1. Data Storage: AtScale is predominantly built on relational databases, enabling users to leverage existing data infrastructure, while Pilosa is designed to handle large volumes of data across distributed systems, providing scalability and high performance for complex queries.

2. Querying Speed: AtScale focuses on optimizing query performance for traditional SQL workloads, enhancing query speed and efficiency within the context of relational databases. In contrast, Pilosa is specifically engineered for high-speed querying at scale, utilizing innovative indexing techniques to deliver rapid response times for analytical queries.

3. Indexing Approach: AtScale utilizes traditional indexing methods such as bitmap indexing to enhance query performance and accelerate data retrieval from underlying databases. Pilosa, on the other hand, employs a unique sparse inverted indexing strategy, optimizing data representation and accessibility for rapid query processing across distributed datasets.

4. Distributed Computing: AtScale provides features for distributed data processing and querying within relational databases, facilitating parallel execution and scalability for analytical workloads. Pilosa, however, is architected as a distributed system by design, enabling seamless horizontal scaling and efficient data distribution for large-scale analytics.

5. Compatibility: AtScale is closely integrated with leading BI tools and SQL interfaces, enabling seamless compatibility and interoperability with various analytics and visualization platforms. In contrast, Pilosa offers a RESTful API interface for querying data, allowing users to interact with the system programmatically and integrate with custom applications or services.

6. Data Model Flexibility: AtScale focuses on providing a virtual data model abstraction layer to simplify query writing and data access for end-users, abstracting underlying complexities of the data storage systems. In contrast, Pilosa adopts a flexible schema-less data model approach, allowing users to ingest and query data without predefined structures or schemas, offering greater adaptability to evolving data requirements.

In Summary, the key differences between AtScale and Pilosa lie in their underlying data storage strategies, querying speed, indexing approaches, distributed computing capabilities, compatibility with external tools, and data model flexibility.