How much service fee should a Web3 platform charge?

Written by: Gérard Cachon, Tolga Dizdarer, Gerry Tsoukalas

Compiled by: Luffy, Foresight News

Web3 aims to reduce reliance on intermediaries, thereby lowering service fees and giving users greater control over their own data and assets. For example, the artificial intelligence computing power service provided by Gensyn (a decentralized AI computing platform) costs only a small fraction of Amazon Web Services (AWS); Drife (a decentralized ride-hailing platform) promises to help drivers escape up to 30% commission exploitation by Uber.

However, despite the appealing concept of reducing costs for users, establishing reasonable fees and pricing standards requires the platform to find a balance among multiple interests. The most successful decentralized markets do not completely abandon fees, but rather combine “decentralized pricing” with a thoughtfully designed fee structure that can create added value, thus achieving a balance between supply and demand.

Based on our research, this paper will elaborate on the following topics: the role of pricing control and cost structure in platform economy and governance; why the “zero fee” model, regardless of how well-intentioned the designers are, is destined to fail; and how blockchain platforms should formulate pricing strategies. We propose a new model of “affine pricing” based on transaction volume, which can resolve the conflict between private information and market coordination.

Why Pricing and Fees are Important

The rise and fall of digital platforms depends on their ability to manage two core levers: pricing control and fee structure (i.e., the amount charged to buyers and sellers using its services). These two are not only revenue generation tools but also market design tools that shape user behavior and determine market outcomes.

Pricing control determines “who sets the transaction price.” For example, Uber uses centralized algorithms to set fares in order to optimize supply-demand balance and pricing stability; in contrast, Airbnb grants hosts the autonomy to set their own prices, only providing moderate guidance through algorithms. Each model focuses on different solutions: centralized pricing ensures collaborative efficiency in large-scale markets; decentralized pricing allows service providers to incorporate private information (such as costs, service quality, differentiation advantages, etc.) into their pricing strategies. Neither model is absolutely superior; their effectiveness depends on the specific application scenario.

The impact of the fee structure is not limited to platform revenue; it also determines which participants will enter the market and how the market operates. The Apple App Store charges a commission of up to 30%, which is used to filter high-quality app supply and provide funding support for platform infrastructure, but may cause dissatisfaction among app developers, although it usually does not directly affect users; in contrast, the high fees of the ticketing platform Ticketmaster, if alternative options exist, would drive artists and fans to turn to other channels. On the low-fee side, Facebook Marketplace's free product listing service has spawned scam issues; several near-zero-fee NFT platforms have led to a chaotic user experience due to an influx of low-quality NFTs.

The pattern is clear: excessively high fees will lead to a loss of suppliers; excessively low fees will harm the quality of services/products.

Many blockchain projects adopt a zero-commission model, based on the logic that by giving up their ability to extract value, they can provide better outcomes for suppliers and users. However, this perspective overlooks the crucial role that “well-designed fees” play in the effective operation of the market: fees are not merely a tool for taking a cut, but can also serve as a collaborative mechanism.

Trade-off between information and collaboration

The core contradiction in platform design lies in how to find a balance between “utilizing the private information of service providers” and “collaborating in the market to improve efficiency.” Our research indicates that the interaction between pricing controls and the fee structure determines whether this contradiction is resolved or exacerbated.

When the platform directly sets prices, it can more easily achieve supply-side coordination and competitive coordination among service providers. However, due to the inability to grasp the private costs of each supplier (such as operational costs, marginal costs, etc.), pricing often leads to mismatches for both supply and demand: prices may be too high for some users and too low for some suppliers. Additionally, the platform usually charges a commission based on the transaction amount, and this inefficient pricing ultimately leads to profit loss.

If service providers set prices autonomously, theoretically their prices can reflect true costs and service capabilities: low-cost providers can gain competitive advantages by lowering prices, thereby achieving better supply-demand matching and market efficiency. However, a lack of a coordinated pricing model may backfire in two ways.

