Layer-1 vs Layer-2 solutions: 11 ways they improve blockchain performance

If you’re comparing Layer-1 vs Layer-2 solutions, you’re really asking how to get more throughput, lower fees, and faster finality without breaking security or decentralization. Layer 1 (L1) is the base blockchain—think the main highway that handles consensus and data availability. Layer 2 (L2) builds on top—like express lanes that batch or offload work while still anchoring to L1 security. In one sentence: L1 changes improve the base protocol, while L2 changes reduce on-chain load by moving or compressing activity, then settle back to L1. The outcome you care about is practical performance: more transactions per second (TPS), lower gas, and a smoother user experience.

At a glance, here’s a quick, skimmable way to choose what to explore first:

• Clarify your goal: lower fees, higher throughput, faster confirmations, or all three.
• Map your trust assumptions: native L1 security vs. inheriting L1 security on L2 vs. separate security on a sidechain.
• Decide on execution: stick with EVM compatibility or consider alternate runtimes (Wasm, ZK-friendly VMs).
• Align with your app’s needs: payments care about latency; DeFi cares about liquidity and composability; games need high volume and low cost.
• Shortlist candidates: sharded L1 improvements, optimistic or ZK rollups, state channels, sidechains, app-specific L2s.
• Pilot, measure, and iterate with real traffic—not just synthetic benchmarks.

Below is a compact reference that frames the comparison you’re about to read:

The sections that follow unpack 11 ways L1 and L2 improvements raise real-world performance, complete with steps, guardrails, and concrete examples you can apply.

1. Scale throughput with sharding and rollups

Sharding at L1 and rollups at L2 both increase effective TPS, but they do it differently. Sharding divides the base chain’s data and validation workload across many partitions, letting the protocol process more transactions in parallel while preserving a shared security umbrella. Rollups, by contrast, execute transactions off-chain (or off the base layer), then post succinct summaries—state roots and proofs or batched data—back to L1. In practice, sharded L1s expand the highway itself, while rollups build express lanes that keep most of the traffic off the base layer except for final settlement and data. The net effect is the same for users—more capacity and less congestion—but the trade-offs differ: protocol complexity and validator requirements for sharding, versus operator assumptions and bridging considerations for rollups.

How it works

• L1 sharding: Splits data and validation; cross-shard communication channels coordinate global state.
• Optimistic rollups: Assume correctness; disputes use fraud proofs to rewind and fix.
• ZK rollups: Post succinct validity proofs that attest correct execution, cutting confirmation time on L1.
• Hybrid approaches: Data availability improvements on L1 make L2 batches cheaper and more frequent.

Numbers & guardrails

• Expect order-of-magnitude increases: a base chain handling tens of TPS can enable thousands+ when paired with efficient rollups.
• Rollups compress transactions into data blobs, commonly reducing per-transaction gas by multiples compared to direct L1 execution.
• Guardrail: capacity gains mean little if data availability is a bottleneck—measure batch sizes and L1 data costs.

Tools/Examples

• Optimistic rollups: Arbitrum, Optimism.
• ZK rollups: zkSync, Starknet.
• Sharding roadmaps: Data-availability first, execution later, with rollup-centric designs.

In short, use sharding to widen the base highway and rollups to move cars off it; together they multiply total capacity.

2. Cut transaction fees via batching, compression, and cheaper data

Fee reduction is the most visible win users notice after moving activity from L1 to L2. L2s reduce gas by batching hundreds or thousands of transactions and posting compressed data to L1, amortizing costs across many users. On L1, protocol upgrades that make data posting cheaper also directly reduce L2 fees, since data is the dominant cost for rollups. Effective compression schemes—like calldata minimization and specialized encodings for signatures or state updates—shrink payloads further. For users, this translates into paying a fraction of the L1 price while retaining strong security guarantees when the L2 inherits L1 security.

How it works

• Batching: Aggregates many transactions into one L1 submission.
• Compression: Uses compact encodings and signature aggregation to shrink bytes per transaction.
• Cheaper data lanes: Protocol changes that lower the cost of data availability make each batch more affordable.

Numbers & guardrails

• Typical L2 fees can drop to cents for simple transfers and stay well below L1 even during busy periods.
• Compression ratios vary by workload; signing schemes and calldata format can save 30–90% bytes.
• Guardrail: watch for fee spikes when L1 demand surges; sustained reductions depend on predictable data pricing.

