Private Monad RPC Endpoints on a Dedicated Node

A shared Monad RPC provider puts you on the same pools as everyone else. That means noisy-neighbor contention, sudden request caps, and unpredictable latency during spikes. A dedicated Monad RPC node runs on single-tenant bare metal, so capacity stays reserved for your workloads. You get more consistent p95, cleaner incident triage, and fewer "random" failures that show up only at peak traffic. This matters most for public dApps, wallets, and indexing jobs that cannot pause when traffic surges.

Monad's own hardware-requirements docs state that nodes must run on bare metal, not virtualized or cloud environments. MonadBFT enforces tight, sub-second time windows, and virtualization adds context-switching overhead plus indirect I/O to SSDs and the network that breaks the determinism Monad needs. RedSwitches runs single-tenant bare metal only, with PCIe Gen4 NVMe sized for MonadDB, which is exactly the deployment profile Monad calls for.

Yes. We can run WebSockets when your node profile calls for subscription traffic. On Monad, WebSocket subscriptions focus on block and log streams. The standard streams are newHeads and logs. Monad also exposes speculative monadNewHeads and monadLogs that publish roughly a second earlier, with extra blockId and commitState fields. Note: the syncing and newPendingTransactions subscriptions are not supported. We help you choose the right stream, then size the node so subscriber fanout stays stable under high concurrency.

You get Geth-compatible JSON-RPC for core reads and writes, across the eth, net, web3, debug, txpool, and admin namespaces. Most dApps and tooling work with standard methods like eth_call, eth_getLogs, eth_getBlockByNumber, and transaction send flows. Differences to plan for: there is no separate trace namespace (use debug tracing), EIP-4844 blob transactions are rejected on submission, and pending-transaction visibility behaves differently under parallel execution. We set expectations early so your team does not ship features that assume Ethereum mainnet behavior.

Use small block ranges and parallelize by time window. Large ranges increase payload size, processing time, and failure rates, especially during backfills. A better pattern is to pull logs in small ranges, checkpoint the last processed block, and retry failed windows with backoff. For heavy indexing, we recommend keeping indexing traffic isolated from user RPC traffic. Your Monad RPC node stays responsive for users while your pipeline ingests logs consistently.

Yes, via Monad's debug namespace (debug_traceBlockByHash, debug_traceCall, and related). Note Monad does not expose a separate trace namespace. Debug tracing is expensive: it raises CPU and disk pressure and can degrade normal RPC latency if it shares the same endpoint. For production we recommend a separate endpoint or a dedicated archive node for debug-heavy teams. That keeps the main Monad RPC node fast for app reads while developers still get deeper introspection.

We provision your server quickly, then plan go-live around your sync window. The key variable is time-to-caught-up plus stability checks after catch-up. Production-ready means your node stays at tip, serves reads consistently, and handles your expected concurrency without drifting. If you need a strict launch date, we help you choose a node profile and region that reduces sync risk and gives you a safer buffer.

A full node is great for current state reads and historical transactional lookups like blocks, receipts, and logs, retained in MonadDB. If your app needs deeper historical data, design around events and indexing rather than relying on arbitrary old state calls. For deeper transactional history workflows, we can propose a higher-capacity archive-style node based on your query patterns, and we will size NVMe so your Monad RPC node matches your real lookback needs.

Yes. Many teams run a private RPC for backends, bots, and internal tools. You can keep your endpoint private by restricting ingress, allowing only trusted IPs, and routing access through your own services. In production, we recommend a layered approach: private-by-default, strict allowlists, and separate endpoints for internal ops versus public app traffic. This reduces attack surface and prevents costly abuse.

No. Unlike shared RPC providers that bill per "compute unit" and throttle you past a quota, a dedicated node is a flat monthly price with unlimited requests and zero overages. One node equals all the throughput your hardware can serve. That makes budgeting predictable and removes the surprise bills that come with usage-based RPC pricing.

Shared RPC pools serve thousands of customers from pooled infrastructure, so you inherit rate limits, noisy-neighbor latency, and compute-unit billing. A RedSwitches node runs on single-tenant bare metal that is yours alone: reserved CPU, RAM, and NVMe, a dedicated 10/25 Gbps port, and a private endpoint you control. Performance tracks your hardware, not another tenant's traffic.

No. We provision from current snapshots so your node is live in hours rather than syncing from genesis for days. You can run it yourself with full root access, or choose our fully managed option where our engineers handle the sync, updates, and monitoring. Either way you receive a working private endpoint, not an empty server.

Yes. With 20+ Tier III locations across the US, EU, Asia, and Australia you can place your node in the same region as your users or a chain's sequencer to cut round-trip latency. Tell us your target region and we will recommend the closest facility. You can also run multi-region nodes for redundancy and split read and write endpoints.