Directed Acyclic Graph (DAG) in Blockchain

What is a DAG and How Does it Differ from a Blockchain?

A Directed Acyclic Graph (DAG) is a type of data structure consisting of vertices (nodes) connected by directed edges that never form a cycle. In the context of distributed ledgers, a DAG-based ledger organizes transactions or events in a web-like graph rather than a single sequential chain. This means that unlike a traditional blockchain where each new block references only one predecessor (forming a linear chain), a node in a DAG may reference multiple previous transactions or blocks. As a result, many transactions can be confirmed in parallel, rather than strictly one-by-one in chronological blocks.

To illustrate the difference, if a blockchain looks like a long chain of blocks (each block containing many transactions), a DAG-based ledger looks more like a tree or web of individual transactions. Every new transaction in a DAG can attach to (and thereby validate) one or more earlier transactions, instead of waiting to be packaged into the next single block. This structural difference leads to several key distinctions:

• Parallel Validation: In blockchains, miners/validators add one block at a time to the chain, so transactions are confirmed in batches per new block. In DAGs, multiple transactions (or small “blocks” of transactions) can be added concurrently, since each can attach to different parts of the graph. This parallelization means DAG networks don’t have to wait for a single long chain to grow one block at a time.
• No Global Sequential Order: A blockchain inherently creates a total order of transactions (every block has a definite place in one sequence). A DAG ledger, by contrast, forms a partial order of transactions. There is no single “latest block” that all transactions queue for; instead, many tips of the graph can coexist and be extended simultaneously. Consensus protocols are then needed to eventually sort out or agree on the order or validity of transactions in the DAG.
• Transaction Confirmation: In a blockchain, transactions are confirmed when they are included in a mined/validated block and that block becomes part of the accepted chain (often after more blocks are added on top). In DAG systems, a new transaction itself helps confirm previous transactions by referencing them. For example, in IOTA’s Tangle (a DAG), each transaction must approve two previous transactions, effectively having users collaboratively validate each other’s transactions. This removes the strict division between “transaction creators” and “validators” that exists in blockchain mining – every participant issuing a transaction also does a bit of validation work.

Importantly, a blockchain is actually a special case of a DAG – a DAG that has been constrained to a single chain of blocks. Both are forms of distributed ledger technology (DLT) and share goals like immutability and decentralization. However, DAG-based ledgers are “blockless” or multi-parent in structure, which gives them different properties in practice. Traditional blockchains like Bitcoin and Ethereum use sequential blocks and often discard any competing blocks (forks), whereas DAG ledgers attempt to incorporate and arrange all transactions without discarding any, as long as they’re not conflicting. This fundamental difference lays the groundwork for the contrasts in performance and design detailed below.

Technical Comparison: DAG vs. Blockchain Architecture

To better understand DAGs vs blockchains, we can compare their architectures and validation processes:

• Data Structure: Blockchains store data in blocks linked in a linear sequence (each block contains many transactions and points to a single previous block, forming one long chain). DAG ledgers use a graph structure: each node in the graph represents a transaction or an event block, and it can link to multiple previous nodes. This directed graph has no cycles, meaning if you follow the links “backwards” you can never loop back to a transaction you started from. The lack of cycles allows a topological ordering of transactions (a way to sort them so that every reference comes after the referenced transaction). In short, blockchains = one-dimensional chain, DAGs = multi-dimensional graph.
• Throughput and Concurrency: Because of the structural differences, blockchains and DAGs handle throughput differently. A blockchain, even under optimal conditions, adds blocks one by one (often waiting for each block to be validated and propagated network-wide before the next one). This inherently limits transaction throughput – for example, Bitcoin averages 5–7 transactions per second (TPS) and Ethereum ~15–30 TPS under the classic proof-of-work design. DAG-based systems, by contrast, allow many new transactions/blocks to enter the ledger concurrently. Multiple branches of transactions can grow simultaneously and later mesh together, dramatically increasing potential throughput. Some modern DAG networks claim throughput in the thousands of TPS, approaching or exceeding traditional payment networks in capacity.
• Transaction Validation Process: In blockchain networks, transactions wait in a mempool and are validated when a miner or validator packages them into a new block, then other nodes verify that block against the history. In DAG networks, validation is often more continuous and decentralized: each new transaction carries out a validation action by referencing (approving) earlier transactions. For example, each transaction in IOTA’s Tangle must confirm two previous transactions by checking their validity and doing a small proof-of-work, thereby “voting” for those transactions. In Nano’s block-lattice DAG, each account’s transactions form their own chain and are validated via votes by representative nodes (more on this later). The net effect is that DAGs spread out the work of validation: rather than a single block producer validating a batch of transactions, every participant or many validators concurrently validate different transactions.
• Consensus Mechanism: Both blockchains and DAGs need a way for the network to agree on the state of the ledger (which transactions are confirmed and in what order). In blockchains, consensus often comes from Proof of Work or Proof of Stake producing the next block and the rule of “longest (or heaviest) chain wins”. In DAG ledgers, consensus can be more complex since there isn’t a single chain. Different DAG projects use different approaches: some use gossip protocols and virtual voting (as in Hedera Hashgraph) to come to agreement on transaction order, others use Markov Chain Monte Carlo tip selection (IOTA’s early approach) or other voting schemes to decide which branches of the graph are preferred. We will discuss specific consensus methods in DAG systems in a later section. Generally, reaching network-wide agreement in a DAG can be faster in terms of throughput, but it requires careful design to handle conflicts (like double-spend attempts) since multiple transactions can exist in parallel before final ordering.
• Fork Handling: In a blockchain, a “fork” (two blocks mined at nearly the same time) results in one branch eventually winning (longest chain) and the other being orphaned (discarded), which wastes any work done on the orphan. In a DAG, the philosophy is to accept forks as additional branches of the graph rather than waste them. The DAG will incorporate both forks; the consensus algorithm then determines which transactions end up confirmed (or how conflicting transactions are resolved) without throwing away all of one branch. This means no mining power or effort is wasted on stale blocks, contributing to efficiency. For example, Conflux’s Tree-Graph (a PoW DAG) attempts to include all blocks in the ledger and orders them, rather than orphaning any, thereby utilizing 100% of produced blocks.

