Celestia vs Algorand
Celestia vs Algorand Scalability
Real-time TPS
Celestia has no data, while Algorand TPS is 5.39 tx/s
Max TPS (100 blocks)
Celestia has no data, while Algorand max TPS is 5,716 tx/s
Max Theoretical TPS
Celestia has no data, while Algorand max theoretical TPS is 9,384 tx/s
Transaction Volume
Celestia has no data, while Algorand transaction volume is 19,403 txns
Block Time
Celestia has no data, while Algorand block time is 2.86s
Finality
Celestia has no data, while Algorand finality is 0s
Type
Celestia has no data, while Algorand is a layer 1 blockchain
Launch Date
Celestia has no data, while Algorand was launched on Jun 12, 2019
Celestia vs Algorand Decentralization
Nakamoto Coefficient
Celestia has no data, while Algorand Nakamoto Coefficient is 12
Validators/Miners
Celestia has no data, while Algorand has 1,869 validators
Stake/Hashrate
Celestia has no data, while Algorand stake is $455.2M
Consensus Mechanism
Celestia has no data, while Algorand is Pure Proof of Stake
Governance
Celestia has no data, while Algorand governance is on-chain
Celestia vs Algorand Developer Activity New
Developers
Celestia has no data, while Algorand has 536 developers
Repos
Celestia has no data, while Algorand has 160 repos
Commits
Celestia has no data, while Algorand has 35,349 commits
Stars
Celestia has no data, while Algorand has 5,063 stars
Watchers
Celestia has no data, while Algorand has 980 watchers
Other Comparisons
Celestia Comparisons
About Blockchains
About Celestia
Celestia is the modular blockchain powering unstoppable applications with full-stack customizability.
About Algorand
Algorand emerges as a blockchain platform committed to fostering transparency and enabling the growth of decentralized projects and applications. Operating as a public, decentralized blockchain, it leverages a Pure Proof-of-Stake (PPoS) consensus mechanism to uphold network security, efficiency, and decentralization. Powered by the Algorand Consensus Algorithm, the network employs a combination of cryptographic techniques and random selection to attain consensus, effectively addressing the constraints of traditional consensus mechanisms.