Loopscale: Order book lending on Solana

Lending on Solana

While Ethereum’s DeFi TVL is still far from its 2021 peak, Solana’s TVL has experienced significant growth and is now at an all-time high (ATH).

Solana’s ecosystem makes it a sound choice for lending protocols, as evidenced by protocols like Solend, which reached close to $1b in deposits already in 2021. The FTX crash greatly affected these developments for the upcoming years. However, Solana lending protocols have shown resilience, giving rise to a new wave of growth.

From sub $1b in 2024, lending protocols on Solana now have a TVL of over $4b, led by Kamino, with over $3b in TVL, and Jupiter, with $750m in TVL.

The changing market conditions are now driving a shift towards more powerful and efficient lending markets.

These factors include:

• Market Maturity

• Asset Variety

• UX Expectations

• Ecosystem Interoperability

Just as the landscape is evolving, so too are lending protocols, leading to the development of new models.

Among the emerging implementations is Loopscale, which uses an order book model. By doing so, Loopscale came up with several interesting design choices, which make lending more powerful, including an easier way to loop funds.

We start this research by investigating the limitations of pool-based models and the emergence of alternative models. Then, we dive into Loopscale, its value proposition and unique features, and how they translate into practical benefits for users. We end with a forward look at the lending sector and some food for thought.

The Evolution of Lending

Most lending protocols, such as Aave and Compound, operate using a pool-based model, where anyone can deposit liquidity in a pool, which is then available for borrowing. On these platforms, the supply and borrow rates are algorithmically adjusted based on the utilisation rate (total borrowed/total supplied amount) of a specific pool.

Initially, protocols had limited design flexibility, as this choice was mainly restricted by the architecture of the Ethereum mainnet.

While this model is extremely versatile for bootstrapping and ensuring deep liquidity for pools with collateral assets, it leads to several limitations in terms of:

• Liquidity Fragmentation (New Asset Listing): Since each added asset necessitates a new pool with its own bootstrapped liquidity, this process inevitably fragments liquidity. From a user perspective, managing multiple positions also becomes more complex and requires active involvement.

• Poor Risk Pricing: Utilisation curves are an inefficient “one-size-fits-all” pricing mechanism, which leads to either risky or conservative terms. In fact, the rates of pools are often aligned with the riskiest collateral assets supported.

• Inefficient Capital Utilisation: Pool-based lending markets only pay interest on the capital being borrowed. However, the yield is distributed across all capital supplied, meaning that the interest earned by lenders is less than the interest paid by borrowers, leading to “deadweight capital”. Under a pool-based model, the capital sitting idle in pools, waiting to be borrowed, also receives a yield, which leads to the rate spread mentioned above.

To address some of these shortcomings, protocols like Euler, Kamino (V2), and Morpho (V1) adopted an iteration of the traditional pool-based model, introducing curated vaults.

This pragmatic approach does not require lending protocols to rebuild their tech stack from the ground up to transition to a new model, while still solving some of the limitations of the pool-based model. Under the curated vault model, vaults are managed by selected curators with bespoke research and risk-exposure skills who handle fund allocation, market selection, set rates, and loan structures. The curators’ work is to analyse the demand and allocate liquidity in a way that reaps the highest return for lenders.

Users can now select from different curators, managing specific isolated vaults tailored to different risk profiles, rather than being exposed to all assets supported by a pool.

Users also benefit from easier position management, as it is handled by curators who can easily allocate assets across new markets more quickly. As such, curated vaults make it easier to route liquidity to new assets in order to bootstrap liquidity.

Nonetheless, this model comes with its own tradeoffs.

For once, vault management is managed by third parties. This requires users to trust them, and it also raises questions about the alignment of interests between curators and users.

Under the curation model, curators are the ones setting the risk parameters, crafting the strategies, and rebalancing liquidity to get higher yields.

By migrating liquidity, curators are effectively competing with their own strategies and also affecting borrowers: as curators are incentivised to keep the utilisation rate high and deliver attractive lender APYs, this inflates rates and affects borrowers.