Tools/Examples

• Batch posters/relayers on rollups.
• Data-availability layers that separate consensus from data storage.
• Signature aggregation and EVM precompiles that reduce per-tx overhead.

Bottom line: cheaper bytes and shared submission costs are the workhorses behind “fees feel near-zero” experiences.

3. Improve latency and finality with faster paths to confidence

Performance isn’t only about TPS; it’s also how quickly transactions feel “done.” L2s often provide near-instant local confirmations from a sequencer, letting apps proceed while settlement to L1 happens in the background. ZK rollups can offer stronger finality sooner because validity proofs attest correctness, while optimistic rollups rely on challenge windows that delay economic finality to L1 but can still give users responsive UX. L1s can reduce perceived latency by optimizing block times, proposer/committees, and fork choice, but they’re constrained by network propagation and safety thresholds. The pragmatic strategy is to pair fast L2 confirmations with clearly communicated settlement guarantees.

How it works

• Local confirmation: Sequencer or operator returns a receipt quickly; the batch later lands on L1.
• Economic vs. social finality: Users need to know when a transaction is safe to act on, not only when it’s on L1.
• Proof-driven finality: Validity proofs shorten the path to strong assurance vs. fraud-proof windows.

Numbers & guardrails

• Local L2 confirmations are typically sub-second to a few seconds; L1 settlement is slower but predictable.
• Challenge windows on optimistic systems can be minutes to days; design UX to reflect this.
• Guardrail: avoid irreversible actions (e.g., releasing collateral) until the appropriate finality tier is reached.

Tools/Examples

• ZK rollups for fast provable finality.
• Optimistic rollups with clear challenge-period messaging.
• Payment channels (for tiny, instant payments) that settle netted results on L1.

The net effect is a system that “feels instant” to users while preserving the safety of L1 settlement.

4. Offload execution while preserving security with fraud and validity proofs

The central idea of L2 security is to avoid trusting off-chain execution blindly. Optimistic rollups secure correctness with fraud proofs: anyone can challenge an incorrect state update and force a replay on-chain to resolve disputes. ZK rollups use validity proofs to proactively demonstrate that state transitions are correct before L1 accepts them. Both models keep the security of the base chain front and center: if the operator misbehaves, the L1 truth machine can rectify it. For developers, the distinction shapes costs, latency, and complexity—and determines how you reason about failure modes and user safety.

How it works

• Fraud proofs: Assume correct by default; allow challenges to catch errors.
• Validity proofs: Provide cryptographic guarantees up front; L1 verifies a succinct proof.
• Escape hatches: Users can exit to L1 even during operator outages when designs include forced withdrawals.

Numbers & guardrails

• Validity proofs add proving costs but can yield short settlement times; fraud proofs add a challenge window but cheaper proving.
• Guardrail: ensure watchtower or monitoring exists so someone can submit challenges when needed.
• Guardrail: track proof generation time for ZK systems; throughput gains rely on stable proving pipelines.

Tools/Examples

• Fraud-proof stacks: popular among optimistic ecosystems.
• ZK toolchains: proving systems (STARKs, SNARKs) and ZK-friendly VMs.

This is how L2s scale without hand-waving security: off-chain work with on-chain correctness guarantees.

5. Parallelize execution and tailor runtimes for higher throughput

Performance jumps when you can process transactions in parallel or run them on engines optimized for your workload. L1 improvements include parallel execution (processing independent transactions concurrently) and more efficient virtual machines. L2s can go further by customizing runtimes: app-specific rollups can use execution environments tailored to games, exchanges, or payments. Parallel mempool designs and conflict analysis reduce contention, while smarter scheduling ensures hot contracts don’t stall everything else. The result is better hardware utilization and a smoother path to consistent throughput even during bursts.

How it works

• Parallel EVM or alternative runtimes: Execute non-conflicting transactions simultaneously.
• Static & dynamic analysis: Detect conflicts; schedule safely to avoid reverts.
• App-specific L2s: Optimize opcodes, fee markets, and storage for the app’s access patterns.

Numbers & guardrails

• Parallelism yields multiplicative gains on multi-core hardware when conflicts are low; conflict-heavy DeFi can see diminished returns.
• Guardrail: measure contention rates; parallel engines shine when independent transfers dominate.
• Guardrail: ensure determinism so nodes reach the same state despite concurrency.

Tools/Examples

• Parallelized EVM initiatives and Wasm-based execution.
• App-specific rollups that tune gas metering and storage schemes for the domain.

