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There is no evidence-based universal winner between Sui and Solana for high-throughput applications. The better choice depends on how your transactions access state, how much data they create and delete, whether they need atomic instruction composition, and how much operational work you can invest in transaction delivery and fee management.
Sui’s object model can parallelize transactions that operate on independent objects, while Solana’s account-and-instruction model lets programs compose several state changes atomically. Those are architectural differences, not a matched benchmark. A defensible decision requires running the same representative workload on both networks and measuring successful throughput, tail latency, failed-transaction cost, retries, RPC behavior, and total operating cost.
At a glance: which design fits your application?
| Application characteristic | Design question | What to investigate |
|---|---|---|
| Many independent user or asset operations | Can each transaction touch a separate object without a shared hotspot? | Sui’s object ownership and parallel execution path may fit, but measure contention created by shared objects. |
| Several state changes must succeed or fail together | Can the operation be expressed as one atomic transaction? | Solana transactions compose multiple instructions and revert their state changes together if an instruction fails. Validate transaction-size and account-access limits against your transaction shape. |
| Frequent creation, mutation, and deletion of on-chain data | How does storage live over the object or account lifecycle? | Model Sui computation and storage fees, including possible rebates when stored objects are deleted. |
| Highly variable compute demand or congested submission periods | How will you estimate fees and get transactions included? | On Solana, budget compute units carefully, estimate priority fees, and operate reliable submission and retry infrastructure. |
| Consumer product with sponsored transactions | Who pays, signs, retries, and monitors the transaction? | Compare Solana’s designated fee-payer model with your security and user-experience requirements; design the equivalent operational controls on Sui. |
This table is a starting hypothesis, not a performance verdict. The reviewed official documentation does not provide a current, independently measured, apples-to-apples Sui-versus-Solana benchmark.
How the two execution models differ
Sui: objects, ownership, and independent work
Sui is a Layer 1 network with delegated proof-of-stake, its own consensus and validation, and an object-based state model. Objects carry type, ownership, and reference information. The architecture documentation describes independent-object transactions as capable of parallel processing.
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For an application team, the practical question is not simply “How many transactions per second can Sui process?” It is “How often do our hot paths touch the same shared object?” A transfer involving an independently owned asset may have a different execution path from an operation that updates a shared order book, shared counter, or global configuration object. Shared-state access can require coordinated handling and may reduce the benefit of otherwise independent work.
Design your data model so that unrelated operations remain unrelated where the business rules allow it. Do not treat the architectural ability to process independent transactions in parallel as a guarantee for your particular workload.
Solana: accounts passed to programs
Solana represents mutable state in accounts. Programs contain code, while the accounts holding the state are passed into each instruction. A transaction can contain multiple instructions, and the instructions execute atomically: if the transaction fails, its state changes are reverted.
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This model makes the transaction’s account list and instruction composition central capacity constraints. A single business operation may combine several program calls in one transaction, but the transaction must still fit Solana’s implementation limits for size and account access. Those limits should be tested with your real instruction data, account metadata, signatures, and address-lookup strategy rather than with a simplified transfer.
Why these are not directly comparable throughput numbers
Sui’s object-parallelism description and Solana’s account-and-instruction design explain how work can be organized; neither is a matched production benchmark. Throughput and latency vary with transaction shape, state contention, geographic distribution, validator conditions, submission strategy, and failure rate. The same application may expose different bottlenecks after its data model is adapted to each chain.
Map your workload before selecting a chain
Write down the operations your application actually performs. A high-throughput claim based on one simple transaction is not useful if the production path performs six account reads, creates a token account, updates a shared market, and retries during congestion.
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1. Classify transaction types
- Read-only requests: off-chain queries and indexer reads do not consume on-chain execution in the same way as state-changing transactions, but they still require dependable RPC capacity.
- Independent writes: transfers or asset updates that touch distinct user or object state.
- Shared-state writes: updates to a common pool, counter, order book, registry, or configuration object.
- Composed operations: workflows that must update several pieces of state atomically.
- Failure and retry paths: submissions that time out, expire, collide with a newer state version, or are rejected for insufficient fees or compute.
2. Build a state-access map
For every transaction class, record the state it reads and writes, whether that state is independently owned or shared, and whether another transaction can modify it concurrently. On Sui, mark object ownership and shared-object access. On Solana, list every account passed to each instruction and identify accounts that become hot under peak load.
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3. Quantify contention
Measure the percentage of transactions that touch the busiest shared object or account. A workload with a low average write set can still bottleneck if nearly every request increments one global counter. Record contention by percentile and by geographic or tenant segment; a single large customer can create a hotspot that an average hides.
4. Define atomicity requirements
Separate operations that may settle independently from those that must succeed or fail as one unit. Solana’s instruction composition provides an explicit atomic transaction boundary. On Sui, determine how your object ownership and transaction design express the same business invariant, especially when shared objects are involved.
Performance: what is established and what must be measured
The available official material does not establish a current, independent winner for throughput, confirmation latency, or total application cost. Do not transplant a number from one network’s documentation onto the other, and do not present theoretical capacity as a production result.
