Every time a wallet shows a balance, a dapp shows a token price, or a user submits a transaction, something asked a blockchain node a question. That something is usually RPC: Remote Procedure Call. RPC is the protocol that lets an application on one machine ask a program on another machine to do something and return the result. In blockchain, the program is a node, the question is a request like "what is this address balance at the latest block", and the answer comes back as structured data the app can display.
Three terms get mixed up, and the confusion causes real setup mistakes. RPC is the protocol: the method of asking. An RPC node is a server running blockchain client software that answers those requests with chain data. An RPC endpoint is the URL your app sends requests to. One endpoint URL can front a whole fleet of nodes behind a load balancer, so your requests are not tied to a single machine. Keep those three apart and most RPC architecture decisions become clearer.
RPC in one example
Take a wallet checking a balance. The wallet does not store the balance itself. The authoritative balance lives on the chain. The wallet forms a JSON-RPC request naming a method, for example eth_getBalance on Ethereum-compatible chains, with parameters for the address and the block, usually "latest". It sends that request to the RPC endpoint of a node. The node looks up the balance in its local copy of chain state and returns it. The wallet displays the number. If the node is behind chain tip, the number is stale. If the endpoint is rate-limited, the request fails. If the endpoint is down, the wallet looks broken even though the chain is fine.
That last point is the one teams underestimate. Users do not distinguish between "the chain is down" and "our RPC endpoint is down". They see a wallet that will not load and blame the app. RPC reliability is therefore product reliability. A team that treats its endpoint as an afterthought will spend launch week explaining an outage that was never a chain problem at all.
Public vs private endpoints
Public endpoints are free, shared, and rate-limited. They are fine for learning, testing, and occasional queries. They are a poor choice for production because you share capacity with everyone else, you have no guaranteed latency, and you cannot control which methods are available or how your traffic is prioritised. During a launch spike or a congested period, a public endpoint is the first thing to slow down, because every other team had the same idea.
A private endpoint is a dedicated endpoint for your app, usually behind an API key, with capacity reserved for your traffic. It gives you your own rate limits, predictable latency, method control, and the ability to add a fallback. It also gives you a bill and an operations responsibility: someone must keep the node synced, patched, and monitored. That trade is worth it the moment your app has real users, because the cost of a private endpoint is small next to the cost of users who cannot use your product.
Rate limits and latency
Rate limits are usually expressed as requests per second or per minute, per key or per IP. A wallet that polls balances for many addresses, an indexer that backfills history, and a frontend that refetches on every render can each burn through a generous limit faster than expected. Audit your request pattern before launch: which methods you call, how often, and whether you cache answers that do not change every block. Caching a token metadata response and polling a balance are different workloads, and treating them the same is how teams hit limits they thought they had headroom for.
Latency is the other silent killer. RPC latency is the round-trip time from your app to the node and back, plus the node's own lookup time. A node far from your users, a node that is still syncing, or a node serving archival queries from slow disk will all feel slow to users even when they are technically up. Measure latency from where your users are, not just from your office, and measure it for the methods you actually call. Averages hide the slow tail: the 95th percentile request is the one users remember.
RPC during a launch spike
Launches concentrate RPC traffic in a way normal days do not. A claim page, a mint, or a sale announcement can multiply requests tenfold in minutes. The failure pattern is predictable: the primary endpoint saturates, requests queue, timeouts rise, users retry, and retries make the saturation worse. The fix is also predictable, but it must be built before the spike: a second synced endpoint, health checks that detect lag behind chain tip, automatic failover to the fallback, and a retry policy with backoff instead of instant retries.
Test the failover, do not just trust it. Kill the primary in staging, watch traffic move, and confirm the fallback is actually synced and serving the same methods. A fallback that is three hundred blocks behind is not a fallback. It is a different kind of outage, one that serves confident-looking stale data. Health checks should compare block height against chain tip and fail over on lag, not only on a dead connection.
When you need your own RPC
You need your own RPC when any of these are true: your app has production users who will notice an outage, you need methods a public endpoint does not expose (such as debug or trace methods), you need historical data that requires an archival node, you need predictable latency in a specific region, or you need to control access with keys and allowlists. If none of those are true yet, a public endpoint plus a plan to migrate is reasonable. The mistake is staying on a public endpoint after the first one becomes true.
Running your own RPC is a node operations job, not a one-time setup. The node must stay synced, stay patched, stay monitored for disk growth, and stay backed by a fallback. If your team does not want that job, a managed endpoint is a legitimate choice: you pay for someone else's operations discipline. Either way, own the decision explicitly. "We use whatever endpoint was in the tutorial" is not an architecture.
If RPC reliability is becoming a product problem for your app, blockchain node setup and DevOps node operations cover private endpoints, failover, and monitoring, starting with a free infra audit of your current endpoint setup. Bring your request pattern and your launch calendar: those two facts decide how much capacity and how many fallbacks you actually need.
Disclaimer: This post is educational only, not financial advice. Figures are listing or project data as of 2026-10-29 and may change - always confirm version numbers, requirements, and dates in the official docs before acting on them.