Why DLMM is a Better Foundation for On-chain Liquidity

The Automated Market Maker (AMM) model underlying a DeFi ecosystem shapes everything built on top of it. Liquidity depth, LP sustainability, and trader experience are downstream of design decisions made at the AMM level. When those decisions are not carefully considered, the effects are predictable: LPs pull out, liquidity thins, and traders route elsewhere.

Concentrated Liquidity Market Makers (CLMMs) were a real improvement over what came before. They gave Liquidity Providers (LPs) more control over capital deployment and addressed a genuine inefficiency in earlier AMM design. But they also introduced new problems, some of which only became clear at scale.

DLMMs approach the problem differently. The bin model and dynamic fee structure reflect different assumptions about who provides liquidity, how markets behave, and what makes a pool sustainable over time. This article looks at where those differences matter.

How We Got Here

AMM design has gone through a few distinct phases, each one responding to a limitation in what came before. When you understand that progression, you will easily see what problems DLMMs are actually solving.

The constant-product AMM

The formula at the heart of early AMMs is x × y = k. Two assets sit in a pool. Their quantities must always multiply to the same constant. When a trader buys one asset, they deposit the other, and the ratio shifts along a fixed curve. No order book, counterparty, or price feed required.

This worked well enough to bootstrap on-chain liquidity at a time when few alternatives existed. But it had a structural problem that became harder to ignore as more capital entered the system.

The constant-product curve covers every possible price from zero to infinity, and liquidity is spread uniformly across it. Most assets trade within a relatively narrow range at any given time. That means most of the capital in a constant-product pool sits at prices that rarely get touched. It earns no fees and dilutes returns for everyone in the pool.

Concentrated liquidity as the response

Uniswap v3 introduced concentrated liquidity in 2021 to address this directly. The idea was to let LPs choose which part of the price curve their capital covers. An LP who expects ETH to trade between $2,800 and $3,200 can put their full position to work in that band. When the price is inside that range, they earn fees on every trade. Capital efficiency improves significantly compared to the uniform distribution model.

The tradeoff is that LPs now have to manage their positions. A range set last week may be out of range today, which means the LP earns nothing until they rebalance. CLMMs reward participants who monitor and adjust their positions regularly. Passive LPs, or those without the tooling to manage positions actively, tend to underperform.

Fee rigidity is a separate issue. Fee tiers in a CLMM are fixed at pool creation. A pool set to 0.3% charges 0.3% whether the market is calm or moving fast. High-volume periods are often the riskiest for LPs, and a static fee does not account for that.

These were known limitations when CLMM became the dominant model. DLMM takes a different approach to both.

How Dynamic Liquidity Market Maker Models (DLMMs) Work

Imagine the price of ETH/USDC sitting at $3,000. A DLMM breaks the area around that price into small fixed-width intervals — say, $2,990–$3,000, $3,000–$3,010, $3,010–$3,020, and so on in both directions. Each of those intervals is a bin. Liquidity sits inside individual bins, not spread across a continuous curve.

When a trader swaps USDC for ETH, the trade fills from the bin at the current price. If the trade is small, it gets filled entirely within that bin, and the price barely moves. If it's large enough to exhaust the bin, it moves into the next one, and the price steps up accordingly. Each bin uses a constant-sum formula internally. Within a single bin, price is fixed, and assets swap at a 1:1 ratio until the bin is empty.

From an LP's perspective, depositing into a DLMM means placing capital into one or more bins around the current price. You don't set a range in the same open-ended way you do in a CLMM. The bin structure handles the pricing boundaries. You choose a deposit strategy, such as spot and curve, that determines how your capital is distributed across those bins.

• Spot: All your capital goes into the bins immediately around the current price. You earn the most fees when price stays close to where it is now, but you go out of range faster if it moves significantly.
• Curve: Your capital is spread across a wider band of bins, with more sitting near the center and less toward the edges. You give up some fee intensity in exchange for staying in range through more price movement.
• Bid-Ask: Your capital sits on either side of the current price with little in the middle. As price moves in one direction, you accumulate the asset it is moving toward. More of a directional bet than a neutral liquidity position.

The other significant difference is how fees work. Rather than a fixed tier set at pool creation, DLMM fees adjust based on measured market volatility. When price is moving fast and volume is high, the fee rate rises. When conditions are calm, it compresses. This means LPs automatically earn more during the periods when providing liquidity carries the most risk, without having to do anything to make that happen.

For traders, the bin model tends to produce more consistent depth around the active price. Because liquidity is organized around where trading is actually happening rather than spread across manually chosen ranges, thin spots are less common during normal market conditions.

The Advantages of the DLMM Model

The dynamic fee model is the most direct improvement over CLMM. LPs earn more during volatile periods without having to do anything because the pool adjusts automatically. This matters most for passive LPs, who in a CLMM would simply absorb the risk without additional compensation.

The bin structure also lowers the barrier to participation without sacrificing much in the way of capital efficiency. In a CLMM, setting a tight range requires a reasonably good read on where the price is going. Set it too wide, and you dilute your returns. Set it too narrow, and you go out of range quickly. The bin model removes that tradeoff. Liquidity stays organized around the active price by design, which means a less sophisticated LP can participate without needing to develop strong range intuition first.

Over time, these two things together tend to produce stickier liquidity. When passive LPs are not quietly bleeding from poor range management and static fees during volatile markets, they are more likely to stay in the pool. Deeper, more stable liquidity benefits traders and makes the pool more useful as a building block for other protocols.

That composability angle is worth noting separately. Bin-based positions are cleaner primitives than open-ended ranges. Vaults, structured products, and automated strategies are easier to build on top of a discrete, well-defined state than on a continuous curve where position boundaries can be anything. This is particularly relevant for newer ecosystems where that layer of infrastructure is still being built.

What This Means for Move Chain DeFi

Move chains are still early. Liquidity is thinner, LP bases are smaller, and there are fewer sophisticated market makers actively managing positions compared to more mature ecosystems like Ethereum. In that environment, the weaknesses of CLMM are more exposed. Passive LPs make up a larger share of the liquidity base, fragmentation across pools has a more immediate impact on depth, and the cost of getting the foundation wrong compounds faster.

The bin model also fits reasonably well with how Move chains execute transactions. Move's parallel execution model benefits from predictable, discrete state changes, which is closer to how bins work than to the continuous curve math of a CLMM.

More broadly, the liquidity culture of an ecosystem tends to reflect the tools available to it early on. If the dominant AMM model rewards only sophisticated, active LPs, that is the participant base that develops. If it is accessible to a wider range of participants without sacrificing depth, the ecosystem grows a broader and more resilient liquidity base. For Move chains still establishing that culture, the choice of AMM model matters more than it would on a chain where liquidity infrastructure is already deep and entrenched.

Conclusion

CLMM was a meaningful improvement over constant-product AMMs, and it remains a functional model. But it was designed around assumptions like active LP management, static fees, and open-ended ranges that do not hold evenly across all participants or all ecosystems.

DLMM addresses those assumptions directly. The bin model simplifies how liquidity is organized and priced. Dynamic fees compensate LPs more accurately for the risk they are taking on. The result is a model that works better for a wider range of participants without requiring them to operate like professional market makers.

For Move chain DeFi specifically, where liquidity infrastructure is still being established and the LP base skews toward less active participants, these properties matter. The foundation a DeFi ecosystem builds on tends to shape what grows on top of it, and DLMM is a more honest foundation for where these ecosystems are right now.