Parallelism doesn’t create capacity from thin air; it unlocks the performance your hardware already had.

6. Use data-availability layers and sampling to unlock cheaper, bigger blocks

Data availability (DA) is the oxygen of scalable blockchains. Even if execution is off-chain, verifiers must be able to download batch data to detect fraud or reconstruct state. L1 upgrades that provide cheaper, purpose-built data lanes make L2 batches larger and more frequent at lower cost. Dedicated DA layers and data-availability sampling decouple consensus from blob storage, letting light clients gain confidence that data is posted without every node downloading it all. This modular split keeps base consensus lean while giving L2s practically unlimited room to grow batch sizes.

How it works

• Blob-like data lanes: Special transaction types make posting large data cheaper and more predictable.
• Sampling: Nodes sample small portions of data to probabilistically confirm availability.
• Modular stacks: L2s settle on L1 but store data on specialized DA layers that plug into the ecosystem.

Numbers & guardrails

• Expect significant fee reductions per byte posted when using dedicated DA paths vs. generic calldata.
• Sampling security rises with samples per node and number of honest samplers—treat these as tunable parameters.
• Guardrail: verify reconstruction paths; your security depends on being able to retrieve batch data later.

Tools/Examples

• Modular DA networks and L1 blob features.
• Rollups configured to publish full data vs. validity proofs only, depending on threat model.

With cheaper DA, L2s scale not just in theory but in every batch they post.

7. Isolate congestion with app-specific L2s and sidechains

General-purpose chains share blockspace across DeFi, NFTs, payments, and games. When a hot mint or liquidation storm hits, everyone competes. App-specific L2s and sidechains isolate traffic by dedicating blockspace to a single application or a small suite. This removes cross-app contention and gives operators latitude to tune gas markets, block sizes, and mempool policies for their workload. The trade-off is fragmentation: you gain predictable performance at the cost of composability and sometimes security, depending on whether the environment inherits L1 security (a rollup) or uses its own validators (a sidechain).

How it works

• App rollups: Purpose-built L2s where the app controls parameters while settling to L1.
• Sidechains: Separate consensus; connect via bridges; often faster but with different trust assumptions.
• Gas market tuning: Prioritize app-relevant operations; throttle spam; widen blocks during events.

Numbers & guardrails

• Dedicated environments can keep fees stable even during global spikes.
• Guardrail: assess bridge risk—sidechains typically carry more trust assumptions than rollups.
• Guardrail: plan liquidity routes; isolated apps need reliable bridging or shared sequencers to avoid user friction.

Tools/Examples

• Gaming rollups with high tick rates.
• Payment-focused sidechains with fast block times.
• Cross-domain communication frameworks to reintroduce composability.

Isolation is a scalpel: use it to cut out congestion for the users who need guaranteed performance.

8. Enhance UX with account abstraction, batching, and intent-based flows

Raw performance matters less if users fumble with keys or pay unpredictable gas. Account abstraction (AA) lets smart accounts sponsor fees, batch operations, and enforce custom recovery rules. Intents—a declarative way for users to state what they want, not how to do it—enable routing systems to find the best path across L1/L2s. L2s often lead on AA because they can move faster and subsidize fees with lower costs. Combine AA with batched interactions (e.g., approve + swap + bridge in one go) and you’ll see user-perceived performance leap, even if raw TPS is unchanged.

How it works

• Smart accounts: Program rules for spending, recovery, session keys, and sponsored gas.
• Bundlers/relayers: Aggregate many operations, posting a single transaction to L1 or L2.
• Intent routers: Search across chains and L2s to fulfill a high-level action at the best price and speed.

Numbers & guardrails

• Bundling can cut user-visible steps from 3–5 clicks to 1, and consolidate gas into a single fee.
• Guardrail: audit paymasters and bundlers; they’re powerful components that touch user funds.
• Guardrail: design fallbacks when routers or relayers are down so users aren’t stranded.

Tools/Examples

• AA toolkits compatible with popular rollups.
• Transaction bundlers and gas sponsors for smoother onboarding.
• Cross-domain routers that discover efficient paths.

A faster app is often a simpler app; AA and batching remove user friction that blocks perceived speed.

