Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsRun the test from the same server or region that will send Solana requests, use the same RPC method, parameters, and commitment for every endpoint, and repeat the test enough times to calculate median and tail latency. Record errors, timeouts, and the slot returned by each node. A quick response is not useful if the node is behind the cluster and serving stale state.
What Solana RPC latency actually measures
RPC latency is the elapsed time between sending an HTTP request and receiving the response. From your application’s point of view, that interval includes several components:
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- Network distance between your machine and the RPC service.
- Internet routing, including anycast or provider-specific regional routing.
- Connection setup, TLS negotiation, and transport overhead when a connection is not reused.
- Queueing and capacity pressure at the RPC provider.
- The time required to execute the requested Solana RPC method.
Therefore, a timing from a laptop in another country is not a reliable estimate for an application running in a cloud region. Solana’s provider guidance recommends measuring from the same regions as the application. There is also no universal “good” latency number: the useful result is the distribution observed by your users, under the workload and commitment level your application actually uses.
Keep HTTP request latency separate from two other experiences. A transaction flow has an end-to-end interval from submission to seeing the signature reach the desired status. A WebSocket subscription has connection, notification, reconnect, and missed-message recovery behavior. Test each workflow independently.
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Choose the right test vantage point
Run from the deployment region
Use the machine, cloud region, edge location, or office network that will originate production requests. If your service runs in more than one region, test each region separately; the fastest endpoint in one location may be slower in another.
Record the vantage point with every sample. “Frankfurt,” for example, is not enough if the test sometimes runs from a local workstation and sometimes from a cloud VM. Keep the provider, operating system, network type, and test time with the results so that later comparisons remain meaningful.
Account for routing behavior
Some services use anycast or dynamically select an RPC server. The address in your configuration may not identify one fixed machine, so repeat tests at different times and from every production region. A single low reading can reflect a temporary route rather than a stable advantage.
Select the cluster and endpoint
First decide whether the application reads Mainnet, Devnet, or Testnet. An endpoint on one cluster cannot answer questions about another cluster’s state. Solana publishes public HTTP RPC endpoints for these networks; use the endpoint for the exact cluster your application targets. The official Mainnet endpoint is https://api.mainnet.solana.com.
Public endpoints are shared infrastructure intended for development and are rate-limited. They are useful as a reference, but a production comparison should include the private RPC services you are considering, tested from the same deployment regions. Do not infer production capacity or support from one quick result against a shared public service.
Use a representative RPC request
Start with a lightweight health check
getSlot is a small read that lets you measure basic request and response behavior. It is suitable for a first pass and makes it easy to compare many endpoints with the same JSON-RPC 2.0 body.
Add the method your application really needs
getLatestBlockhash is another useful read and is included in Solana Foundation monitoring checks. If your application performs account, program, transaction, or token reads, include those methods as a separate workload. Different methods can have different execution costs, so a provider that is quick for getSlot may not be quick for a heavier account query.
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Keep the request shape fixed. Use the same parameters, encoding, account list, and commitment for every endpoint in a comparison. Changing any of those variables changes the work performed and makes the timings difficult to interpret.
Understand commitment before comparing results
Commitment tells the node how finalized the returned state must be:
| Commitment | Meaning | Typical measurement implication |
|---|---|---|
processed |
Newest data observed by the node; it can still be rolled back. | Often the freshest view, but not the strongest confirmation. |
confirmed |
The block has been voted on by a supermajority. | A middle ground between freshness and finality. |
finalized |
The strongest confirmation state. | May require waiting for a more settled block. |
Use one commitment value across all endpoints when comparing them. If your application mixes commitments, benchmark each workflow separately rather than averaging unlike requests.
Run a first latency check with curl
Solana RPC uses JSON-RPC 2.0 over an HTTP POST with a JSON request body. This command measures the complete round trip observed by curl:
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curl -sS -o /dev/null -w 'total=%{time_total}sn'
-H 'Content-Type: application/json'
-d '{"jsonrpc":"2.0","id":1,"method":"getSlot","params":[]}'
'https://api.mainnet.solana.com'Replace the URL with the endpoint under test, and run the command from the target region. The value includes network transit, provider routing, queueing, and method execution; it does not isolate those components. A one-off result is only a spot check, not a provider ranking.
Repeat the request and retain failures
Run enough samples to show normal variation and tail behavior. Thirty to one hundred requests is a practical starting range for a manual comparison, with the same interval and request body for each endpoint. If your service has predictable busy periods, run a second sample during representative peak load.
A simple shell loop prints one timing per request:
for i in $(seq 1 30); do
printf '%s sample=%s ' "$(date -Iseconds)" "$i"
curl -sS --max-time 10 -o /dev/null
-w 'http=%{http_code} total=%{time_total}sn'
-H 'Content-Type: application/json'
-d '{"jsonrpc":"2.0","id":1,"method":"getSlot","params":[]}'
'https://api.mainnet.solana.com' || echo 'curl_error=1'
sleep 1
doneThe ten-second limit in this example prevents a hung request from blocking the series; choose a timeout that reflects your application’s own deadline. Do not discard failed commands, HTTP errors, JSON-RPC errors, or timeouts. They are part of the endpoint’s observed reliability.
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For a production-quality harness, save each response and timestamp in a file or metrics system. Include an endpoint identifier, region, cluster, method, parameters, commitment, HTTP status, RPC error code, elapsed time, and whether the request timed out.
Capture freshness as well as speed
When a method returns a slot, save that slot with the timing. Compare it with a second healthy endpoint queried at approximately the same time. A node can answer quickly while lagging behind the cluster, which means your application may render old balances, miss a recent block, or make a decision using stale state.
