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What you are actually monitoring
A useful Sui monitoring setup answers four different questions:
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- Did this transaction finish? Check its execution status, digest, checkpoint and timestamp.
- What did it cost? Read the executed net gas fee and the components behind it, rather than assuming the gas budget was spent.
- How aggressively was it priced? Compare the transaction’s gas price with the reference gas price for its epoch and note any tip.
- Is demand putting pressure on the network? Watch several indicators over time, including tips, transaction outcomes, checkpoint progress and your own latency. No single field defines a universal congestion threshold.
These questions require different data-access methods. A block explorer is ideal for one-off inspection. GraphQL lets you select exactly the transaction, epoch, event and balance fields needed by a dashboard. gRPC is better for a high-throughput stream of finalized checkpoints. A custom index supplies joins, retention and application-specific metrics, while archival access covers history that a pruned full node no longer stores.
Check one Sui transaction in an explorer
For an incident, support request or fee audit, start with the transaction digest. An explorer’s transaction page normally exposes the execution result, checkpoint, time, gas price, gas budget and net gas fee. Record those values together; a single number without its context is easy to misread.
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- Open the explorer’s transaction search. Paste the complete digest, not an address or a shortened fragment.
- Confirm execution status. A failed transaction can still consume computation gas, so do not treat a failure as a zero-cost event.
- Record checkpoint and timestamp. These let you correlate the transaction with other activity and with the epoch in which it executed.
- Record gas price and gas budget. The price is what the transaction offered per unit; the budget is the upper spending limit permitted for execution.
- Read the net gas fee. Sui’s gas documentation says the explorer displays the net fee for each transaction. Use this executed amount for accounting.
The explorer’s net value is not the same as the budget. A budget caps permitted spending; the final net fee reflects the computation and storage actually used, less any storage rebate. Keep the digest with your notes so another operator can reproduce the lookup.
How Sui calculates the fee
Sui separates computation and storage costs. The protocol’s net-gas definition is:
Net gas fee = computation gas fee + storage gas fee − storage rebate.
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Computation cost
Computation gas pays for execution work. Its unit price is the transaction gas price. That price can equal the epoch reference gas price or include an additional tip.
Storage cost and rebate
Storage gas covers the state a transaction creates or updates. When state is later deleted, the protocol can return part of the originally paid storage amount as a rebate. Current Sui gas documentation describes storage rebates as 99% of originally paid storage fees. Treat that percentage as a protocol detail that can change; verify it against the documentation used by your network and software version.
Budget, price and fee are different fields
| Value | What it means | How to use it |
|---|---|---|
| Gas budget | The maximum spending the transaction permits. | Use it to detect an undersized limit or a deliberately large safety cap; do not report it as the amount paid. |
| Gas price | The price offered per unit of computation gas. | Compare it with the epoch reference price to identify a premium tip. |
| Net gas fee | The executed computation and storage charges after the storage rebate. | Use it for the transaction’s actual cost. |
Current gas documentation lists a minimum gas budget of 2,000 MIST (0.000002 SUI) and a maximum of 50 billion MIST (50 SUI). These are implementation limits, not a promise that a transaction will consume either amount. They are also volatile: confirm them before enforcing them in a wallet, service or monitoring rule.
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Reference gas price, tips and congestion
Validators update an epoch reference gas price at epoch boundaries. A transaction may submit that reference price or add a tip above it. Under ordinary conditions users are not expected to pay tips. Sui documentation describes tips as a market-based mechanism for extreme congestion, when validators cannot scale quickly enough: a higher offered price can discourage additional demand and help prioritize transactions.
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Tip amount = transaction gas price − epoch reference gas price.
A positive result tells you that the transaction paid above the reference anchor. It does not prove that the entire network was congested. A single application may choose a premium, a wallet may apply policy, or a short-lived burst may affect only part of the workload. Combine the comparison with a time series of transaction outcomes, checkpoint progress and observed confirmation latency before declaring a congestion event.
Repeatable monitoring with GraphQL RPC
GraphQL is the most flexible starting point for a dashboard or an operational script that asks different questions from the same endpoint. In a GraphQL client or explorer-provided GraphiQL interface, query only the fields needed for the job and save the query with its variables.
Transaction-level fields
For a digest lookup, request the transaction’s gas sponsor (when exposed by the schema), gas price, gas budget, execution effects and status, checkpoint, timestamp and the epoch reference gas price. Store the digest as the primary key. Persist the raw response or a schema-versioned copy if you need to audit later, because field names and retention policies can evolve.
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Epoch-level fields
For a current operating view, request the latest epoch’s reference gas price. For historical analysis, request each epoch’s total gas fees alongside its reference price and boundaries. This lets you distinguish a one-transaction premium from a period in which aggregate fee demand rose.
Pagination is mandatory for history
Do not assume one GraphQL response contains every transaction or event. Use the endpoint’s cursor-based pagination, request a bounded page size, save the returned cursor and continue until the response indicates no next page. Checkpoint- or time-based windows are easier to resume than an unbounded query. Apply backoff for transient errors and record the endpoint and query timestamp in your monitoring data.
Build a practical query record
- Identity: digest, sender or gas sponsor, checkpoint and timestamp.
- Execution: status and effects, including failure information where available.
- Pricing: gas price, gas budget and the epoch reference gas price.
- Accounting: computation fee, storage fee, storage rebate and net fee when the schema exposes the components.
- Context: epoch identifier and, for aggregate views, epoch total gas fees.
Keep the original units. MIST and SUI are not interchangeable in a report; convert only at the presentation layer and label the conversion.
