When it comes to licensing your company’s data to AI labs, you have two fundamental options: work through a broker who shops your data to multiple buyers, or go direct to a lab yourself. The choice you make has significant implications for how much money you receive, what exclusivity obligations you take on, and how long the entire process takes. This guide breaks down the full comparison so you can make an informed decision before entering any deal.
What a Data Broker Does
A data broker acts as your agent in the AI data market. They take your business data, prepare a structured manifest that describes what you have, then shop that manifest to multiple AI labs simultaneously. Their job is to understand which labs are currently buying, what price points are realistic for your data type, and how to structure a deal that works for both parties.
Brokers handle the parts of the process that most business owners do not have the time, relationships, or technical knowledge to manage themselves: identifying the right buyers, negotiating terms, managing data room logistics, and navigating legal documentation. In exchange, they charge a success fee, typically in the range of 10 to 15 percent of the total deal value, paid only when the transaction closes.
Key broker platforms in this space include Polyshares, which operates a non-exclusive model and charges its success fee to the buyer side rather than the seller, and Troveo, which also aggregates and packages business datasets for AI lab buyers. Both have established relationships with major buyers and active deal pipelines.
What Going Direct Looks Like
Going direct means approaching an AI lab on your own, without a broker intermediary. Companies like Scale AI, Mercor, and similar data acquisition teams do accept inbound submissions from businesses. It is technically possible to initiate a direct conversation.
The challenge is that without an existing relationship, your cold outreach to a lab’s data acquisition team may sit in a queue for weeks or never receive a substantive response. Labs receive enormous volumes of inbound pitches. Brokers who have pre-existing relationships and who bring vetted, structured data tend to get prioritized. Even when a direct conversation does happen, you are negotiating without market knowledge: you do not know what comparable datasets have sold for, which labs are currently most aggressive buyers, or what deal structure is standard. That information asymmetry typically costs you money.
The Fee Structure Compared
On the surface, going direct sounds financially superior: keep 100 percent of the deal value versus paying a broker 10 to 15 percent. But this math is often misleading.
A broker who knows the market can frequently negotiate a total deal value significantly higher than what you would achieve on your own. If a broker gets you $400,000 where you would have closed at $280,000 directly, the 12 percent broker fee ($48,000) still leaves you with $352,000, which is $72,000 more than the direct deal. The broker paid for themselves and then some.
This is the core argument for using a broker: competitive tension. When a broker shops your data to three or four labs simultaneously, those labs know they are competing. That dynamic produces better terms for the seller. In a direct approach, the lab knows they are the only buyer at the table. That changes the negotiation entirely.
Additionally, Polyshares specifically charges its success fee to the buyer side in many deal structures, meaning the seller receives the full agreed price rather than a net-of-commission figure. For those deals, the comparison is not 100 percent vs 85 percent; it is 100 percent vs whatever you could have negotiated alone.
Exclusivity Implications
Exclusivity terms are where the broker vs direct comparison becomes particularly important for sellers to understand.
When you go direct to a single AI lab, that lab will almost always require exclusivity as a condition of the deal. They want to know that the dataset they are paying for will not simultaneously be licensed to a competitor. Exclusivity means you give up the right to sell the same data to anyone else for some defined period, often 12 to 24 months or longer.
Brokers operating non-exclusive models change this calculation completely. Polyshares, for example, operates on a non-exclusive basis by default, meaning they can submit your data manifest to multiple buyers simultaneously and the deal structure does not require you to lock out other potential buyers unless the winning lab specifically negotiates for it and pays a premium for that exclusivity. When exclusivity is sold, it is sold at a price that compensates you for the limitation. That is very different from simply accepting an exclusivity clause because a direct buyer insisted on it.
The practical difference: a non-exclusive arrangement means your data can potentially be licensed to multiple buyers over time, multiplying total lifetime value beyond the initial transaction.
Which Gets You More Money?
For the vast majority of small and mid-sized businesses, working with a broker will produce a higher net outcome than going direct. This is not a universal law, but it reflects the practical reality of the market.
The reasons come down to three factors: relationships, information, and competition. Brokers have buyer relationships that get deals prioritized. Brokers have deal history that tells them what your data is actually worth in current market conditions. And brokers create competitive tension among multiple buyers that pushes prices upward.
Most business owners doing this for the first time have none of those advantages. They are negotiating cold, without comparable data, against buyers who close deals like this every month. The odds of leaving money on the table in a direct approach are high.
For a detailed look at how platform deal structures compare across the major brokers, see our breakdown of Polyshares vs Mode vs DataSupply.
When Going Direct Makes Sense
There are scenarios where a direct approach is the right call.
Very large datasets: If you are a mid-market or enterprise business with an exceptionally large, well-documented dataset, you may have enough leverage to negotiate directly. Labs will make time for truly exceptional data at scale, and your size may eliminate the need for broker relationships to get in the door.
Established lab relationships: If you or someone on your leadership team already has a professional relationship inside a major lab’s data acquisition function, that warm connection changes the calculus. Relationship deals close differently than cold pitches.
Internal legal and compliance resources: Data deals involve complex agreements covering IP rights, data governance, privacy compliance, permitted use cases, and indemnification. If you have a capable in-house legal team that has experience with data licensing agreements, you can handle the documentation yourself. Most SMBs do not have this resource, and a broker’s experience navigating deal documentation has real value.
The Hybrid Approach
The smartest move for most business owners is a hybrid strategy: start with a broker assessment to establish your market value, then make a more informed decision about whether to proceed through the broker or explore a direct conversation.
The broker assessment costs you nothing and requires no data transfer. It simply gives you a market-informed view of what your data might be worth, which labs are likely interested, and what deal structure is realistic. Armed with that information, you are in a much stronger position if you later decide to approach a lab directly. You know your number. You know the terms that are standard. You are not negotiating blind.
For most businesses that go through this process, the broker simply closes the deal because they do it better, faster, and at a higher price. But having the market information changes the dynamic in your favor regardless of which path you ultimately take.
To understand what drives your dataset’s value before entering any process, see our guide on how to sell company data to AI labs.
Our Recommendation
For most small and mid-sized businesses approaching AI data licensing for the first time, the broker path is the right starting point. Specifically, we recommend Polyshares as the primary broker option based on three key advantages: their non-exclusive model protects your ability to pursue additional buyers, their seller-side fee structure means you are not paying the commission out of your deal proceeds in most cases, and their net 30 to 60 payout schedule is among the fastest in the market.
Submit your business for a free assessment at Polyshares here. There is no data transferred at the assessment stage, no commitment required, and no cost. You simply describe your business and find out whether a deal is worth pursuing. That information alone is valuable, and in many cases it is the first step toward a six-figure licensing transaction.
If you want to compare platforms before deciding, Mode.inc is a strong secondary option, particularly for businesses with smaller teams or niche professional verticals. Running assessments through both platforms costs nothing and gives you the most complete picture of what your data could yield.
The data you have built over years of operation has real value in today’s AI market. The question is not whether to pursue it. The question is how to pursue it in a way that puts the most money in your pocket.
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For a full breakdown of every platform buying business data for AI and how they compare, see companies buying business data for AI training in 2026.
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