9. Strengthen security economics and decentralize sequencing

As throughput rises, so do the stakes of operator behavior. L2s commonly use a sequencer to order transactions quickly; decentralizing or adding fallback paths reduces downtime risk and censorship potential. Economic alignment mechanisms—like staking, shared sequencers, and slashing—help scale safely. On L1, proposer-builder separation (PBS) and similar designs address Miner/Maximal Extractable Value (MEV) while keeping block production healthy. The objective is not to eliminate trust entirely but to minimize and make it accountable, so users can rely on performance even under stress.

How it works

• Sequencer decentralization: Rotate leaders; provide permissionless backup routes; publish batches trustlessly.
• Shared sequencers: Coordinate ordering across many rollups, improving cross-domain liveness.
• Staking & slashing: Put operator skin in the game to deter misbehavior.

Numbers & guardrails

• Target high uptime (e.g., four nines) through redundancy and automatic failover.
• Guardrail: publish state roots on a cadence that balances latency vs. L1 cost.
• Guardrail: log and prove inclusion guarantees so users can show if they were censored.

Tools/Examples

• Shared sequencing projects connecting multiple L2s.
• PBS-like designs on base layers to limit harmful MEV while keeping throughput high.

Robust economics plus redundancy turn raw capacity into dependable performance.

10. Improve interoperability, bridging, and shared liquidity

Performance gains lose value if assets and messages take ages to move. Efficient bridges and cross-domain messaging are crucial so users can enter, exit, and compose across L1 and L2s without jams. Native bridges that inherit L1 security via proofs are the baseline for safety; third-party bridges can provide speed but may add trust assumptions. Liquidity networks, market makers, and intent-based routers reduce wait times and slippage. The goal is straightforward: fast, safe movement between domains so your users don’t feel trapped.

How it works

• Native L2 bridges: Use fraud/validity proofs to finalize moves to/from L1.
• Liquidity bridges/routers: Front-run settlement with bonded liquidity for speed.
• Cross-domain standards: Canonical message formats reduce bespoke adapters and errors.

Numbers & guardrails

• Native exits on optimistic stacks can take longer due to challenge windows; liquidity bridges often complete near instantly at a fee premium.
• Guardrail: cap per-bridge limits and use rate limiters to reduce blast radius of incidents.
• Guardrail: mandate proof verification on destination chains for high-value flows.

Tools/Examples

• Canonical L2 bridges tied to rollup contracts.
• Liquidity networks for fast exits.
• Standardized message buses to simplify app integrations.

Good bridges make multi-domain scaling feel like one cohesive network rather than a patchwork of silos.

11. Reduce MEV impact and improve fairness with ordering and auctions

Even high-TPS systems can feel slow or expensive when bidding wars erupt in the mempool. MEV (Maximal Extractable Value) arises when ordering power can capture value from pending transactions. L1 approaches like PBS, inclusion lists, and fair ordering give more predictable outcomes; L2s can integrate similar mechanisms at the sequencer layer. Auctioning the right to build blocks, smoothing fees, and shielding mempools (e.g., private orderflow) reduce harmful MEV while preserving healthy market-making. The payoff is less congestion from sandwiching or spam and more consistent user costs.

How it works

• Proposer-builder separation: Builders compete to assemble blocks; proposers pick the best while enforcing rules.
• Fair ordering windows: Limit latency games; batch and randomize order within time slices.
• Private or encrypted mempool: Hide details until inclusion to deter predatory strategies.

Numbers & guardrails

• Fee smoothing can reduce peak gas by meaningful percentages during volatile events.
• Guardrail: ensure liveness—overly strict ordering can stall blocks.
• Guardrail: publish transparent auction rules so participants can adapt predictably.

Tools/Examples

• PBS variants on L1 and mirrored designs on L2.
• Orderflow protection services integrated into wallets and relayers.

Calmer mempools translate directly into steadier performance users can rely on during surges.

Conclusion

The cleanest way to think about Layer-1 vs Layer-2 solutions is divide-and-conquer: make the base chain great at consensus and data, and move most execution where it’s cheap and flexible—then settle back to L1 for security. Sharding and rollups multiply throughput; batching and dedicated data lanes cut fees; fast local confirmations and validity proofs reduce perceived latency; and better sequencing, bridges, and MEV policies keep the system fair and dependable. If you’re choosing a path today, start with your app’s constraints—latency sensitivity, liquidity needs, security assumptions—and pick the mix that hits those targets with measurable guardrails. Pilot on a small user slice, gather concrete metrics (TPS achieved, average fee, time-to-finality, failed transaction rate), and iterate toward a stack that keeps users in flow. When in doubt, keep settlement safety on L1, push execution to L2, and let modular components do what they do best.

