Stakers deposit funds into Jito and receive a return on investment in the form of staking rewards (supply-side revenue). Note: fee, revenue and supply-side fees currently only include validator tips.

Jito is a non-custodial liquid staking protocol built on Solana. Jito was founded by Zano Sherwani and Lucas Bruder and launched in 2022.

Jito team and investors own and govern the protocol. They currently don’t take a cut of the total return on investment (revenue).

github

twitter

discord

website

Liquid staking

Jito

# Jito Active users (weekly) Methodology

## 1. Overview

Jito Weekly Active Users (WAU) measures the number of unique users who interacted with Jito's services within a rolling 7-day window. This metric captures user engagement across two main Jito products: liquid staking (JitoSOL deposits and withdrawals) and MEV tipping services. The calculation uses HyperLogLog (HLL) approximation for efficient counting of unique users across time windows and provides breakdowns by project, chain, user type, and product dimensions.

## 2. Data sources

- **Solana native transfers**: Native SOL transfer events on the Solana blockchain, specifically transfers to Jito fee collection accounts
- **Solana token mints**: JitoSOL token mint events representing liquid staking deposits
- **Solana token burns**: JitoSOL token burn events representing liquid staking withdrawals  
- **Solana transaction records**: Transaction metadata including transaction signers to identify user addresses
- **Jito fee accounts**: Predefined list of 8 Solana addresses that collect MEV tips and fees

## 3. Step-by-step calculation

### Component A: Liquid staking user identification
- **Purpose**: Identifies users who deposit or withdraw from Jito's liquid staking service
- **Logic**: Monitors token mint and burn events for the JitoSOL token address `J1toso1uCk3RLmjorhTtrVwY9HJ7X8V9yYac6Y7kGCPn`. Token mints represent deposits and are classified as 'Depositor' user type, while token burns represent withdrawals and are classified as 'Withdrawer' user type. Transaction signers are identified as the users performing these actions, excluding transactions signed by Jito fee accounts to avoid counting internal operations
- **Key fields**: `block_timestamp` for timing, `transaction_signer` as user identifier, `product_id` set to 'liquidstaking', and `user_type` indicating 'Depositor' or 'Withdrawer'

### Component B: MEV tipping user identification  
- **Purpose**: Identifies users who pay MEV tips to Jito validators
- **Logic**: Monitors native SOL transfers to 8 predefined Jito fee collection account addresses. These transfers represent MEV tips paid by users to prioritize their transactions. Transaction signers are identified as 'Tipper' user type, excluding transfers from fee accounts themselves to avoid internal transfers
- **Key fields**: `block_timestamp` for timing, `signer` as user identifier, `product_id` set to 'mevtips', and `user_type` set to 'Tipper'

### Component C: Daily active user aggregation
- **Purpose**: Combines liquid staking and MEV tipping users into daily unique user counts
- **Logic**: Unions the two user identification components and aggregates by truncating timestamps to daily boundaries. Creates distinct user counts per day grouped by chain, user type, and product. Applies HyperLogLog initialization to prepare for efficient weekly aggregation
- **Key fields**: `timestamp` truncated to day, `chain` set to 'solana', `user_type`, `product_id`, and `user_dau` containing unique user addresses

### Component D: Seven-day rolling window creation
- **Purpose**: Creates a sliding 7-day window for each date to calculate weekly active users
- **Logic**: Uses a datespine approach generating all dates in the analysis period, then for each date creates joins with the 6 preceding days using `generate_timestamp_array`. This ensures complete coverage even for dates with no activity. Joins the date combinations with daily HLL user counts to create a comprehensive window dataset
- **Key fields**: `timestamp` for the target date, `joinday` for dates within the 7-day window, and HLL sketches containing daily user counts

### Final aggregation
- **Combining components**: For each 7-day window, merges HLL sketches using `hll_count.merge_partial` to combine daily unique user counts, then extracts the final unique count using `hll_count.extract`
- **Currency conversion**: Not applicable as this is a user count metric
- **Time aggregation**: Daily boundaries with 7-day rolling windows, where each day represents users active in that day plus the 6 preceding days
- **Final calculation**: Creates four dimensional breakdowns - project level (all users combined), chain level (grouped by blockchain), user type level (Depositor/Withdrawer/Tipper), and product level (liquidstaking/mevtips). The `finalize_metric_user_wau` macro standardizes the schema and fills missing days with zero values to ensure complete time series data

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

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

JitoJTO

Active users (weekly)

Active users (weekly)

Competitive landscape