There’s a fundamental architectural decision every GTM platform has to make: how do you price company and contact data access? Most platforms meter it. We don’t. Here’s why that matters for anyone building in this space.
Look at any modern GTM stack and you’ll find metering everywhere. 10,000 contacts per month. 500 account lookups. API rate limits that force you to choose which accounts to enrich. Premium tiers for “unlimited” access that aren’t really unlimited.
This creates a perverse incentive: teams hoard their queries for “important” accounts. They build workarounds. They sample instead of scanning. They make decisions on partial data because full data is too expensive.
We’ve all been there. You’re trying to identify expansion opportunities across your customer base, but you can only afford to deeply analyze your top 100 accounts. What about account 101? What about the sleeping giant at position 500 that just hired a new CTO from one of your champions?
You’ll never know. The meter ran out!
When you remove metering from core company and contact data, something interesting happens: teams stop optimizing for API calls and start optimizing for outcomes.
Pattern Recognition at Scale: Without limits, you can run broad pattern detection across your entire addressable market. Not just your target accounts. Not just your predefined ICP. Everything. That’s how you discover that companies using Kubernetes are 3x more likely to buy your solution, but only if they also use Datadog and have more than 50 engineers.
Real-time Refresh Cycles: Static data ages like milk. When you don’t meter, teams can refresh constantly. Every account, every contact, every day if needed. Job changes get caught immediately. New technologies get detected as they’re implemented. Funding events trigger instant workflows.
Experimental Freedom: Want to test a hypothesis about companies with high engineering velocity? Run the query across 50,000 companies. Didn’t work? Try another angle. And another. When you meter data, every failed experiment costs money. When you don’t, failed experiments become learning opportunities.
Here’s what most vendors won’t tell you: the marginal cost of serving core company and contact data approaches zero at scale. Once you’ve built the infrastructure to ingest, process, and index millions of companies and contacts, serving that data is cheap. CPU cycles and bandwidth are commodities.
What’s expensive? Building and maintaining the data pipeline. Creating the matching algorithms. Ensuring quality and freshness. But these are fixed costs that don’t scale linearly with usage.
Metering core data is a business model choice, not a technical necessity.
When teams have unlimited access to core data, they build differently:
Wider Nets: Instead of precisely targeting 1,000 accounts, teams can cast intelligent nets across 1,000,000 and let engagement signals narrow the focus. More coverage, better conversion.
Deeper Intelligence: Every interaction enriches the dataset. Did someone open an email? Check their recent job history. Did a company visit your pricing page? Pull their entire tech stack. These micro-enrichments compound into macro-intelligence.
Faster Iteration: Product-market fit in GTM requires constant experimentation. New segments, new messages, new sequences. When data is metered, each experiment needs budget approval. When it’s unlimited, teams can move at the speed of ideas.
Here’s the counterintuitive truth: not metering core data creates a stronger business moat than metering it.
When you meter, customers constantly evaluate whether they’re getting ROI on each query. They build abstraction layers. They multi-source. They’re always one price increase away from shopping around.
When you don’t meter, customers build their entire workflow around unlimited access. They stop thinking about data costs and start thinking about business outcomes. The switching cost isn’t just moving data; it’s redesigning processes built on abundance.
The next generation of GTM platforms will be built on AI that needs to see everything to work properly. You can’t train effective ICP models on sampled data. You can’t predict buying intent from partial signals. You can’t personalize at scale when you’re rationing lookups.
The platforms that win will be those that recognize a simple truth: in GTM, data isn’t the product. Intelligence is the product. And intelligence requires unlimited access to the full picture, not carefully rationed glimpses.
That’s why we don’t meter core data at RevenueBase. Not as a pricing gimmick or a growth hack, but as a fundamental architectural decision about how modern GTM should work.
If you’re building in this space, ask yourself: are you optimizing for margin on data delivery, or for customer outcomes? The answer will determine whether you’re building for yesterday’s market or tomorrow’s.
Want to discuss GTM architecture? Reach out. We’re always interested in talking shop with other builders in the space.