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| Need | Best fit | Implementation guidance |
|---|---|---|
| Flexible one-off queries or a dashboard spanning transactions, checkpoints, events and balances | GraphQL RPC | Select narrow field sets, paginate large results and retain cursors for recovery. |
| High-throughput live processing | gRPC | Subscribe to finalized checkpoints and request only the transaction and event data your pipeline consumes. |
| Custom joins, derived metrics or controlled retention | Application-specific indexer | Define a data model, reprocessing strategy and retention period that match your product. |
| Lookups older than a pruned full node retains | Archival store or service | Use archival access for complete historical coverage instead of assuming a recent-history RPC can answer every query. |
| Control over availability, read performance and retention | Self-operated full node | Gain infrastructure control, but accept the work of upgrades, storage, monitoring and incident response. |
GraphQL for dashboards and backends
GraphQL is appropriate when analysts need to change filters or combine related objects without maintaining a separate ingestion pipeline. It is also a good fit for a backend that serves recent transaction views. Limit concurrency, cache stable epoch data and avoid polling the same object more often than necessary.
gRPC for a live stream
Sui documentation recommends gRPC for high-throughput use and describes subscriptions to finalized checkpoints with selected transaction and event data. Consume checkpoints in order, persist the last successfully processed checkpoint, and make handlers idempotent so a reconnect does not duplicate alerts or accounting records.
When to add an indexer
Build an application-specific index when you need joins that are expensive to perform repeatedly, derived metrics such as per-tenant fee totals, or retention longer than the source endpoint offers. Define how you will backfill, handle schema changes and reconcile a reprocessed checkpoint with data already emitted to users.
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When archival access is necessary
Full nodes are state replicas that serve RPC requests, but a node may prune older data. If a compliance report or investigation reaches beyond that retention window, use an archival store or service designed for historical lookup. Confirm the archive’s coverage and consistency before making it the sole source for an audit.
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Public Sui GraphQL endpoints are rate-limited and intended for development or public-good access. They are useful for prototypes and occasional investigation, but production applications should evaluate a managed provider endpoint or operate their own infrastructure against explicit uptime, throughput and retention requirements. The documentation does not establish a universal request quota, so do not hard-code an assumed number.
Managed endpoint
A hosted Sui RPC or data service can reduce the operational burden when your team needs predictable access, scaling or support. Compare providers on measured latency, rate-limit policy, historical coverage, authentication, data residency and failure behavior. A provider is optional; it is not required for a one-transaction check.
Self-operated stack
Running a full node or indexer gives you control over read capacity and retention policy. It also makes you responsible for capacity planning, software upgrades, disk health, peer connectivity, backups, alerting and recovery. Choose it when those controls justify the operational cost, not merely because a public endpoint returned an error once.
Monitor a network, not just a fee
A useful congestion view combines independent observations:
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- Tip distribution: Track the share and size of gas prices above the epoch reference price. Persistent premiums are more informative than one outlier.
- Confirmation behavior: Measure the time from submission to finalized checkpoint for your own transactions, separating client, endpoint and chain delays.
- Execution outcomes: Watch failure rates and repeated attempts by workload; application errors can look like network trouble.
- Checkpoint progression: Confirm that finalized checkpoints continue advancing and that your consumer is not merely falling behind.
- Aggregate fees: Compare epoch total gas fees over equivalent periods, while remembering that higher totals can reflect higher activity rather than impaired service.
- Infrastructure health: If you run a node, use Sui’s monitoring guidance for node health and performance metrics. Those metrics describe your replica and service quality; they do not define a universal public congestion threshold.
Set alerts on combinations, such as a sustained rise in tips together with longer finalization times, rather than on a single premium transaction. Keep alert thresholds tied to your workload’s normal baseline and document the epoch and endpoint used for each measurement.
Troubleshoot misleading readings
“The budget is huge, so the transaction must have cost a lot.”
Check the net gas fee and its computation, storage and rebate components. The budget is only the permitted ceiling.
“A tip means the whole network is congested.”
Compare the price with the epoch reference, then examine a sample over time and your confirmation latency. A tip is a signal, not a complete congestion index.
“The query returned no old transaction.”
Determine whether the node or endpoint prunes history. Retry through an archival source and record the coverage boundary so the missing result is not mistaken for a nonexistent digest.
“The dashboard is missing transactions.”
Inspect pagination cursors, checkpoint gaps, endpoint rate limits and consumer offsets. For streams, resume from the last committed finalized checkpoint and make processing idempotent.
“Fees differ between two reports.”
Check whether one report shows the gas budget, gross computation and storage charges, or the explorer’s net fee. Confirm that both reports use the same epoch, units and rebate treatment.
A repeatable operating checklist
- For an individual digest, capture status, checkpoint, timestamp, gas price, gas budget and net fee from an explorer.
- For automation, select the corresponding transaction and epoch fields through GraphQL and paginate every collection.
- For live ingestion, subscribe to finalized checkpoints through gRPC and checkpoint your consumer offset.
- Calculate the difference between transaction gas price and epoch reference price, labeling it as a tip rather than a congestion verdict.
- Track confirmation latency, outcomes, checkpoint progress and aggregate epoch fees as a time series.
- Move to an indexer for custom joins or retention, and to archival access for history beyond node pruning.
- Before production launch, replace public development endpoints with a provider or self-operated stack that meets your measured uptime and throughput needs.
The Bottom Line
Monitor Sui in layers: explorer for a digest, GraphQL for selective history and dashboards, gRPC for finalized-checkpoint streams, and indexing or archival infrastructure for durable analysis. Judge cost from the executed net fee, judge pricing against the epoch reference gas price, and treat tips as one contextual signal rather than proof of network-wide congestion.
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