When products or services are severely homogenized, it can easily trigger a price war. High-cost suppliers are forced to exit the market, leading to a decrease in supply; meanwhile, demand is often on the rise, ultimately weakening the platform's ability to meet market demand. At the same time, while the average price decline may benefit consumers, it directly impacts the platform's commission-based revenue model.

When products or services need to be paired together to maximize value, suppliers often price them too high. Although a large number of suppliers may flood the platform, the high prices they set will drive up the market average price, ultimately driving users away.

This is not purely a theoretical inference: in 2020, Uber tested the “Luigi Plan” in California, allowing drivers to set their own prices. The results showed that the fares set by drivers were generally too high, leading users to turn to other transportation platforms, and the plan was terminated after approximately one year of implementation.

Key conclusion: The above results are not coincidental, but rather an equilibrium outcome under standard commission contracts. Even optimizing commission contracts may still lead to such persistent market failures. Therefore, the core issue is not “how much commission the platform should charge,” but rather “how to design the fee structure to ensure that the market is effective for all participants.”

How to solve problems

Our research found that a targeted fee structure can cleverly solve market coordination problems while retaining the advantages of “pricing personalization.” This affine fee model adopts a “two-part tariff” mechanism, where service providers must pay the platform:

Fixed base fee for each transaction;

Floating fees: increase with the transaction volume (surcharge) or decrease with the transaction volume (discount fee).

This model will have a differentiated impact based on the supplier's costs and market positioning.

In this type of market, there are significant differences in supplier costs: some suppliers have naturally lower costs due to more advanced technology, access to renewable energy, or efficient cooling systems; while other suppliers, despite higher costs, can offer premium services such as high reliability.

In a traditional commission model, if market competition is excessive, low-cost GPU suppliers will set extremely aggressive low prices, capturing too large a market share, which in turn leads to the market distortions mentioned earlier: some suppliers exit, resulting in constrained transaction volumes, while the market average price is driven down.

For this scenario, the optimal strategy is “Transaction Volume Surcharge”: the more customers a supplier serves, the higher the fee that needs to be paid for each transaction.

This mechanism can create a “natural constraint” on aggressive low-cost suppliers, preventing them from occupying too much market share with unsustainable low prices, thereby maintaining market balance.

When the level of market competition is moderate or insufficient, the optimal strategy shifts to “volume discount fees”: the more customers a supplier serves, the lower the fees paid for each transaction. This mechanism incentivizes suppliers to expand transaction volumes through price reductions, effectively enhancing market competitiveness while avoiding prices falling below sustainable levels.

For example, on a decentralized social platform, creators with “higher user interaction” can be charged lower fees, encouraging them to set more competitive prices for paid content while attracting more users to participate.

The ingenious aspect of the affine fee mechanism is that it does not require the platform to grasp the specific costs of each supplier. The fee structure creates positive incentives, guiding suppliers to self-regulate based on their own private cost information. Low-cost suppliers can still gain an advantage by offering prices lower than those of high-cost competitors, but the fee structure prevents them from monopolizing the market in a way that harms the overall health of the ecosystem.

We validated through mathematical simulation that a reasonably calibrated “volume-based fee structure” can enable the platform to achieve over 99% theoretical optimal market efficiency. In the theoretical framework, its performance far exceeds that of the “centralized pricing” and “zero commission” models. The resulting market will possess the following characteristics:

Low-cost suppliers retain a competitive advantage but will not occupy excessive market share;

High-cost suppliers can continue to participate by focusing on “niche markets for differentiated services”;

The overall market has reached a more balanced equilibrium, with reasonable price differences.

The platform achieves sustainable revenue while enhancing market functions.

Moreover, the analysis indicates that the optimal cost structure depends on “observable market characteristics” rather than the “private cost information” of each supplier. When designing contracts, the platform can use observable signals such as “price” and “transaction volume” as proxy indicators for “implicit costs,” allowing suppliers to retain pricing power based on private information while also addressing the inherent coordination failure issues in a fully decentralized system.

The future development path of blockchain projects

Many blockchain projects have harmed their financial sustainability and reduced market efficiency by adopting traditional commission models or zero-fee models.

Our research confirms that a well-designed fee structure is not contrary to decentralization, but rather a core element in building an operational decentralized market.