HyperRPC

The free development plan includes automatic deletion policies to ensure fair resource allocation:

• Hard Limits:
  • Deployments that exceed 20GB of storage will be automatically deleted
  • Deployments older than 30 days will be automatically deleted
• Soft Limits (whichever comes first):
  • 100,000 events processed
  • 5GB storage used
• Inactivity: a deployment with no queries for 7 days gets a warning and is deleted 24 hours later unless it receives a query

When soft limits are breached, the two-stage deletion process begins:

• Grace Period (7 days) - Your indexer continues to function normally, you receive notification about the upcoming deletion
• Read-Only Access (3 days) - Indexer stops processing new data, existing data remains accessible for queries
• Full Deletion - Indexer and all data are permanently deleted

Timeline Subject to Change: The grace period durations (7 + 3 days) are subject to change. Always monitor your deployment status and upgrade when approaching limits.

The auto-delete feature is part of our resource management system for the development plan:

• It ensures fair resource allocation while still allowing thorough testing of your indexer.
• A deployment is flagged when it breaches a soft limit: 100,000 events processed or 5GB of storage (whichever comes first).
• Once flagged, a two-stage process begins: a 7-day grace period where the indexer keeps running normally, followed by 3 days of read-only access before the deployment and its data are deleted.
• A deployment with no queries for 7 days gets a warning and is deleted 24 hours later unless it receives a query.
• Upgrading to a production or dedicated plan at any point keeps your indexer running.

This approach lets you test your indexer at scale while keeping development resources available for everyone. The grace period durations (7 + 3 days) are subject to change, so monitor your deployment status and upgrade when approaching limits. For larger-scale indexers that run for extended periods, consider our production or dedicated plans.

HyperIndex offers broad chain support:

• All EVM (Ethereum Virtual Machine) chains are supported through our optional RPC sync configuration.
• 81+ EVM networks are HyperSync enabled, providing the blazingly fast sync speeds we're known for.
• Solana and Fuel are also fully supported.

For a comprehensive list of HyperSync-enabled networks, please check our supported networks documentation. If you need support for a specific network that isn't listed, don't hesitate to reach out to us.

Yes, we support Solana and Fuel, and are looking to expand support to other networks. Have something in mind? Please reach out!

Multichain indexing, also known as cross-chain indexing, allows you to index information from multiple blockchain networks (e.g., Ethereum and Base) within a single indexer. It offers two modes:

• Time-ordered mode: Preserves the chronological order of events across all indexed chains, enabling operations on entities from different chains while maintaining temporal consistency.
• Unordered mode: Indexes data from each chain as quickly as possible without concern for cross-chain ordering, optimizing for speed.

Both modes are frequently used by our customers for different use cases. Multichain indexing is available on all pricing plans, offering flexibility and powerful data aggregation capabilities for your project. For more detailed information about unordered mode, check out our documentation.

Our sync speeds are extremely fast and best-in-class:

• Sync millions of events in minutes, not hours.
• While previous frameworks might require weeks to sync at scale (100M+ events), HyperIndex can achieve the same feat in just over an hour.
• This represents a more than 100x improvement in sync speed compared to traditional methods.
• Our team is continuously working on optimizations, making our sync speeds even faster.

These blazing-fast sync speeds translate to quicker development cycles, reduced infrastructure costs, and the ability to handle large-scale data with ease. For more details on our performance across multiple use cases, check out our benchmarking results.