AVSs (Autonomous Verifiable Services) reward stakers and operators in exchange for security for third party services (supply-side fees).

EigenCloud is a marketplace connecting stakers and operators with applications that need active security. EigenCloud was founded by Sreeram Kannan and launched in 2023.

EigenCloud team and investors own and govern the protocol. They do not currently take a cut of the total rewards paid by AVSs (revenue). EigenCloud's investors include Blockchain Capital, Polychain Capital, Coinbase Ventures, etc.

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Liquid staking

EigenCloud

# EigenCloud Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for EigenLayer measures the number of unique users who performed any liquid staking activity within a rolling 7-day window. This metric captures user engagement with the EigenLayer protocol by tracking deposits, withdrawals, and delegation operations. The calculation uses HyperLogLog probabilistic counting to efficiently handle large-scale distinct user aggregation across time windows and provides breakdowns by project, chain, user type, and product dimensions.

## 2. Data sources

The calculation relies on the following raw blockchain data sources:

- **Ethereum event logs**: Parsed events from EigenLayer v1 smart contracts deployed on Ethereum
- **StrategyManager contract events**: `Deposit` and `WithdrawalCompleted` events containing user addresses and staking operations  
- **DelegationManager contract events**: `OperatorSharesDecreased` and `OperatorSharesIncreased` events tracking delegation changes
- **Contract metadata**: EigenLayer protocol contract addresses and event schemas for proper parsing
- **Blockchain transaction data**: Transaction hashes, block timestamps, and log indices for event context

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identifies all user addresses that interacted with EigenLayer contracts and classifies their activity type
- **Logic**: Extracts user addresses from four specific EigenLayer contract events on Ethereum. `Deposit` events identify users as 'Depositor' type by extracting the `depositor` parameter. `WithdrawalCompleted` events classify users as 'Withdrawer' type from the `withdrawer` parameter. `OperatorSharesIncreased` events identify 'Depositor' type users from the `staker` parameter. `OperatorSharesDecreased` events identify 'Withdrawer' type users from the `staker` parameter. Each interaction is tagged with liquid staking as both the market sector and business segment.
- **Key fields**: `block_timestamp` for timing, `user_address` for unique user identification, `user_type` for activity classification, `chain` for network identification, `transaction_hash` and `log_index` for event uniqueness

### Component B: Daily active user aggregation  
- **Purpose**: Aggregates user interactions into daily unique active user counts using probabilistic counting
- **Logic**: Groups all user interactions by day using timestamp truncation to daily granularity. Creates distinct combinations of date, chain, user type, and product dimensions. Initializes HyperLogLog sketches with 15-bit precision for each user address to enable efficient distinct counting. Applies chain availability filtering to ensure data quality across different blockchain networks.
- **Key fields**: `timestamp` truncated to day, `user_dau` containing unique user addresses, `timestamp_grain` set to 'day', dimensional fields for breakdown categories

### Component C: Rolling window generation
- **Purpose**: Creates 7-day rolling windows for each date to capture weekly activity patterns
- **Logic**: Generates a complete date spine from the earliest available data to the current date minus one day to avoid incomplete data. For each date in the spine, creates join relationships with the previous 6 days to form 7-day windows. Joins the rolling windows with daily HyperLogLog sketches to combine user data across the weekly time period. Applies incremental processing filters for efficient data updates.
- **Key fields**: `timestamp` for the window end date, `joinday` for each day within the 7-day window, `hll` containing merged probabilistic user counts

### Component D: Multi-dimensional WAU calculation
- **Purpose**: Calculates weekly active users across four different dimensional breakdowns
- **Logic**: Merges HyperLogLog sketches within each 7-day window to count unique users probabilistically. Creates four separate breakdowns: project-level aggregation with all dimensions set to null for overall WAU, chain-level breakdown preserving chain identity, user type breakdown preserving user classification, and product-level breakdown preserving product identification. Extracts final WAU counts from merged HyperLogLog sketches using the extract function.
- **Key fields**: `user_wau` containing the final weekly active user count, `dimensions` indicating breakdown type, dimensional identifier fields for appropriate breakdown level

### Final aggregation
- **Combining components**: Unions all four dimensional breakdowns into a single result set with consistent schema structure
- **Currency conversion**: Not applicable for user count metrics  
- **Time aggregation**: Uses 7-day rolling windows where each date represents users active in that date plus the previous 6 days
- **Final calculation**: The weekly active user count for any given date equals the distinct count of users who performed any EigenLayer liquid staking operation within the 7-day window ending on that date, calculated using HyperLogLog probabilistic estimation for scalable distinct counting

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique revenue-generating users over a rolling 7-day window.

EigenCloudEIGEN

Active users (weekly)

Active users (weekly)

Competitive landscape