There is no universal lag threshold in the provider guidance. Define an operational tolerance for your own application, then alert when an endpoint falls behind that tolerance or diverges materially from your reference. Treat freshness as a separate decision axis rather than hiding it inside an average latency number.
Summarize the measurements correctly
Use median for the typical request
Sort successful elapsed times and report the median. It describes the middle observation and is less distorted by an occasional slow request than an arithmetic mean. Report the sample period and region beside the value.
Use p95 and p99 for user-visible tails
Calculate p95 and p99 when the sample is large enough to make those percentiles meaningful. They show how slow the slowest normal requests become. A service with a slightly slower median but a much smaller p99 can feel better than one with an excellent median and frequent long stalls.
Report errors and timeouts as rates
Count successful responses, HTTP failures, JSON-RPC errors, and client-side timeouts. A provider that is fast only when requests succeed may be worse for users than a slightly slower endpoint with a consistently high success rate. State the denominator and the timeout setting so another engineer can reproduce the calculation.
Solana’s guidance recommends comparing median and tail latency alongside errors and timeouts, but it does not set a location-independent latency target. Your service’s deadlines, user interface, and transaction requirements determine what is acceptable.
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Compare endpoints on the same scorecard
| Dimension | What to record | Why it matters |
|---|---|---|
| Region | Actual client or server region and test time | Network paths and routing differ by location. |
| Request workload | Method, parameters, encoding, and commitment | RPC methods have different execution costs. |
| Latency distribution | Individual samples, median, p95, and p99 | Shows typical and tail user experience. |
| Reliability | Success rate, HTTP errors, RPC errors, and timeouts | Captures failures that a latency average hides. |
| Freshness | Returned slot and difference from a healthy reference | Fast responses can still contain stale state. |
| Connection behavior | HTTP reuse and WebSocket reconnect results | Persistent applications depend on more than one request. |
| Production fit | Capacity, rate limits, support, and fallback design | A benchmark alone does not establish operational suitability. |
Keep a separate row for every region and endpoint. Do not combine all regions into one average: that number can conceal a poor result for the users who matter most.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure transaction latency as a separate workflow
If your application sends transactions, a read benchmark is incomplete. Measure the interval from submission to observing the signature at the commitment your application requires. Keep the transaction payload, preflight settings, commitment, and confirmation method fixed across endpoints.
Record at least the submission timestamp, the RPC response, whether the node accepted the transaction, the time the signature became visible, and the time it reached the required commitment. Separate submission failures from confirmation delays. A provider can have fast reads but poor transaction propagation, or the reverse.
Test WebSocket subscriptions independently
HTTP timings do not measure subscription reliability. For a WebSocket workflow, test initial connection time, time to receive an expected notification, behavior after an intentional disconnect, reconnect time, and recovery of notifications missed during the outage.
Run the test with the same subscription type and commitment your application uses. Record whether the stream resumes automatically, whether duplicate notifications occur, and whether your client must resynchronize with an HTTP read after reconnecting. These results belong in a separate scorecard from HTTP p95 and p99.
Troubleshoot surprising results
Every endpoint is slow from one region
Check the local network, firewall, DNS path, proxy, and cloud-region routing. Repeat from another machine in the same region. If all candidates change together, the bottleneck is more likely the vantage point or shared network path than one provider.
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Repeat with the same request and sample window, then inspect timeouts and HTTP or JSON-RPC errors. A high p95 or p99 isolated to one endpoint can indicate provider queueing, capacity limits, or a route-specific problem. Do not replace the result with the fastest single sample.
Latency is low but slots are behind
Treat the endpoint as stale for the affected workflow. Compare returned slots with a healthy reference, check whether the lag persists, and route freshness-sensitive reads elsewhere until the node catches up.
Results change between runs
Check the test time, region, request mix, commitment, and connection reuse. Shared public services and dynamic routing can vary. Run at several times, preserve the raw samples, and compare distributions rather than selecting one favorable run.
Requests fail only under load
Increase concurrency gradually while keeping the method and parameters constant. Track error and timeout rates as load rises, and stop before the test becomes an unsafe production-like flood. Use the resulting capacity and tail behavior to evaluate whether a private service and a fallback path are necessary.
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Choose an endpoint only after checking all of the following for every application region:
- Its median, p95, and p99 meet your workflow’s latency needs.
- Its success, error, and timeout rates remain acceptable during representative load.
- Returned slots are fresh enough for the decisions your application makes.
- Its rate limits and capacity match expected traffic, not just a short test.
- It offers the operational support and regional coverage your team requires.
- Your client has a defined fallback or failover behavior.
For production workloads, Solana’s guidance favors private RPC access with capacity and support matched to traffic. Treat public endpoints as shared development infrastructure and avoid using a low-latency spot check as proof of production readiness.
Maintain the measurement over time
RPC performance is not permanent. Routes, provider capacity, public limits, and cluster conditions can change. Schedule recurring checks from each deployment region and alert on shifts in p95, p99, error rate, timeout rate, or slot freshness.
Keep the test definition versioned: endpoint, cluster, method, request body, commitment, timeout, sample count, and region. When an alert fires, compare raw samples with the last known-good period before changing providers. The maintained monitor software and provider implementations can also change, so verify current behavior before treating an automated check as authoritative.
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Quick checklist
- Run tests from the actual application region.
- Use the correct Solana cluster and record the endpoint.
- Send identical JSON-RPC POST requests to every candidate.
- Test the methods and commitment levels the application really uses.
- Repeat requests and retain failures and timeouts.
- Report median, p95, p99, success rate, and error rate.
- Compare returned slots for freshness.
- Measure transaction confirmation separately from reads.
- Measure WebSocket reconnect and notification recovery separately.
- Evaluate private capacity, limits, support, and fallback before production.
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