Published Solana figures are vendor claims
Solana’s Payments documentation states “~400 ms settlement” and “Median fee ~$0.001 per transaction.” These are Solana-published figures accessed in 2026, not independently matched measurements against Sui. Actual results depend on transaction contents, congestion, fee conditions, geographic placement, and the delivery path used by your application.
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Use a matched workload test
- Freeze the business operation. Define identical success criteria, such as a settled transfer plus an application record update.
- Implement equivalent data models. Do not benchmark a minimal object write on one chain against a multi-account, multi-instruction transaction on the other.
- Generate realistic mixes. Include independent writes, shared-state writes, composed transactions, invalid requests, and retries in production-like proportions.
- Run at increasing offered load. Record the point at which successful throughput stops scaling, queue time rises, or failure and retry rates become unacceptable.
- Measure tail latency. Capture p50, p95, and p99 submission-to-confirmation times, not only an average or a fastest observed confirmation.
- Measure end-to-end cost. Include successful and failed transaction fees, storage lifecycle, fee estimation, retries, RPC usage, and any infrastructure required to keep transactions landing.
- Repeat across regions and congestion conditions. A test from one data center during a quiet period cannot represent a globally distributed product.
Metrics that matter in production
- Accepted, confirmed, finalized, failed, expired, and dropped submissions.
- Retries per successful business operation.
- Time spent waiting for an RPC response versus time waiting for chain confirmation.
- State-conflict, stale-reference, insufficient-compute, and insufficient-fee error rates.
- p95 and p99 latency during peak load.
- Cost per successful business operation, not merely cost per submitted transaction.
- Recovery time when an endpoint, region, indexer, or fee-estimation service is unavailable.
How Sui fees work
Sui separates computation and storage in its gas mechanism. Sui documentation describes the model this way: “A unique feature of the Sui gas price mechanism is that users pay separate fees for transaction execution and for storing the data associated with each transaction.”
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Computation, storage, and rebates
Your Sui cost model must include the work performed and the data created or changed. When a transaction deletes previously stored objects, a storage rebate may reduce the net fee. A workload that creates temporary objects and later deletes them can therefore have a different long-run cost from a workload that permanently accumulates data, even if their execution paths are similar.
Reference gas price and epochs
Validators establish the reference gas price at epoch boundaries using validator survey signals. The price is therefore an epoch-level network parameter, not a permanently fixed application constant. Keep the current reference price in configuration or estimation logic and test behavior when it changes.
Gas budgets and failure accounting
Transactions require a gas budget. Your estimator should account for computation, storage changes, object creation, mutation, deletion, and the selected budget. A budget that is too low can cause a transaction to fail; a budget that is needlessly high can complicate cost forecasting even when the final charge reflects actual work. Record both the requested budget and the final charge in telemetry.
How Solana fees work
Solana’s official fee documentation specifies a base fee of 5,000 lamports per signature, plus an optional priority fee. The base component is tied to signatures; adding signatures changes the fee even when the program logic is unchanged.
Priority fee and requested compute limit
For legacy and versioned (v0) transactions, Solana calculates the priority fee from the compute-unit price multiplied by the requested compute-unit limit, rounded to lamports. The requested limit matters: setting it substantially above the transaction’s actual requirement can waste fees because the priority calculation uses the requested limit rather than only the units ultimately consumed.
Fees apply to failed transactions
Solana charges fees whether a transaction succeeds or fails. Retries, stale account data, invalid instructions, and under-budgeted compute therefore have a direct cost. Instrument failures by reason and include them in your cost per successful operation.
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Fee sponsorship
A Solana transaction has a designated fee payer. A production fee-abstraction system must decide who funds that account, how sponsorship is authorized, how abuse is limited, how balances are replenished, and how failures are reconciled with the user-visible operation. Sponsorship can remove a wallet hurdle, but it adds key-management, policy, monitoring, and fraud controls.
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The “Median fee ~$0.001 per transaction” statement on Solana’s Payments page is an official estimate, not a guaranteed price. Token-account creation, transaction size, signatures, compute demand, priority-market conditions, and congestion can move an individual transaction far from that median.
Transaction shape and limits
Sui considerations
- Track which objects are created, mutated, transferred, or deleted in each operation.
- Separate independently owned objects from shared objects in your workload model.
- Test gas-budget behavior for both normal and worst-case execution paths.
- Include storage retention and deletion timing when forecasting long-term cost.
- Verify object-reference and transaction constraints in the current Sui documentation before production deployment.
Solana considerations
- Count signatures, instruction data, and every account passed to every instruction.
- Measure transaction size and account-list limits with the exact program composition you intend to ship.
- Use simulation or equivalent estimation to set a compute-unit limit close to actual demand, with a deliberate safety margin.
- Choose a priority-fee policy that reacts to congestion without blindly overpaying.
- Test whether address-lookup techniques or splitting a workflow into multiple transactions changes latency, failure behavior, or user experience.
Production operations can dominate the decision
Solana transaction delivery
Solana’s production guidance warns that public RPC endpoints are rate-limited and carry no service-level agreement. A high-throughput application should not assume that a public endpoint is a production control plane. Plan capacity, health checks, routing, backoff, and failover for the regions where users submit transactions.
