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What an RPC node does—and why your endpoint matters
An application connects to a blockchain node to read data or send transactions. As Ethereum.org explains, “In order for a software application to interact with the Ethereum blockchain (i.e., read blockchain data and/or send transactions to the network), it must connect to an Ethereum node.” Ethereum clients implement JSON-RPC, giving applications a common set of methods; hosted node services provide endpoints so developers can access supported chains without running all the node infrastructure themselves.
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An endpoint that performs well on one read method from one region may be a poor fit for another app. HTTP read latency does not establish transaction inclusion time, WebSocket stability, archive-data correctness or end-to-end application performance. Treat “fastest” and “most reliable” as questions about your own workload, not universal provider labels.
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Define the workload before comparing providers
Write down what the application actually needs. A benchmark is useful only when the tested endpoints support the necessary chain and methods, and the test resembles production traffic.
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- Networks and methods: List each target chain and the exact JSON-RPC calls you need, separating reads from transaction submission. Note whether you need enhanced APIs, tracing or debugging methods.
- Request shape and volume: Record representative payload sizes, log-query ranges, peak request volume and expected traffic patterns.
- Connections: Specify whether the app uses HTTP, secure WebSockets or both, and whether it requires persistent subscriptions.
- Data depth: Identify any archive-history requirements and how far back the app must query.
- Geography: Identify where users or backend services originate. Measure from those relevant regions, rather than assuming one region represents them all.
Build a shortlist from current service documentation
Ethereum.org’s node-services directory lists managed options including Alchemy, Ankr, Chainstack, dRPC, Infura and QuickNode, alongside other providers. It illustrates how offerings can differ: some list HTTP and WebSocket access, dedicated or archive nodes, regional options, analytics, support or service-level commitments.
Use the directory to discover candidates, not as an endorsement or a current guarantee. For each service, confirm directly that its current offering supports your chains and methods, and check capacity, limits, pricing and service terms before relying on it.
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Use published benchmarks to narrow candidates, not name a universal winner
Published results are snapshots of specific tests. Their rankings may change with the window, region, account tier, chain, method mix and connection setup. A latency number usually describes successful responses; read it alongside the success rate and the treatment of failures, timeouts and rate limits.
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Chainstack Labs’ September 2026 Ethereum benchmark compared standard shared endpoints for Chainstack, Alchemy, QuickNode and dRPC. The report describes a seven-day rolling window refreshed every three minutes, with concurrent tests from Frankfurt, a US test node and Singapore. Its global summary reported:
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| Provider | Availability | P50 | P95 | P99 |
|---|---|---|---|---|
| Chainstack | 99.93% | 41 ms | 86 ms | 140 ms |
| Alchemy | 99.96% | 37 ms | 123 ms | 209 ms |
| QuickNode | 99.94% | 81 ms | 173 ms | 253 ms |
| dRPC | 99.90% | 52 ms | 576 ms | 1,370 ms |
In that tested set, Chainstack Labs reported the lowest global P95; its report also called out a US P99 tail-latency spike for dRPC. These are the publisher’s results for its stated setup, not a guarantee that the same ordering applies to another workload or date. The table also shows why a median alone can mislead: dRPC’s P50 was 52 ms, while its reported P95 was 576 ms and P99 was 1,370 ms.
Other benchmark pages use different methods
Alchemy’s live benchmark compares Alchemy, QuickNode, dRPC and Infura on configured EVM read tests. Its methods include eth_getBalance, eth_getBlockByNumber, eth_getLogs at several ranges and eth_getTransactionReceipt. It displays latency percentiles, success rates and failed-request counts. Its methodology uses warmed, reused HTTP connections, excludes full application workflows, writes, WebSockets, cold connection setup and many possible provider, chain, region, method and payload combinations, and counts failed attempts without retrying them away.
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The live page displayed the following 24-hour aggregate across its configured EVM benchmark set when viewed October 4, 2026. These are a dated page snapshot, not current guarantees:
| Provider | Average | P50 | P95 | Success |
|---|---|---|---|---|
| Alchemy | 13.34 ms | 6.11 ms | 28.98 ms | 99.99% |
| QuickNode | 41.34 ms | 9.59 ms | 182.27 ms | 100% |
| dRPC | 108.46 ms | 28.70 ms | 579.11 ms | 99.99% |
| Infura | 412.33 ms | 140.11 ms | 1,411.49 ms | 0.77% |
Because this is a live benchmark, its values change as the window advances. Check the page for current figures and methodology before using it to shortlist services.
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QuickNode’s benchmark methodology describes probes from 15 hosts across AWS, Google Cloud and Oracle Cloud in five regions: N. Virginia, California, Frankfurt, Tokyo and Singapore. It says probes open fresh connections, use identical payloads, chain, region and schedule for each provider, and record failures, timeouts and rate limits rather than masking them with retries. Its latency figures count successful responses only.
Do not rank figures from these publishers as if they came from one identical test. Their regions, connection approaches, scopes and windows differ. Use them to identify candidates and useful measurement practices; let a controlled test of your own workload decide the application-specific choice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run a representative comparison
- Test the same conditions. Where possible, send requests concurrently from the same infrastructure and region to every candidate. Hold chain, method, payload, schedule, timeout and success definition constant.
- Capture every attempt. Record successful responses, errors, throttling and timeouts. Do not hide failures behind retries; if your production client retries, record retry behavior separately so you can see both raw endpoint reliability and application-level recovery.
- Separate latency from reliability. Calculate success rate independently from response time. Report P50 and P95/P99 for successful responses rather than relying on an average alone. P95 means 95% of successful requests for the measured method, chain, region and time window completed at or below that latency.
- Break out important cases. Segment results by region and method so a slow log query or weak regional result is not averaged away by faster calls elsewhere.
- Repeat over a meaningful window. Include realistic demand and label the measurement period and test conditions. A short test may miss time-dependent variation.
- Test what a synthetic read cannot prove. If important to the product, separately evaluate transaction submission, WebSocket subscriptions, archive access and application-level workflows. A single-method read test does not establish their performance or reliability.
Compare operational fit, not just speed
| Decision axis | Questions to check |
|---|---|
| Chain and method coverage | Does the endpoint support every target network and required method? Are enhanced APIs needed? |
| Latency and geography | What are successful P50, P95 and P99 times from relevant regions, broken out by important method? |
| Reliability | What are the success and error rates, timeout behavior, rate limits and observed regional failure patterns? |
| Transport | Are HTTP and secure WebSockets available? Are persistent subscriptions suitable for the application? |
| Data depth | Does the app need archive history, tracing or debugging methods, and which plan includes them? |
| Capacity and price model | Is capacity shared or dedicated? How are usage, rate limits, overages and scaling handled? Confirm current vendor terms. |
| Operations | What support and SLA commitments apply? Can the app fail over to a second provider or use a load balancer? |
| Security and control | Are endpoint keys, access restrictions and operational controls appropriate for the application? |
For additional tooling ideas, Ethereum.org’s developer tools catalog identifies an endpoint utility that latency-tests public RPCs and a self-hosted NodeCore load balancer that scores upstream endpoints by latency, error rate and availability. Their listing is a starting point for investigating comparison or routing options, not a recommendation or verification of current suitability.
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