In summary, blockchains offer a simpler, strictly ordered structure where validation is block-by-block, whereas DAGs provide a more complex graph structure allowing asynchronous and parallel transaction processing. DAG-based ledgers must employ additional consensus logic to manage this complexity, but they promise significantly higher throughput and efficiency by utilizing the network’s full capacity rather than forcing a single-file queue of blocks.

Benefits of DAG-Based Blockchain Systems

DAG architectures were introduced primarily to overcome the limitations of traditional blockchains in scalability, speed, and cost. Here are the key benefits of DAG-based distributed ledgers:

• High Scalability & Throughput: DAG networks can achieve high transaction throughput because they handle many transactions in parallel. Since there is no single chain bottleneck, the TPS (transactions per second) can scale with network activity. In fact, some DAG protocols have demonstrated throughput on the order of thousands of TPS. For example, Hedera Hashgraph has the capacity to process 10,000+ transactions per second in the base layer, far outpacing Bitcoin or Ethereum. In practice, Hedera has demonstrated finalizing transactions in about 3–5 seconds, compared to the minutes or longer confirmation times on PoW blockchains. Even DAG-based smart contract platforms like Fantom have achieved near-instant finality (~1–2 seconds) for transactions under normal loads. This scalability makes DAGs attractive for applications requiring high volume, such as IoT microtransactions or real-time data streams.
• Low Transaction Costs (Feeless or Minimal Fees): Many DAG-based ledgers boast negligible fees or even feeless transactions. By design, they often don’t rely on miners expecting block rewards or fees; for instance, in IOTA and Nano, there are no mandatory transaction fees – a crucial property for micro-payments in IoT and everyday use. Where fees exist (e.g., Hedera or Fantom), they tend to be very low and predictable, since the network can handle load without bidding wars for limited block space. Hedera transactions cost around $0.0001 (a ten-thousandth of a dollar) in fees, a tiny fraction of typical blockchain fees. Such low costs open the door to use cases like high-frequency transactions or tiny payments which would be infeasible on fee-heavy chains. Also, because DAGs include all valid transactions rather than dropping some in case of forks, there’s less “wasted” work – which indirectly helps keep costs down by utilizing resources efficiently.
• Fast Confirmation and Low Latency: In DAG ledgers, transactions don’t need to wait for inclusion in a global block, so confirmation can be faster. Many DAG systems achieve quick finality – the point at which a transaction is considered permanently confirmed. For example, Hedera Hashgraph’s consensus typically finalizes transactions within a few seconds with 100% certainty (ABFT finality). Nano's network often sees transactions confirmed in <1 second thanks to its lightweight voting process. This low latency enhances user experience, making transactions appear nearly instant, which is important for real-world payments and interactive applications.
• Energy Efficiency: DAG-based networks often do not require the intensive proof-of-work mining that many blockchains use, making them far more energy-efficient. Even compared to proof-of-stake blockchains, some DAG networks use minimal energy per transaction. For instance, a single Hedera Hashgraph transaction consumes on the order of 0.0001 kWh (kilowatt-hour) of energy. This is several orders of magnitude less than Bitcoin (which can be hundreds of kWh per transaction) or even many PoS chains. The efficiency comes from eliminating wasteful computations (no mining race) and from not discarding any transaction attempts. If blockchain networks were to switch to DAG-based models universally, the energy savings could be monumental. The carbon footprint of DAG networks like Hedera is so low that its overall network is carbon-negative when offsets are considered. Such energy efficiency is increasingly crucial for sustainable Web3 infrastructure.
• No Mining & Democratized Validation: In many DAG models, there is no distinct miner/validator role that ordinary users can’t perform. For example, every IOTA user who issues a transaction is also helping validate two others, essentially decentralizing the validation work to the edges of the network. This can reduce the need for powerful mining hardware or staking large amounts of capital to participate in consensus, potentially making the network more accessible. (However, some DAG networks do still use validators or coordinators – see the discussion on consensus and decentralization later.)
• Smooth Handling of High Traffic: Blockchains often suffer from mempool backlogs and fee spikes under high load (since only one block at a time can clear transactions). DAG networks, due to their parallel nature, generally handle traffic spikes more gracefully. As more transactions flood the network, they simply create more parallel branches in the DAG, which the system can process concurrently. There is less of a hard cap on throughput (scalability is more “horizontal”). This leads to better scalability under load, with fewer delays and only modest increases in confirmation times or fees, up to the capacity of the nodes’ network and processing power. In essence, a DAG can absorb bursts of transactions without congesting as quickly, making it suitable for use cases that involve bursts of activity (e.g., IoT devices all sending data at once, or a viral DApp event).

In summary, DAG-based ledgers promise faster, cheaper, and more scalable transactions than the classical blockchain approach. They aim to support mass adoption scenarios (micropayments, IoT, high-frequency trading, etc.) that current mainstream blockchains struggle with due to throughput and cost constraints. These benefits, however, come with certain trade-offs and implementation challenges, which we will address in later sections.

Consensus Mechanisms in DAG-Based Platforms

Because DAG ledgers don’t naturally produce a single chain of blocks, they require innovative consensus mechanisms to validate transactions and ensure everyone agrees on the ledger state. Different projects have developed different solutions tailored to their DAG architecture. Here we outline some notable consensus approaches used by DAG-based platforms:

• IOTA’s Tangle – Tip Selection and Weighted Voting: IOTA’s Tangle is a DAG of transactions designed for the Internet of Things (IoT). In IOTA’s original model, there are no miners; instead, every new transaction must do a small Proof of Work and approve two previous transactions (these are the “tips” of the graph). This tip selection is often done via a Markov Chain Monte Carlo (MCMC) algorithm that probabilistically chooses which tips to approve, favoring the heaviest subtangle to prevent fragmentation. Consensus in early IOTA was partly achieved by this cumulative weight of approvals – the more future transactions indirectly approve yours, the more “confirmed” it becomes. However, to secure the network in its infancy, IOTA relied on a temporary centralized Coordinator node that issued periodic milestone transactions to finalize the Tangle. This was a major criticism (centralization) and is being removed in the upgrade known as “Coordicide” (IOTA 2.0). In IOTA 2.0, a new consensus model applies a leaderless Nakamoto-style consensus on a DAG. Essentially, nodes perform on-tangle voting: when a node attaches a new block, that block implicitly votes on the validity of the transactions it references. A committee of validator nodes (chosen via a staking mechanism) issues validation blocks as votes, and a transaction is confirmed when it accumulates enough weighted approvals (a concept called approval weight). This approach combines the idea of the heaviest DAG (similar to longest chain) with explicit voting to achieve consensus without a coordinator. In short, IOTA’s consensus evolved from tip selection + Coordinator to a fully decentralized voting on DAG branches by nodes, aiming for security and quick agreement on the ledger state.
• Hedera Hashgraph – Gossip and Virtual Voting (aBFT): Hedera Hashgraph uses a DAG of events coupled with an asynchronous Byzantine Fault-Tolerant (aBFT) consensus algorithm. The core idea is “gossip about gossip”: each node rapidly gossips signed information about transactions and about its gossip history to other nodes. This creates a Hashgraph (the DAG of events) where every node eventually knows what every other node has gossiped, including the structure of who heard what and when. Using this DAG of events, Hedera implements virtual voting. Instead of sending out actual vote messages for ordering transactions, nodes simulate a voting algorithm locally by analyzing the graph of gossip connections. Leemon Baird’s Hashgraph algorithm can deterministically calculate how a theoretical round of votes on transaction order would go, by looking at the “gossip network” history recorded in the DAG. This yields a consensus timestamp and a total order of transactions that is fair and final (transactions are ordered by the median time they were received by the network). Hashgraph’s consensus is leaderless and achieves aBFT, meaning it can tolerate up to 1/3 of nodes being malicious without compromising consensus. In practice, Hedera’s network is governed by a set of 39 known organization-run nodes (the Hedera Council), so it’s permissioned but geographically distributed. The benefit is extremely fast and secure consensus: Hedera can reach finality in seconds with guaranteed consistency. The Hashgraph consensus mechanism is patented but has been open-sourced as of 2024, and it showcases how DAG + innovative consensus (gossip & virtual voting) can replace a traditional blockchain protocol.
• Fantom’s Lachesis – Leaderless PoS aBFT: Fantom is a smart contract platform that uses a DAG-based consensus called Lachesis. Lachesis is an aBFT Proof-of-Stake protocol inspired by Hashgraph. In Fantom, each validator node assembles received transactions into an event block and adds it to its own local DAG of events. These event blocks contain transactions and references to earlier events. Validators gossip these event blocks to each other asynchronously – there’s no single sequence in which blocks must be produced or agreed upon. As event blocks propagate, the validators periodically identify certain events as milestones (or “root event blocks”) once a supermajority of nodes have seen them. Lachesis then orders these finalized events and commits them to a final Opera Chain (a traditional blockchain data structure) that acts as the ledger of confirmed blocks. In essence, the DAG of event blocks allows Fantom to achieve consensus asynchronously and very fast, then the final outcome is a linear chain for compatibility. This yields about 1–2 second finality for transactions on Fantom. Lachesis has no miners or leaders proposing blocks; all validators contribute event blocks and the protocol deterministically orders them. The consensus is secured by a Proof-of-Stake model (validators must stake FTM tokens and are weighted by stake). Lachesis is also aBFT, tolerating up to 1/3 faulty nodes. By combining DAG concurrency with a final chain output, Fantom achieves high throughput (several thousand TPS in tests) while remaining EVM-compatible for smart contracts. It’s a good example of using a DAG internally to boost performance, without exposing a DAG’s complexity to the application layer (developers still see a normal chain of transactions in the end).
• Nano’s Open Representative Voting (ORV): Nano is a payment cryptocurrency that uses a unique DAG structure called a block-lattice. In Nano, each account has its own blockchain (account-chain) that only the account owner can update. All these individual chains form a DAG, since transactions from different accounts link asynchronously (a send in one account-chain references a receive in another, etc.). Consensus in Nano is achieved via a mechanism called Open Representative Voting (ORV). Users designate a representative node for their account (this is a weight delegation, not locking up funds), and these representatives vote on the validity of transactions. Every transaction is settled individually (there are no blocks bundling multiple txns) and is considered confirmed when a supermajority (e.g. >67%) of the voting weight (from representatives) agrees on it. Since honest account owners won’t double-spend their own funds, forks are rare and usually only caused by malicious attempts, which reps can quickly vote to reject. Finality is typically achieved in under a second for each transaction. ORV is similar to Proof-of-Stake in that voting weight is based on account balances (stake), but there is no staking reward or fee – representatives are voluntary nodes. The lack of mining and block production means Nano can operate feelessly and efficiently. However, it relies on a set of trusted representatives being online to vote, and there’s an implicit centralization in which nodes accumulate large voting weight (though users can switch reps anytime, maintaining decentralization control in the hands of users). Nano’s consensus is lightweight and optimized for speed and energy efficiency, aligning with its goal of being a fast, feeless digital cash.
• Other Notable Approaches: Several other DAG-based consensus protocols exist. Hedera Hashgraph and Fantom Lachesis we covered; beyond those:
  • Avalanche Consensus (Avalanche/X-Chain): Avalanche uses a DAG-based consensus where validators repeatedly sample each other in a randomized process to decide which transactions or blocks to prefer. The Avalanche X-Chain (exchange chain) is a DAG of transactions (UTXOs) and achieves consensus via this network sampling method. Avalanche’s protocol is probabilistic but extremely fast and scalable – it can finalize transactions in ~1 second and reportedly handle up to 4,500 TPS per subnet. Avalanche’s approach is unique in combining DAG data structures with a metastable consensus (Snowball protocol), and it’s secured by Proof-of-Stake (anyone can be a validator with sufficient stake).
  • Conflux Tree-Graph: Conflux is a platform that extended Bitcoin’s PoW into a DAG of blocks. It uses a Tree-Graph structure where blocks reference not just one parent but all known previous blocks (no orphaning). This allows Conflux to use Proof-of-Work mining but keep all forks as part of the ledger, leading to much higher throughput than a typical chain. Conflux can thus achieve on the order of 3–6k TPS in theory, using PoW, by having miners produce blocks continually without waiting for a single chain. Its consensus then orders these blocks and resolves conflicts by a heaviest subtree rule. This is an example of a hybrid PoW DAG.
  • Hashgraph Variants and Academic Protocols: There are numerous academic DAG protocols (some implemented in newer projects): SPECTRE and PHANTOM (blockDAG protocols aimed at high throughput and fast confirmation, from DAGlabs), Aleph Zero (a DAG aBFT consensus used in Aleph Zero blockchain), Parallel Chains / Prism (research projects splitting transaction confirmation into parallel subchains and DAGs), and recent advancements like Sui’s Narwhal & Bullshark which use a DAG mempool for high throughput and a separate consensus for finality. While not all of these have large-scale deployments, they indicate a rich field of research. Many of these protocols differentiate between availability (writing lots of data fast to a DAG) and consistency (agreeing on one history), trying to get the best of both.

