1. Data Model: In Amazon Timestream, data is stored in a time series database optimized for time-ordered data, enabling efficient storage and querying of time-series data. MySQL, on the other hand, follows the relational data model, storing data in tables with rows and columns.

2. Scalability: Amazon Timestream is designed to handle large amounts of time-series data with automatic data retention management and built-in scaling capabilities. MySQL requires manual partitioning and sharding to manage scalability for large data sets.

3. Query Performance: Amazon Timestream offers accelerated query performance for time-series data due to its optimized storage and indexing for time-based queries. MySQL may experience slower query performance when dealing with large datasets, especially for time-based queries.

4. Data Ingestion: Amazon Timestream provides easy data ingestion capabilities for time-series data with integrations to popular IoT and monitoring services. MySQL may require additional data processing and transformation steps for efficient ingestion of time-series data.

5. Cost: Amazon Timestream offers a pay-as-you-go pricing model based on data ingestion, storage, and query processing, providing cost-effective solutions for time-series data management. MySQL typically requires upfront hardware and software costs, potentially leading to higher costs for managing time-series data at scale.

In Summary, Amazon Timestream and MySQL differ in their data models, scalability, query performance, data ingestion capabilities, and cost structures when it comes to managing time-series data in a database.