The curated model also fails to address some of the shortcomings of pool-based models:

• Value Leak from inefficient rates, which impair the capital efficiency of lending markets

• High cost to bootstrap new markets

• Liquidity is still fragmented across multiple isolated markets

• Still features volatile rates, which are not appealing to institutions.

• Lacks the flexibility to support new assets and credit products that require governance approval and the creation of new isolated pools.

While curated vaults offer more flexibility and risk management compared to generic pools by segmenting liquidity, they still operate on a modified pool principle. As the number of isolated markets or curated vaults proliferates to support an ever-growing variety of assets and risk profiles, this approach begins to resemble an order book conceptually. In an order book, each individual offer for lending or borrowing is its own ‘isolated market’ with specific terms, providing ultimate granularity.

As the market constantly evolves, it requires a flexible and future-proof infrastructure that can meet the new requirements.

While curator vaults have stretched the applications of the pool-based model, a new model is needed.

In particular, we have seen the emergence of a valid alternative: the order book model.

Why now?

While the conceptual benefits of an order book for lending have long been understood, their practical deployment faced significant hurdles. Previous order book implementations were often impractical due to the high costs of transactions on networks like Ethereum mainnet and technical limitations, presenting their own set of flaws in terms of scalability and capital efficiency.

The emergence of alternative networks such as Solana, with its low costs and high throughput, has finally made it feasible to create scalable and efficient order book lending markets.

While pool-based lending models have supported and enabled lending protocols to scale, order book models are the missing piece of the puzzle, providing the much-needed flexibility that the constantly evolving market requires.

This includes specifically being able to support institutional demand and all types of assets, such as yield-bearing RWA tokens (such as OnRe’s ONyc), AMM LP positions, Jupiter Liquidity Provider (JLP) tokens, Meteora Liquidity Provider (MLP) tokens, and LSTs ($7b+ TVL), giving users full control over their risk allocation.

The next section delves into Loopscale, offering tangible examples of its unique value proposition, key features, and future outlook for deploying an order book lending protocol on Solana.

Loopscale: Order Book Lending on Solana

Loopscale is an order book-based lending protocol on Solana, which currently has over $100m in deposited liquidity and $40m in active loans.

Unlike traditional lending platforms based on pools, Loopscale focuses on loans by enabling lenders to create tailored offers, giving them full control over defining a loan structure that meets their specific needs and risk profile.

These orders are then “listed” in an order book, according to the rate and other conditions, and matched by Loopscale’s matching engine.

However, Loopscale extends beyond a simple order book, offering powerful features such as automated, curated vaults and simplified looping to enhance user experience and capital efficiency.

For users looking to simplify this even more, Loopscale automates the process through its own “curated” vaults. The liquidity deposited in the vaults is available across all curator-approved markets. Each vault has a risk curator, setting a unique risk profile and strategy.

The results are differentiated strategies that cater to each user’s risk profile. While some users may be more comfortable with a reinsurance exposure (via ONyc) through the USDC OnRE, others may want to play it safer and deposit in the USDC Genesis vault, which conservatively diversifies liquidity across Loopscale markets.

Aside from traditional lending, Loopscale also support looping.

Loops allows users to get leverage exposure to yield-bearing assets (including JLP, ALP, digitSOL, ONyc, etc.).

At its core, a loop works by depositing collateral and borrowing more of the same asset deposited, so that both the initial position and the borrowed tokens earn yield. The leverage a user can be exposed to depends on the market LTV (Loan to Value) ratio.

Using LSTs as an example: in a typical loop, users deposit LSTs, then acquire the underlying asset, swap it back for LSTs, and use the LSTs as collateral to borrow more.

1. Deposit wstETH

2. Borrow ETH

3. Swap ETH for wstETH

4. Borrow ETH again to gain exposure to a higher yield on wstETH

However, it’s worth noting that loops are only effective when the LST yield exceeds the Borrow APY.

On Loopscale, the process is simplified and abstracted in one click.

With Loops, users can maximise the APR on their yield-bearing tokens:

Leverage loops, on the other hand, allow directional leverage traders of assets like stocks.

