1. Data Model: InfluxDB is a time-series database optimized for handling high write and query loads of time-stamped data points, making it ideal for IoT and monitoring use cases. MongoDB is a document-oriented database that stores data in flexible, JSON-like documents, allowing for a more complex and hierarchical data structure. PostgreSQL is a relational database management system that organizes data into tables with rows and columns, enabling powerful querying capabilities through SQL.

2. Query Language: InfluxDB uses its own query language InfluxQL, tailored for time-series data manipulation and aggregation. MongoDB uses a query syntax similar to JSON, providing a rich set of operators to query and manipulate documents. PostgreSQL utilizes SQL (Structured Query Language), a standardized language for managing relational databases, offering advanced features like joins, subqueries, and window functions.

3. Scalability: InfluxDB is designed to scale horizontally to handle large volumes of time-series data by clustering multiple nodes together. MongoDB can scale horizontally through sharding, distributing data across multiple servers to improve performance and capacity. PostgreSQL can also scale horizontally using techniques like table partitioning and replication, but it is primarily perceived as a powerful single-node database solution.

4. Consistency Model: InfluxDB follows a flexible consistency model, allowing users to choose between strong consistency or eventual consistency based on their application requirements. MongoDB offers tunable consistency levels, giving developers control over how strict data consistency should be maintained. PostgreSQL provides ACID compliance, ensuring strong consistency and reliability in transaction processing.

5. Data Replication: InfluxDB supports continuous queries and continuous data replication mechanisms to ensure data redundancy and high availability. MongoDB employs replica sets to replicate data across multiple servers, providing fault tolerance and automated failover. PostgreSQL utilizes streaming replication and logical replication methods to replicate data for backup, load balancing, and disaster recovery purposes.

6. Community Ecosystem: InfluxDB has a strong focus on time-series use cases, attracting a community of developers and users dedicated to optimizing performance for such workloads. MongoDB has a diverse community supporting a wide range of use cases, from mobile apps to large-scale enterprise applications. PostgreSQL has a mature and extensive community contributing to its development, ensuring a robust feature set and frequent updates.

In Summary, InfluxDB shines in handling time-series data efficiently, MongoDB excels in flexible document storage, and PostgreSQL offers robust relational database capabilities.