What is an RPC? A Guide for Hyperliquid RPC Developers

The Architecture of Hyperliquid Data Integration

Hyperliquid runs two execution environments that share the same consensus (HyperBFT): HyperCore and HyperEVM.

• HyperCore (L1) handles the on-chain order book, matching engine, perpetuals, and exchange logic with sub-second finality. It enables high-frequency trading operations entirely on-chain, without requiring a smart contract for each trade.

• HyperEVM, by contrast, is an EVM-compatible environment for general-purpose smart contracts and DeFi integrations. Developers can deploy Solidity contracts and use standard Ethereum tooling. HyperEVM leverages HYPE as its native utility token. Beyond simple gas fees, HYPE anchors the network’s decentralized security model, ensuring that every smart contract execution on the EVM layer remains computationally verified and sybil-resistant.

Because these two environments serve fundamentally different workloads, Hyperliquid exposes distinct RPC interfaces for each layer.

• HyperEVM provides the familiar JSON-RPC interface for Ethereum-style calls such as eth_call and eth_sendRawTransaction.

• HyperCore, however, exposes specialized API methods for exchanging data and trading actions, including REST endpoints and WebSocket feeds for real-time data updates.

This architectural split directly influences how builders design Hyperliquid integration.

Hyperliquid RPC: Data Streams vs API

Hyperliquid’s RPC infrastructure supports both traditional request-response calls and streaming models. Understanding the distinction is essential for high-performance systems. Pull-based models rely on JSON-RPC over HTTP. Clients send requests and receive responses. This works well for:

• Balance checks

• Transaction submission

• Occasional state queries

Push-based models rely on WebSocket streaming. Clients subscribe once and receive real-time data continuously. Below is a structured comparison:

Polling introduces delay and inefficiency. Streaming maintains a synchronized state without repetitive requests.

Production builders often combine both: JSON-RPC for deterministic transactional logic and WebSocket streams for market data.

Where does gRPC fit?

Integrate gRPC for high-volume environments where text-based JSON is too heavy. By using a binary format, gRPC minimizes data overhead and CPU usage, allowing your trading engine to process massive order-book updates with significantly less lag than traditional HTTP-based calls.

Real-Time Streams: Mastering the Hyperliquid WebSocket

Because HyperCore powers high-frequency on-chain order books, streaming becomes foundational rather than supplementary.

Through WebSocket connections, clients establish a persistent channel. Once connected, the server pushes updates automatically as events occur.

Typical subscriptions include:

• Order book depth updates

• Trade executions

• Position changes

• Funding updates

During volatile periods, streams can produce high message volumes, thereby making throughput management critical.

At scale, this influences how data is received while maintaining state integrity under pressure. To maintain high uptime, production systems should automate state resynchronization after a disconnect. Implementing exponential backoff and message buffering prevents your client from being overwhelmed by data bursts when the connection restores.

Why HypeRPC is the Preferred Private Gateway for Data

As integration complexity increases, infrastructure variability becomes the limiting factor. Public endpoints are shared infrastructure. During peak demand, congestion increases Latency and triggers strict Rate limits.

Even well-architected systems degrade if the underlying RPC provider introduces unpredictability.

HypeRPC functions as a private gateway optimized for Hyperliquid workloads. It isolates traffic and provides predictable infrastructure performance.

HypeRPC eliminates the 'noisy neighbor' effect of public endpoints. By providing dedicated bandwidth allocation and stabilized rate limits, HypeRPC ensures your trading logic executes during high-volatility events when public infrastructure typically throttles users.

HIP-4 and Beyond: Data Needs for Outcome Trading

With HIP-4 Outcome Markets, the performance bar rises further.

Outcome trading introduces event-driven dynamics that depend on precise timing and continuous state awareness.

As HIP-4 matures, developers must prioritize instantaneous order-update feeds. Because outcome markets involve fixed-range settlements without oracles, your infrastructure is the only source of truth for tracking market-driven event odds in real-time.

With these systems, low latency and sustained throughput become the determining factor on how outcome engines remain competitive.

At this stage, Hyperliquid RPC becomes a synchronization mechanism between streaming data and transactional execution.

Scaling Your Integration with a Private Hyperliquid Data API

As Hyperliquid evolves, infrastructure discipline becomes a differentiator. Although development environments tolerate throttling, production systems do not.

Once user load increases and strategies become more sophisticated, scaling Hyperliquid integration becomes essential, and this requires:

• Monitoring RPC error codes

• Tracking response times

• Proactive management of Rate limits

• Implementing failover nodes

• Horizontal scaling of client services

A private gateway reduces exposure to unpredictable throttling. Combined with observability tooling, it enables sustainable growth.

At scale, RPC infrastructure is a core architectural component.

Final Thoughts on RPCs

Hyperliquid RPCs govern how applications query state, submit transactions, and consume real-time market data across HyperCore and HyperEVM. Leveraging private gateways such as HypeRPC aligns infrastructure reliability with production demands.

Developers who understand Hyperliquid RPC at the protocol level build systems that scale predictably, execute reliably, and remain competitive as the network evolves.