The Loopscale design directly addresses several of the shortcomings we highlighted above for pool-based models, leading to.

1. Liquidity Aggregation

The order book model can fix the fragmentation of pool-based markets. Loopscale addresses the liquidity fragmentation of the pool-based model and the lack of reusable capital within early implementations of the order book model with the creation of “Virtual Markets”, which enable lenders to seamlessly place their orders across multiple markets with a single deployment, eliminating the need to be constrained to a single market or manage multiple positions.

2. Efficient Rates

Each market on Loopscale is modular, with its own collateral types, lending rates and terms. This means that lenders can set rates for specific collateral and principal, as they are no longer constrained by the utilisation rate. Finally, every asset will have different rates according to its market rate in the order book (which could be influenced, among others, by the asset’s volatility), offering advantages in terms of collateral flexibility and risk management. In turn, this minimises deadweight capital and ensures the borrowing rate will match the supply rate exactly. If you remember, in the pool-based mode, “the yield is distributed across all capital supplied, meaning that the interest earned by lenders is less than the interest paid by borrowers”, while on Loopscale, interests are only paid out to capital utilised, ensuring rates match perfectly.

In particular, being able to offer fixed-rate and fixed-term loans allows Loopscale to cater to institutional demand. These market participants are, in fact, reluctant to accept volatile rates offered by the pool-based lending model, where rates are based on utilisation.

3. Optimised Capital Utilisation

Loopscale leverages “Optimised Yield” to minimise the unutilised capital in the order book that is waiting for a matching order. The way this works is simple: Loopscale routes this liquidity to MarginFi, ensuring lenders can “earn competitive yields until their orders are filled”.

4. Extended Asset Support

On Loopscale, it’s easy for the team to integrate with other protocols and leverage Solana’s asset composability, broadening support to assets which could not otherwise find traction on pool-based markets.

Combined, these translate into tangible benefits for users. Users have full control and granularity over their loan terms, collateral, and the markets in which they want to participate.

As lending markets compete across their rate offering, Loopscale’s model represents an improvement over rates calculated based on the utilisation rate of a pool. By directly matching orders, they are able to closely match rates, saving money for borrowers and increasing lenders' earnings.

On Loopscale, these instruments provide rate certainty, shifting onchain lending “from a variable-rate savings account into something closer to a bond or fixed-income instrument, a prerequisite for broader institutional adoption”.

Future Outlook and Conclusion

By combining modular markets with the flexibility and scalability of order books, Loopscale is scaling lending on Solana, directly addressing the inefficiencies of the traditional pool-based model.

This translated into direct user benefits, including customised rates and loan structures, improved rates, more competitive collateral pricing, and enhanced risk management.

As DeFi matures beyond crypto-native users and assets, the order book model can prove to be critical to scale accordingly, as a prerequisite for institutional capital to arrive onchain.

Interestingly enough, before diving into crypto-native lending, Loopscale’s team had already been focused on RWA-collateralised loans.

Currently, Loopscale supports several RWAs and exotic assets, and is working on further partnerships and integrations. Listing a new market is fairly easy, as it comes down to having an oracle and initial liquidity in the order book, which can be supplied by one of its vaults (e.g. USDC Genesis for lower-risk assets), specific-purpose vaults or individual lending positions.

As Solana benefits from the increased adoption of new token primitives, including billions in LSTs and LRTs assets, Staked SOL (now 60% of total SOL), Liquidity Positions, RWA assets, and more, reducing the barriers to onboarding new assets as collateral is key to improving market efficiency.

The viability of the order book lending model is confirmed by broader adoption, with protocols like Morpho launching a similar design for its V2.

Loopscale has demonstrated resilience following a hack in April 2025, shortly after its launch, where all funds were recovered. Dealing with more complex collateral comes with its own risks, which have to be properly assessed and mitigated, both from an operational and user interface perspective.

Accounting for those, the architecture of Loopscale can blossom by leveraging Solana’s tech stack and scaling up its platform seamlessly.

We’ll watch closely in the upcoming months to see how the order book model will perform at scale.

See you in the loop!