Each DAG platform tailors its consensus to its needs – whether it’s feeless microtransactions, smart contract execution, or interoperability. A common theme, however, is avoiding a single serial bottleneck: DAG consensus mechanisms strive to allow lots of concurrent activity and then use clever algorithms (gossip, voting, sampling, etc.) to sort things out, rather than constraining the network to a single block producer at a time.

Case Studies: Examples of DAG-Based Blockchain Projects

Several projects have implemented DAG-based ledgers, each with unique design choices and target use cases. Below we examine some prominent DAG-based platforms:

• IOTA (The Tangle): IOTA is one of the first DAG-based cryptocurrencies, designed for the Internet of Things. Its ledger, called the Tangle, is a DAG of transactions where each new transaction confirms two previous ones. IOTA’s goal is to enable feeless microtransactions between IoT devices (paying tiny amounts for data or services). It launched in 2016, and to bootstrap security it used a Coordinator node (run by the IOTA Foundation) to prevent attacks on the early network. IOTA has been working on “Coordicide” to fully decentralize the network by introducing a voting consensus (as described earlier) where nodes vote on conflicting transactions using a leaderless Nakamoto consensus on the heaviest DAG. In terms of performance, IOTA can, in theory, achieve very high throughput (the protocol doesn’t set a hard TPS limit; more activity actually helps it confirm transactions faster). In practice, testnets have demonstrated hundreds of TPS, and the upcoming IOTA 2.0 is expected to scale well for IoT demand. Use cases for IOTA revolve around IoT and data integrity: e.g., sensor data streaming with integrity proofs, vehicle-to-vehicle payments, supply chain tracking, and even decentralized identity (the IOTA Identity framework allows issuing and verifying digital credentials/DIDs on the Tangle). IOTA does not natively support smart contracts on its base layer, but the project has introduced a parallel Smart Contracts framework and tokens on a secondary layer to enable more complex DApp functionality. A notable feature of IOTA is its zero fees, which is enabled by requiring a small PoW by the sender instead of charging a fee – this makes it attractive for high-volume, low-value transactions (e.g., a sensor sending data every few seconds for a negligible cost).
• Hedera Hashgraph (HBAR): Hedera is a public distributed ledger that uses the Hashgraph consensus algorithm (invented by Dr. Leemon Baird). Hedera started in 2018 and is governed by a council of large organizations (Google, IBM, Boeing, and others) who run the initial set of nodes. Unlike most others, Hedera is permissioned in governance (only approved council members run consensus nodes currently, up to 39 nodes) though anyone can use the network. Its Hashgraph DAG enables very high throughput and fast finality – Hedera can process over 10,000 TPS with finality in 3-5 seconds under optimal conditions. It achieves this with the aBFT gossip-based consensus described earlier. Hedera emphasizes enterprise and Web3 use cases that need reliability at scale: its network offers services for tokenization (Hedera Token Service), a Consensus Service for tamper-proof event logging, and a Smart Contract service (which is EVM-compatible). Notable applications on Hedera include supply chain provenance (e.g., Avery Dennison’s apparel tracking), high-volume NFT minting (low fees make minting NFTs inexpensive), payments and micropayments (like ad tech micropayments), and even decentralized identity solutions. Hedera has a DID method registered with W3C and frameworks like Hedera Guardian to support verifiable credentials and regulatory compliance (for example, tracking carbon credits). A key feature is Hedera’s strong performance combined with claimed stability (the Hashgraph algorithm guarantees no forks and mathematically proven fairness in ordering). The trade-off is that Hedera is less decentralized in node count than open networks (by design, with its governance model), though the council nodes are located globally and the plan is to eventually increase openness. In summary, Hedera Hashgraph is a prime example of a DAG-based DLT targeting enterprise-grade applications, with an emphasis on high throughput, security, and governance.
• Fantom (FTM): Fantom is a smart contract platform (Layer-1 blockchain) that employs a DAG-based consensus called Lachesis. Launched in 2019, Fantom gained popularity especially in the DeFi boom of 2021-2022 as an Ethereum-compatible chain with much higher performance. Fantom’s Opera network runs the Lachesis aBFT consensus (detailed above), where validators keep a local DAG of event blocks and achieve consensus asynchronously, then finalize transactions in a main chain. This gives Fantom a typical time-to-finality of ~1 second for transactions and the ability to handle thousands of transactions per second in throughput. Fantom is EVM-compatible, meaning developers can deploy Solidity smart contracts and use the same tooling as Ethereum, which greatly helped its adoption in DeFi. Indeed, Fantom became home to numerous DeFi projects (DEXes, lending protocols, yield farms) attracted by its speed and low fees. It also hosts NFT projects and gaming DApps – essentially any Web3 application that benefits from fast, cheap transactions. A noteworthy point is that Fantom achieved a high level of decentralization for a DAG platform: it has dozens of independent validators securing the network (permissionless, anyone can run a validator with the minimum stake), unlike some DAG networks that restrict validators. This positions Fantom as a credible alternative to more traditional blockchains for decentralized applications, leveraging DAG tech under the hood to break the performance bottleneck. The network’s FTM token is used for staking, governance and fees (which are only a few cents per transaction, much lower than Ethereum gas fees). Fantom demonstrated that DAG-based consensus can be integrated with smart contract platforms to achieve both speed and compatibility.
• Nano (XNO): Nano is a lightweight cryptocurrency launched in 2015 (originally as RaiBlocks) that uses a DAG block-lattice structure. Nano’s primary focus is peer-to-peer digital cash: instant, feeless transactions with minimal resource usage. In Nano, each account has its own chain of transactions, and transfers between accounts are handled via a send block on the sender’s chain and a receive block on the recipient’s chain. This asynchronous design means the network can process transactions independently and in parallel. Consensus is achieved by Open Representative Voting (ORV), where the community appoints representative nodes by delegation of balance weight. Representatives vote on conflicting transactions (which are rare, usually only in double-spend attempts), and once a quorum (67% weight) agrees, the transaction is cemented (irreversibly confirmed). Nano’s typical confirmation times are well below a second, making it feel instantaneous in everyday use. Because there are no mining rewards or fees, running a Nano node or representative is a voluntary effort, but the network’s design minimizes load (each transaction is only 200 bytes and can be processed quickly). Nano’s DAG approach and consensus allow it to be extremely energy-efficient – there is a tiny PoW performed by senders (mainly as an anti-spam measure), but it’s trivial compared to PoW blockchains. The use cases for Nano are simple by design: it’s meant for currency transfers, from everyday purchases to remittances, where speed and zero fees are the selling points. Nano does not support smart contracts or complex scripting; it focuses on doing one thing very well. A challenge for Nano’s model is that it relies on the honest majority of representatives; since there are no monetary incentives, the security model is based on the assumption that large token holders will act in the network’s best interest. So far, Nano has maintained a fairly decentralized set of principal representatives and has seen use in merchant payments, tipping, and other micropayment scenarios online.
• Hedera vs IOTA vs Fantom vs Nano (At a Glance): The table below summarizes some key characteristics of these DAG-based projects:

