns and growth capacity during expansions
The Experimentation Revolution
When data access costs approach zero for initial exploration, the innovation calculus changes completely. Consider these scenarios enabled by framework-based pricing:
Rapid Hypothesis Testing
A fintech startup can test whether social media sentiment predicts loan default rates by analyzing a small cohort across multiple data sources for under $100. Traditional approaches would require tens of thousands in upfront commitments before the first insight.
Cross-Dataset Discovery
A retail analyst can explore correlations between foot traffic, weather patterns, local events, and sales performance across different markets by consuming only the specific entity-insight combinations relevant to their hypothesis. No need to purchase entire datasets for exploratory analysis.
Competitive Intelligence Agility
Strategy teams can quickly assess new market opportunities by combining demographic, economic, and competitive data sources in novel ways—paying only for the specific insights that prove valuable rather than subscribing to comprehensive feeds “just in case.”
Academic and Research Applications
Universities and research institutions can access enterprise-grade data for specific studies without the prohibitive costs that traditionally limited academic research to publicly available datasets.
The Network Effects of Rational Pricing
As more organizations adopt framework-based data consumption, powerful network effects emerge:
- Data vendor incentives align: Providers focus on data quality and utility rather than vendor lock-in strategies
- Innovation acceleration: Lower barriers to data experimentation drive faster discovery of valuable insights and analytical approaches
- Market efficiency: Pricing signals reflect actual data value, encouraging investment in high-utility data sources while reducing waste
- Democratization: Smaller organizations gain access to data resources previously available only to large enterprises
The Path Forward
Framework-based pricing represents more than a billing innovation—it’s a fundamental reimagining of how data markets should operate. By aligning costs with actual consumption and value, Carbon Arc’s approach transforms data from a capital allocation challenge into a strategic asset that scales naturally with business success.
The traditional data feed monopoly relied on artificial scarcity and forced bundling. The future belongs to platforms that recognize data’s true value: not as a commodity to be hoarded, but as a utility to be consumed efficiently, explored freely, and applied intelligently.
When you can test any hypothesis for the cost of a lunch, explore any market for the price of a coffee, and validate any strategy for less than a dinner—that’s when real innovation begins. Framework-based pricing doesn’t just change how you pay for data. It changes how you think about what’s possible.