The same guidance discusses priority-fee APIs and gives Helius, QuickNode, and Triton as examples. Their inclusion in documentation is not a performance guarantee or endorsement. Evaluate any provider with your own workload, geographic distribution, rate limits, data-retention needs, reliability targets, and commercial terms.
Landing, confirmation, and retries
- Generate and sign the transaction with a bounded validity window.
- Submit through an endpoint selected for the user’s region and current health.
- Track the transaction through the confirmation states your product requires; do not equate an RPC acknowledgment with final application success.
- Retry only when the failure mode is retryable. Blindly resubmitting a transaction can duplicate business intent or multiply fees.
- Persist an idempotency key or business operation identifier so a client retry cannot create two successful outcomes.
- Alert on rising expiration, dropped-submission, confirmation-latency, and fee-estimation error rates.
Sui operational questions
The available material supports planning around object design, shared-state contention, gas budgets, epoch reference prices, storage fees, and rebates. It does not establish a complete, comparable inventory of Sui infrastructure providers or a measured developer-experience advantage. Validate RPC, indexing, observability, failover, and regional availability as part of your own deployment review.
A practical selection framework
Score both networks against the same workload instead of choosing from headline TPS claims. Use a weighted worksheet such as the following, then replace the weights with your product’s actual priorities.
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| Decision axis | Questions to answer | Evidence to collect |
|---|---|---|
| State model and contention | What is independent, what is shared, and where are the hotspots? | Object or account access maps, conflict rates, throughput by transaction class. |
| Atomicity and composition | Which invariants require one all-or-nothing operation? | Instruction count, account list, transaction size, split-versus-composed failure behavior. |
| Cost | What happens when data is created, changed, deleted, or retried? | Sui computation/storage charges and rebates; Solana signatures, compute limits, priority fees, and failed-transaction charges. |
| Latency and throughput | What p95 and p99 service levels are required? | Matched-load results by region, congestion level, and transaction type. |
| Delivery reliability | Can transactions be submitted, confirmed, and recovered during endpoint incidents? | RPC saturation tests, failover drills, retry rates, and recovery time. |
| Product experience | Who pays fees and how much wallet friction is acceptable? | Fee-sponsorship design, abuse controls, user failure rates, and support burden. |
Choose the network that meets your required service levels with an operational margin, not the one with the most impressive unqualified capacity claim.
Common mistakes and how to correct them
Mistake: treating protocol architecture as a benchmark
Correction: Use architecture to form hypotheses, then test matched workloads. Independent Sui objects and Solana’s composable instructions can both be advantageous, but under different access patterns.
Mistake: benchmarking only successful transactions
Correction: Include rejected, expired, under-budgeted, stale, and retried transactions. Solana fees apply to failures, and failed operations on either network consume engineering and infrastructure capacity.
Mistake: ignoring storage lifecycle
Correction: For Sui, model object creation, mutation, deletion, and possible storage rebates over the full retention period. A one-time execution estimate is not a lifecycle cost.
Mistake: setting Solana compute limits far above need
Correction: Simulate representative transactions, set a measured limit with a safety margin, and monitor actual consumption. The requested limit contributes to priority-fee calculation.
Mistake: relying on one public RPC endpoint
Correction: Design endpoint health checks, rate-limit handling, regional routing, and tested failover. Public Solana endpoints are documented as rate-limited and without an SLA.
Mistake: counting an RPC acknowledgment as settlement
Correction: Define the confirmation state your application needs, persist operation IDs, and reconcile chain state independently of the initial submission response.
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- Specify service levels. Set target throughput, p95/p99 confirmation latency, maximum retry rate, acceptable failure rate, and cost per successful business operation.
- Freeze representative data. Use realistic object sizes, account counts, instruction data, signatures, and shared-state ratios.
- Implement both versions. Preserve business semantics while adapting each implementation to its native state model; document every adaptation.
- Instrument every stage. Capture signing, submission, endpoint response, confirmation, finality, failure reason, retry, and user-visible outcome.
- Run a quiet-load baseline. Establish normal fees, latency, and resource use before applying stress.
- Increase load gradually. Find the knee where queueing, contention, endpoint throttling, or fee escalation begins.
- Inject failures. Disable an endpoint, introduce stale state, lower compute budgets, and force retries to test recovery logic.
- Review lifecycle economics. Include storage retention and deletion on Sui, and signatures, compute requests, priority fees, and failed submissions on Solana.
- Repeat after optimization. Re-run the exact test after changing data layout, transaction composition, fee policy, or provider routing.
Bottom line
Sui is a strong candidate when your workload can preserve independent object operations and when storage lifecycle economics are important. Solana is a strong candidate when your application benefits from composing account-based instructions atomically and you are prepared to engineer compute budgeting, priority-fee estimation, transaction landing, retries, and RPC redundancy.
Neither conclusion is a universal throughput ranking. Commit only after a matched, production-shaped pilot demonstrates that the chosen network meets your latency, reliability, and cost targets under the contention and failure conditions your users will actually create.
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