1. Architecture and Purpose: Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. It is designed for building web applications, offering features such as ORM, admin panel, and authentication. InfluxDB, on the other hand, is a time-series database specifically built for handling high write and query loads of time-stamped data. It is optimized for time-series data and is commonly used in monitoring, IoT, and real-time analytics applications.

2. Data Model: Django provides a flexible Object-Relational Mapping (ORM) layer that allows developers to interact with the database using high-level python objects rather than SQL queries directly. InfluxDB, being a time-series database, has a specialized data model optimized for handling time-stamped data efficiently. It uses measurements, tags, fields, and timestamps to organize and query time-series data.

3. Query Language: Django primarily uses SQL for querying and manipulating data in relational databases. Developers can write raw SQL queries or use Django's ORM to interact with the database. In contrast, InfluxDB uses its own query language called InfluxQL, specifically tailored for querying time-series data. It provides functions and capabilities unique to time-series databases that enhance data manipulation and aggregation.

4. Scalability and Performance: Django is known for its flexibility and ease of use for developing web applications, but it may not be the best choice for handling massive amounts of time-series data due to its architecture. InfluxDB is designed to handle high write and query loads of time-stamped data efficiently, making it well-suited for scalable and performant time-series data storage and retrieval.

5. Integration and Ecosystem: Django has a vast ecosystem of plugins, libraries, and extensions that enhance its functionality and allow developers to extend its capabilities further. It integrates seamlessly with various third-party tools and services for building complex web applications. InfluxDB also offers integrations with popular monitoring and visualization tools like Grafana and Prometheus, making it easy to incorporate time-series data into monitoring and analytics workflows.

6. Monitoring and Alerting: InfluxDB has built-in support for monitoring and alerting, making it easier for developers and operations teams to set up alerts based on time-series data thresholds. Django, on the other hand, may require additional setup and integrations with monitoring tools to achieve similar monitoring and alerting functionalities for web applications.

In Summary, Django and InfluxDB differ in their architecture and purpose, data model, query language, scalability, integration ecosystem, and monitoring capabilities, making them suitable for different types of applications and use cases.