Project (Year)	Data Structure & Consensus	Performance (Throughput & Finality)	Notable Features / Use Cases
IOTA (2016)	DAG of transactions (“Tangle”); each tx approves 2 others. Originally coordinator-secured; moving to decentralized leaderless consensus (vote on heaviest DAG, no miners).	Theoretically high TPS (scales with activity); ~10s confirmation in active network (faster as load increases). Ongoing research to improve finality. Feeless transactions.	IoT micropayments and data integrity (feeless microtransactions), supply chain, sensor data, auto, decentralized identity (IOTA Identity DID method). No base-layer smart contracts (separate layers for that).
Hedera Hashgraph (2018)	DAG of events (Hashgraph); gossip-about-gossip + virtual voting consensus (ABFT), run by ~29–39 council nodes (PoS weighted). No miners; timestamps for ordering.	~10,000 TPS max; finality 3-5 seconds for transactions. Extremely low energy per tx (0.0001 kWh). Very low fixed fees ($0.0001 per transfer).	Enterprise and Web3 applications: tokenization (HTS), NFTs and content services, payments, supply chain tracking, healthcare data, gaming, etc. Council governance by big corporations; network is EVM-compatible for smart contracts (Solidity). Focus on high throughput and security for businesses.
Fantom (FTM) (2019)	DAG of validator event blocks; Lachesis aBFT PoS consensus (leaderless). Each validator builds DAG of events, which are confirmed and stitched into a final blockchain (Opera chain).	Empirically a few hundred TPS in DeFi usage; 1-2 second finality typical. Capable of thousands of TPS in benchmarks. Low fees (fractions of a cent).	DeFi and smart contracts on a high-speed L1. EVM-compatible (runs Solidity DApps). Supports DEXes, lending, NFT marketplaces (fast trading, cheap minting). DAG consensus hidden behind a developer-friendly blockchain interface. Staking available for anyone (decentralized validator set).
Nano (XNO) (2015)	DAG of account-chains (block-lattice); each tx is its own block. Open Representative Voting for consensus (dPoS-like voting on conflicts). No mining, no fees.	~Hundreds of TPS feasible (limited mainly by network I/O). <1s confirmation for typical transactions. No fees at all (feeless). Extremely low resource usage (efficient for IoT/mobile).	Digital currency for instant payments. Ideal for micropayments, tipping, retail transactions, where fees and latency must be minimal. Not designed for smart contracts – focuses on simple transfers. Very low power consumption (green cryptocurrency). Community-run representatives (no central authority).

(Table: Comparison of selected DAG-based ledger projects and their characteristics. TPS = transactions per second.)

Other DAG-based projects not detailed above include Obyte (Byteball) – a DAG ledger for conditional payments and data storage, IoT Chain (ITC) – an IoT-focused DAG project, Avalanche – which we discussed as using DAG in its consensus and has become a major DeFi platform, Conflux – a high-throughput PoW DAG in China, and academic prototypes like SPECTRE/PHANTOM. Each explores the design space of DAG ledgers in different ways, but the four examples above (IOTA, Hedera, Fantom, Nano) illustrate the diversity: from feeless IoT transactions to enterprise networks and DeFi smart contract chains, all leveraging DAG structures.

Use Cases of DAG Technology in the Web3 Ecosystem

DAG-based blockchain systems unlock certain use cases particularly well, thanks to their high performance and unique properties. Here are some current and potential use cases where DAGs are making an impact in Web3:

• Internet of Things (IoT): IoT involves millions of devices transmitting data and potentially transacting with each other (machine-to-machine payments). DAG ledgers like IOTA were explicitly designed for this scenario. With feeless microtransactions and the ability to handle high frequencies of small payments, a DAG ledger can enable IoT devices to pay for services or bandwidth on the fly. For example, a smart electric car might automatically pay a charging station a few cents worth of electricity, or sensors could sell data to a platform in real time. IOTA’s Tangle has been used in smart city pilots, supply chain IoT integrations (tracking goods and environmental conditions), and decentralized data marketplaces where sensor data is immutably logged and traded. The scalability of DAGs addresses the huge volume that widespread IoT networks generate, and their low cost suits micropayment economics.
• Decentralized Finance (DeFi): DeFi applications like decentralized exchanges (DEXs), lending platforms, and payment networks benefit from high throughput and low latency. DAG-based smart contract platforms (e.g. Fantom, and to an extent Avalanche’s X-Chain for simple asset transfers) offer an advantage in that trades can settle faster and fees remain low even during high demand. In 2021, Fantom saw a surge of DeFi activity (yield farming, automated market makers, etc.) and was able to handle it with much lower congestion than Ethereum at the time. Additionally, DAG networks’ quick finality reduces the risk of trade execution uncertainty (on slow chains, users wait many blocks for finality which can introduce risk in fast-paced trading). Another angle is decentralized payment networks – Nano, for example, can be viewed as part of the DeFi spectrum, enabling peer-to-peer transfers and potentially being a micropayment rail for layer-2 of other systems. DAG’s performance could also support high-frequency trading or complex multi-step DeFi transactions executing more smoothly.
• Non-Fungible Tokens (NFTs) and Gaming: The NFT boom has highlighted the need for low-cost minting and transfers. On Ethereum, minting NFTs became costly when gas fees spiked. DAG networks like Hedera and Fantom have been pitched as alternatives where minting an NFT costs a tiny fraction of a cent, making them viable for in-game assets, collectibles, or large-scale drops. Hedera’s Token Service allows native token and NFT issuance with the network’s low, predictable fees, and has been used by content platforms and even enterprises (e.g., music artists issuing tokens or universities tracking degrees). In gaming, where micro-transactions are common, a fast DAG ledger could handle frequent asset trades or reward distributions without slowing the game or bankrupting players in fees. The high throughput ensures that even if a popular game or NFT collection draws in millions of users, the network can handle the load (whereas we’ve seen games on Ethereum clog the network in the past). For instance, an NFT-based game on Fantom can update state quickly enough to provide near-real-time responsiveness.
• Decentralized Identity (DID) and Credentials: Identity systems benefit from an immutable ledger to anchor identities, credentials, and attestations. DAG networks are being explored for this because they offer scalability for potentially billions of identity transactions (every login, certificate issuance, etc.) and low cost, which is crucial if, say, each citizen’s ID interactions were recorded. IOTA Identity is one example: it provides a DID method did:iota where identity documents are referenced on the Tangle. This can be used for self-sovereign identity: users control their identity documents, and verifiers can retrieve proofs from the DAG. Hedera is also active in the DID space – it has a DID specification and has been used in projects like securing tamper-proof logs of college degrees, COVID vaccination certificates, or supply chain compliance documents (via the Hedera Consensus Service as an ID anchoring service). The advantages of DAGs here are that writing data is cheap and fast, so updating an identity state (like rotating keys, adding a credential) doesn’t face the cost or delay hurdles of a busy blockchain. Additionally, the finality and ordering guarantees can be important for audits (Hashgraph, for example, provides a trusted timestamp order of events which is useful in compliance logging).
• Supply Chain and Data Integrity: Beyond identity, any use case that involves logging a high volume of data entries can leverage DAG DLTs. Supply chain tracking is a notable one – products moving through a supply chain generate many events (manufactured, shipped, inspected, etc.). Projects have used Hedera and IOTA to log these events on a DAG ledger for immutability and transparency. The high throughput ensures the ledger won’t become a bottleneck even if every item in a large supply network is being scanned and recorded. Moreover, the low or zero fees mean you can record even low-value events on-chain without incurring major costs. Another example is IoT data integrity: energy grids or telecommunications might log device readings on a DAG ledger to later prove that data wasn’t tampered with. Constellation Network’s DAG (another DAG project) focuses on big data validation for enterprises and government (like US Air Force drone data integrity) – highlighting how a scalable DAG can handle big data streams in a trusted way.
• Payments and Remittances: Fast and feeless transactions make DAG cryptocurrencies like Nano and IOTA well-suited for payment use cases. Nano has seen adoption in scenarios like online tipping (where a user can send a few cents to a content creator instantly) and international remittances (where speed and zero fees make a big difference compared to waiting hours and paying percent-level fees). DAG networks can serve as high-speed payment rails for integrating with point-of-sale systems or mobile payment apps. For instance, a coffee shop could use a DAG-based crypto for payments and not worry about latency or cost (the user experience can rival contactless credit card payments). Hedera’s HBAR is also used in some payment trials (due to its fast finality and low fee, some fintech applications consider it for settlement). Additionally, because DAG networks often have higher capacity, they can maintain performance even during global shopping events or spikes in usage, which is valuable for payment reliability.
• Real-time Datafeeds and Oracles: Oracles (services that feed external data to blockchain smart contracts) require writing many data points to a ledger. A DAG ledger could act as a high-throughput oracle network, recording price feeds, weather data, IoT sensor readings, etc., with a guarantee of ordering and timestamp. The Hedera Consensus Service, for example, is used by some oracle providers to timestamp data before feeding it into other chains. The speed ensures that data is fresh, and the throughput means even rapid data streams can be handled. In decentralized Web3 analytics or advertising, where every click or impression might be logged for transparency, a DAG backend can cope with the event volume.

In all these use cases, the common thread is that DAG networks aim to provide the scalability, speed, and cost-efficiency that broaden the scope of what we can decentralize. They are particularly useful where high frequency or high volume transactions occur (IoT, microtransactions, machine data) or where user experience demands fast, seamless interactions (gaming, payments). That said, not every use case will migrate to DAG-based ledgers – sometimes the maturity and security of traditional blockchains, or simply network effects (e.g. Ethereum’s huge developer base), outweigh raw performance needs. Nonetheless, DAGs are carving out a niche in the Web3 stack for scenarios that strain conventional chains.

Limitations and Challenges of DAG-Based Networks

While DAG-based distributed ledgers offer enticing advantages, they also come with trade-offs and challenges. It’s important to critically examine these limitations:

• Maturity and Security: The majority of DAG consensus algorithms are relatively new and less battle-tested compared to Bitcoin or Ethereum’s well-studied blockchain protocols. This can mean unknown security vulnerabilities or attack vectors might exist. The complexity of DAG systems potentially opens new avenues for attacks – for example, an attacker might try to spam or bloat the DAG with conflicting subtangles, or take advantage of the parallel structure to double-spend before the network reaches consensus. Academic analyses note that increased complexity introduces a broader range of vulnerabilities compared to simpler linear chains. Some DAG networks have suffered issues: e.g., early on, IOTA’s network experienced a few instances where it had to be paused due to irregularities/hacks (one incident in 2020 involved stolen funds and the Coordinator was shut off temporarily to resolve it). These incidents underline that the security models are still being refined. Moreover, finality in some DAGs is probabilistic – e.g., pre-Coordicide IOTA had no absolute finality, only increasing confirmation confidence – which can be tricky for certain applications (though newer DAGs like Hashgraph and Fantom provide instant finality with aBFT guarantees).
• Consensus Complexity: Achieving consensus in a DAG often involves complicated algorithms (gossip protocols, virtual voting, random sampling, etc.). This complexity can translate to larger codebases and more complicated implementations, increasing the risk of software bugs. It also makes the system harder for developers to understand. A blockchain’s longest-chain rule is conceptually simple, whereas, say, Hashgraph’s virtual voting or Avalanche’s repeated random sampling are not immediately intuitive. The complexity can slow down adoption: developers and enterprises may be hesitant to trust a system they find harder to comprehend or audit. As one study pointed out, partial-order based systems (DAGs) require more effort to integrate with existing infrastructure and developer mindsets. Tools and libraries for DAG networks are also less mature in many cases, meaning the developer experience might be rougher than on Ethereum or Bitcoin.
• Decentralization Trade-offs: Some current DAG implementations sacrifice some degree of decentralization to achieve their performance. For instance, Hedera’s reliance on a fixed council of 39 nodes means the network is not open to anyone to participate in consensus, which has drawn criticism despite its technical strengths. IOTA, for a long time, relied on a central Coordinator to prevent attacks, which was a single point of failure/control. Nano’s consensus relies on a small number of principal representatives holding most voting weight (as of 2023, the top few reps often control a large portion of online voting weight), which could be seen as a concentration of power – though this is somewhat analogous to mining pools in PoW. In general, blockchains are currently perceived as easier to decentralize widely (thousands of nodes) than some DAG networks. The reasons are varied: some DAG algorithms might have higher node bandwidth requirements (making it harder for many nodes to participate fully), or the project’s design might intentionally keep a permissioned structure initially. This isn’t an inherent limitation of DAGs per se, but rather of specific implementations. It’s possible to have a highly decentralized DAG network, but in practice many haven’t reached the node counts of major blockchains yet.
• Need for Volume (Security vs Throughput): Some DAG networks paradoxically require high transaction volume to function optimally. For example, IOTA’s security model becomes robust when lots of honest transactions are constantly confirming each other (raising the cumulative weight of honest subtangles). If the network activity is very low, the DAG can suffer from laziness – tips not getting approved quickly, or an attacker finding it easier to try and override parts of the DAG. In contrast, a traditional blockchain like Bitcoin doesn’t require a minimum number of transactions to remain secure (even if few transactions occur, miners are still competing to extend the chain). Thus, DAGs often thrive under load but might stagnate under sparse usage, unless special measures are taken (like IOTA’s coordinator or background “maintenance” transactions). This means performance can be inconsistent – great when usage is high, but possibly slower confirmation in off-peak times or low-use scenarios.
• Ordering and Compatibility: Because DAGs produce a partial order of events that eventually needs to be consistent, the consensus algorithms can be quite intricate. In smart contract contexts, total ordering of transactions is required to avoid double-spending and to maintain deterministic execution. DAG systems like Fantom solve this by building an ordering layer (the final Opera Chain), but not all DAG systems support complex smart contracts easily. The state management and programming model can be challenging on a pure DAG. For example, if two transactions are non-conflicting, they can confirm in parallel on a DAG – that’s fine. But if they do conflict (say, two txns spending the same output or two trades on the same order), the network must decide one and drop the other. Ensuring that all nodes make the same decision in a decentralized way is harder without a single chain ordering everything. This is why many DAG projects initially avoided smart contracts or global state and focused on payments (where conflicts are simpler to detect via UTXOs or account balances). Interfacing DAG ledgers with existing blockchain ecosystems can also be non-trivial; for example, connecting an EVM to a DAG required Fantom to create a mechanism to linearize the DAG for the EVM execution. These complexities mean that not every use case can be immediately implemented on a DAG without careful design.
• Storage and Sync: A potential issue is that if a DAG ledger allows a high volume of parallel transactions, the ledger can grow quickly. Efficient algorithms for pruning the DAG (removing old transactions that are no longer needed for security) are important, as well as for letting light nodes operate (light clients need ways to confirm transactions without storing the entire DAG). Research has identified the reachability challenge: ensuring new transactions can reach and reference earlier ones efficiently, and figuring out how to truncate history safely in a DAG. While blockchains also face growth issues, the DAG’s structure might complicate things like calculating balances or proofs for partial state, since the ledger isn’t a simple list of blocks. This is largely a technical challenge that can be addressed, but it adds to the overhead of designing a robust DAG system.
• Perception and Network Effects: Outside of pure technical issues, DAG projects face the challenge of proving themselves in a blockchain-dominated space. Many developers and users are simply more comfortable with blockchain L1s, and network effects (more users, more dApps, more tooling on existing chains) can be hard to overcome. DAGs are sometimes marketed with bold claims (“blockchain killer”, etc.), which can invite skepticism. For example, a project might claim unlimited scalability – but users will wait to see it demonstrated under real conditions. Until DAG networks host “killer apps” or large user bases, they may be seen as experimental. Additionally, getting listed on exchanges, custody solutions, wallets – the whole infrastructure that already supports major blockchains – is an ongoing effort for each new DAG platform. So there’s a bootstrapping challenge: despite technical merits, adoption can lag due to ecosystem inertia.

In summary, DAG-based ledgers trade simplicity for performance, and that comes with growing pains. The complexity of consensus, potential centralization in some implementations, and the need to gain trust equivalent to older blockchain systems are hurdles to overcome. The research community is actively studying these issues – for instance, a 2024 systematization-of-knowledge paper on DAG protocols notes the increasing variety of designs and the need for holistic understanding of their trade-offs. As DAG projects mature, we can expect many of these challenges (like removal of coordinators, open participation, better dev tools) to be addressed, but they are important to consider when evaluating DAG vs blockchain for a given application.

