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 (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who interact with the EigenLayer liquid staking protocol within a rolling 30-day period. This metric captures user engagement by tracking distinct addresses that perform staking operations (deposits, withdrawals, delegation changes) over a monthly window. The calculation uses HyperLogLog approximation for scalable distinct counting and provides dimensional breakdowns across project, chain, user type, and product levels.

## 2. Data sources

The calculation relies on the following blockchain data sources:

- **EigenLayer protocol events**: Parsed Ethereum event logs from EigenLayer v1 contracts including `Deposit`, `WithdrawalCompleted`, `OperatorSharesDecreased`, and `OperatorSharesIncreased` events
- **Event parameters**: User addresses extracted from event parameters (`depositor`, `withdrawer`, `staker` fields)
- **Blockchain metadata**: Block timestamps and transaction hashes for temporal ordering and deduplication

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identifies and categorizes user addresses from EigenLayer protocol events
- **Logic**: Extracts user addresses from four types of EigenLayer events on Ethereum. `StrategyManager_Deposit_event` and `DelegationManager_OperatorSharesIncreased_event` identify users as "Depositor" type. `StrategyManager_WithdrawalCompleted_event` and `DelegationManager_OperatorSharesDecreased_event` identify users as "Withdrawer" type. Each interaction includes the block timestamp, transaction hash, and log index for precise identification
- **Key fields**: `user_address` (the interacting wallet), `user_type` (Depositor or Withdrawer), `block_timestamp`, `chain` (Ethereum), and protocol classification fields (`market_sector_id` and `business_segment_id` both set to "liquid_staking")

### Component B: Daily active user aggregation  
- **Purpose**: Creates daily distinct user counts using HyperLogLog for efficient approximate counting
- **Logic**: Groups user interactions by day using timestamp truncation to daily boundaries. For each day, creates HyperLogLog sketches of distinct user addresses, segmented by chain, user type, and product dimensions. Uses HLL initialization with precision parameter 15 for optimal accuracy-memory tradeoff
- **Key fields**: `timestamp` (daily boundary), `user_dau` (distinct user addresses), `chain_id`, `user_type`, `product_id`, and HLL sketch data structures

### Component C: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for monthly active user calculation
- **Logic**: Generates a complete datespine covering all days in the analysis period. For each target day, creates a 30-day lookback window by generating an array of the previous 29 days plus the target day. Joins this window with the daily HLL data to capture all user activity within each 30-day period
- **Key fields**: `timestamp` (target day), `joinday` (each day in the 30-day window), and associated HLL sketches for users active on each day

### Component D: Dimensional aggregations
- **Purpose**: Creates multiple views of monthly active users across different dimensions
- **Logic**: Merges HLL sketches across the 30-day window and extracts distinct counts for four dimensional breakdowns: project-level (all chains and user types combined), chain-level (by blockchain), user-type-level (Depositor vs Withdrawer), and product-level (by product classification). Uses HLL merge operations to combine sketches and extract final distinct counts
- **Key fields**: `dimensions` (breakdown type), `user_mau` (monthly distinct user count), and dimensional identifiers (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set with standardized schema, filling null values for non-applicable dimensions
- **Time aggregation**: Uses daily timestamp boundaries with monthly partitioning for efficient querying and 30-day rolling windows updated daily
- **Final calculation**: The monthly active user count represents the distinct number of user addresses that performed any EigenLayer interaction (deposit, withdrawal, delegation change) within the trailing 30-day period from each calculation date
- **Incremental processing**: Implements incremental updates with configurable lookback periods to handle late-arriving data and ensure data freshness

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

Unique revenue-generating users over a rolling 30-day window.

EigenCloudEIGEN

Active users (monthly)

Active users (monthly)

Competitive landscape