CTA: Ready to map your stack? Shortlist two candidate L2s and one DA option, run a week-long pilot with real users, and compare fee, latency, and failure metrics side by side.

FAQs

What’s the simplest definition of a Layer 2?
A Layer 2 is an auxiliary network that executes transactions off the base chain to improve performance, then posts compressed data or proofs back to Layer 1. You get lower fees and higher throughput while anchoring security to the base chain. The exact guarantees depend on whether the L2 uses fraud proofs (optimistic) or validity proofs (ZK).

Do Layer-2 fees always stay low during spikes?
They’re far more stable than L1 fees because batches amortize costs across many users, but they can still rise if L1 data prices surge or if the L2 is congested. Solid L2s mitigate this with larger batches, better compression, and data lanes that price bytes more predictably. Monitoring fee volatility is essential for apps with tight cost budgets.

Is a sidechain the same as a rollup?
No. A rollup inherits security from L1 and uses proofs to keep operators honest, whereas a sidechain runs its own consensus and relies on its own validator set. Sidechains can be very fast and cheap but introduce additional trust assumptions, especially around bridges. Choose sidechains when you accept that trade-off for specific workloads.

How fast is “final” on an L2?
Users often get local confirmations within seconds, which is usually enough for routine actions. Economic finality on L1 depends on the proof model: ZK systems can confirm quickly once a proof verifies; optimistic systems have a challenge period before final settlement. Design your app to reflect these tiers—quick UX for the user, stronger guarantees before irreversible actions.

Can I keep EVM compatibility and still scale?
Yes. Many L2s run EVM-compatible execution to preserve tooling and contracts, while still gaining scale through batching and compression. If your workload benefits from specialized opcodes or parallelism, app-specific rollups or alternative runtimes can help—just weigh the migration and tooling costs against the performance gain.

What’s data availability and why should I care?
Data availability ensures that transaction data for a block or batch is actually published so anyone can reconstruct state and verify correctness. Without it, fraud proofs and light verification break down. Cheaper, dedicated data lanes and sampling techniques make posting large batches affordable, which is the backbone of L2 fee reductions.

How do shared sequencers help?
Shared sequencers coordinate transaction ordering across multiple rollups. This improves liveness (fewer outages), reduces cross-domain latency, and can restore some composability between apps that sit on different L2s. They also create clearer economic incentives and accountability for ordering decisions.

What are the biggest risks when bridging?
Bridges concentrate risk. Native, proof-based bridges that inherit L1 security are safest for high-value transfers but may take longer; liquidity bridges are faster but add trust assumptions and potential inventory risk. Implement rate limits, proof verification, and clear limits per transfer to reduce the blast radius of incidents.

Does MEV go away on L2s?
No, but its impact can be managed. Sequencer policies, private orderflow, and fair ordering reduce predatory strategies and gas spikes. On L1, proposer-builder separation helps contain harmful MEV; similar ideas on L2s keep the user experience steady, especially during volatile market events.

When should I choose an app-specific L2?
Pick an app-specific L2 when you need predictable performance for a single app or narrow domain, and when cross-app composability matters less than guaranteed throughput and stable fees. It’s ideal for games and high-frequency payments; plan good bridges and liquidity routes to keep the user journey smooth.

References

• Layer 2 scaling, Ethereum.org, https://ethereum.org/en/developers/docs/scaling/layer-2-scaling/
• Optimistic rollups, Ethereum.org, https://ethereum.org/en/developers/docs/scaling/optimistic-rollups/
• ZK rollups, Ethereum.org, https://ethereum.org/en/developers/docs/scaling/zk-rollups/
• Data availability and danksharding overview, Ethereum.org, https://ethereum.org/en/roadmap/danksharding/
• EIP-4844 specification (blob-carrying transactions), Ethereum, https://eips.ethereum.org/EIPS/eip-4844
• The Lightning Network overview, Lightning Engineering Docs, https://docs.lightning.engineering/the-lightning-network/ln-overview
• Starknet documentation, Starknet, https://docs.starknet.io/documentation/
• Arbitrum documentation, Offchain Labs, https://docs.arbitrum.io/
• Optimism documentation, Optimism, https://docs.optimism.io/
• zkSync Era documentation, zkSync, https://docs.zksync.io/
• Celestia data availability, Celestia, https://celestia.org/what-is-data-availability/
• MEV overview, Ethereum.org, https://ethereum.org/en/developers/docs/mev/