Adoption Trends and Future Outlook

The adoption of DAG-based blockchain technology is still in its early stages relative to the widespread use of traditional blockchains. As of 2025, only a handful of public distributed ledgers use DAGs at scale – notable ones being Hedera Hashgraph, IOTA, Fantom, Nano, Avalanche (for part of its system), and a few others. Blockchains (linear chains) remain the dominant architecture in deployed systems. However, interest in DAGs has been steadily increasing in both industry and academia. We can identify a few trends and the outlook for DAG in blockchain:

• Growing Number of DAG Projects and Research: There is a visible uptick in the number of new projects exploring DAG or hybrid architectures. For example, recent platforms like Aleph Zero (a privacy-focused network) use a DAG consensus for fast ordering, and Sui and Aptos (Move-language chains) incorporate DAG-based mempool or parallel execution engines to scale performance. Academic research into DAG-based consensus is flourishing – protocols like SPECTRE, PHANTOM, GhostDAG, and newer ones are pushing the boundaries, and comprehensive analyses (SoK papers) are being published to classify and evaluate DAG approaches. This indicates a healthy exploration and the emergence of best practices. As research identifies solutions to earlier weaknesses (for instance, how to achieve fairness, how to prune DAGs, how to secure DAGs under dynamic conditions), we’ll likely see these innovations trickle into implementations.
• Hybrid Models in Mainstream Use: An interesting trend is that even traditional blockchains are adopting DAG concepts to improve performance. Avalanche is a prime example of a hybrid: it presents itself as a blockchain platform, but at its core uses a DAG consensus. It has gained significant adoption in DeFi and NFT circles, showing that users sometimes adopt a DAG-based system without even realizing it, as long as it meets their needs (fast and cheap). This trend may continue: DAG as an internal engine while exposing a familiar blockchain interface could be a winning strategy, easing developers in. Fantom did this with its Opera chain, and other projects might follow suit, effectively making DAG tech an unseen backbone for next-gen chains.
• Enterprise and Niche Adoption: Enterprises that require high throughput, predictable costs, and are comfortable with more permissioned networks have been inclined to explore DAG ledgers. Hedera’s Governing Council model attracted big companies; they in turn drive use cases like asset tokenization for financial services, or tracking software licenses, etc., on Hedera. We’re seeing consortia consider DAG-based DLT for things like telecommunications settlements, advertising impression tracking, or interbank transfers, where the volume is high and they need finality. IOTA has been involved in European Union funded projects for infrastructure, digital identity pilots, and industrial IoT – these are more long-term adoption paths, but they show that DAGs are on the radar beyond just the crypto community. If some of these trials prove successful and scalable, we could see sector-specific adoption of DAG networks (e.g., an IoT consortium all using a DAG ledger to share and monetize data).
• Community and Decentralization Progress: Early criticisms of DAG networks (central coordinators, permissioned validators) are gradually being addressed. IOTA’s Coordicide will, if successful, remove the central coordinator and transition IOTA to a fully decentralized network with a form of staking and community-run validators. Hedera has open-sourced its code and hinted at plans to further decentralize governance in the long run (beyond the initial council). Nano’s community continuously works on decentralizing representative distribution (encouraging more users to run reps or split their delegations). These moves are important for the credibility and trust in DAG networks, aligning them more with the ethos of blockchain. As decentralization increases, it’s likely that more crypto-native users and developers will be willing to build on or contribute to DAG projects, which can accelerate growth.
• Interoperability and Layer-2 Use: We might also see DAGs being used as scaling layers or interoperable networks rather than standalone ecosystems. For example, a DAG ledger could serve as a high-speed layer-2 for Ethereum, periodically anchoring batched results to Ethereum for security. Alternatively, DAG networks could be linked via bridges to existing blockchains, allowing assets to flow where it’s cheapest to transact. If the UX can be made seamless, users might transact on a DAG network (enjoying high speed) while still relying on a base blockchain for settlement or security – getting the best of both worlds. Some projects consider this kind of layered approach.
• Future Outlook – Complement, not Replacement (for now): It’s telling that even proponents often say DAG is an “alternative” or complement to blockchain rather than an outright replacement. In the near future, we can expect heterogeneous networks: some will be blockchain-based, some DAG-based, each optimized for different scenarios. DAGs might power the high-frequency backbone of Web3 (handling the grunt work of microtransactions and data logging), while blockchains might remain preferred for settlement, extremely high-value transactions, or where simplicity and robustness are paramount. Over a longer horizon, if DAG-based systems continue to prove themselves and if they can demonstrate equal or greater security and decentralization, it’s conceivable they could become the dominant paradigm for distributed ledgers. The energy efficiency angle also aligns DAGs well with global sustainability pressures, potentially making them more politically and socially acceptable in the long run. The carbon footprint benefits of DAG networks, combined with their performance advantages, could be a major driver if regulatory environments emphasize green technology.
• Community Sentiment: There is a segment of the crypto community that is very excited about DAGs – seeing them as the next evolutionary step of DLT. You’ll often hear phrases like “DAGs are the future; blockchains will eventually be seen as the dial-up internet compared to DAG’s broadband.” This enthusiasm has to be balanced with practical results, but it suggests that talent and investments are flowing into this area. On the other hand, skeptics remain, pointing out that decentralization and security shouldn’t be compromised for speed – so DAG projects will have to demonstrate that they can have the best of both worlds.

In conclusion, the future outlook for DAG in blockchain is cautiously optimistic. Right now, blockchains still dominate, but DAG-based platforms are carving out their space and proving their capabilities in specific domains. As research resolves current challenges, we’ll likely see more convergence of ideas – with blockchains adopting DAG-inspired improvements and DAG networks adopting the lessons of blockchains on governance and security. Web3 researchers and developers would do well to keep an eye on DAG advancements, as they represent a significant branch of the DLT evolution tree. The coming years may see a diverse ecosystem of interoperable ledgers where DAGs play a vital role in scaling and special-purpose applications, moving us closer to the vision of a scalable, decentralized web.

In the words of one Hedera publication: DAG-based ledgers are “a promising step forward” in the evolution of digital currencies and decentralized tech – not a silver bullet to replace blockchains outright, but an important innovation that will work alongside and inspire improvements in the distributed ledger landscape as a whole.

Sources: The information in this report is drawn from a variety of credible sources, including academic research on DAG-based consensus, official documentation and whitepapers from projects like IOTA, Hedera Hashgraph, Fantom, and Nano, as well as technical blogs and articles that provide insights into DAG vs blockchain differences. These references support the comparative analysis, benefits, and case studies discussed above. The continued dialogue in the Web3 research community suggests that DAGs will remain a hot topic as we seek to solve the trilemma of scalability, security, and decentralization in the next generation of blockchain technology.