1. 1. Scalability and Performance: Elasticsearch is designed for horizontal scalability and can handle large amounts of data and traffic. It uses sharding and replication to distribute data across multiple nodes and ensure high availability. On the other hand, Loggly is a cloud-based log management service that does not offer the same scalability and performance as Elasticsearch. It relies on its own infrastructure to process and index logs, which may not be as efficient as Elasticsearch's distributed architecture.

2. 2. Full-Text Search and Analytics: Elasticsearch is primarily built for full-text search and analytics. It uses inverted indices to provide fast search capabilities across large volumes of structured and unstructured data. Loggly, on the other hand, focuses specifically on log management and analytics. It provides pre-built dashboards and visualizations tailored for log analysis, making it easier for users to monitor and troubleshoot their systems.

3. 3. Data Retention and Storage: Elasticsearch allows users to define data retention policies and manage the storage of their data through index lifecycle management (ILM). This gives users control over how long data is retained and how it is stored (e.g., in hot, warm, or cold storage). Loggly, on the other hand, provides a fixed retention period based on the user's subscription plan. Users have limited control over how long their logs are retained and cannot customize storage options.

4. 4. Log Collection Methods: Elasticsearch supports various log collection methods, including agents, plugins, and integrations with external systems. It can ingest logs from different sources and protocols, making it a versatile solution for log management. Loggly, on the other hand, provides a centralized log collection system that relies on log shipping agents. While it supports popular log shipping methods like Syslog and Logstash, it does not offer the same level of flexibility as Elasticsearch.

5. 5. Querying and Filtering: Elasticsearch provides a powerful query DSL (Domain-Specific Language) that allows users to perform complex searches and aggregations on their data. It supports filtering based on various criteria, such as time range, keyword matching, and numeric ranges. Loggly, on the other hand, offers a simplified query language that is focused on log analysis. It provides predefined search filters and allows users to perform basic search queries but lacks the advanced querying capabilities of Elasticsearch.

6. 6. Security and Access Control: Elasticsearch offers robust security features, including authentication, authorization, and encryption. It supports role-based access control (RBAC) and integrates with external authentication providers like Active Directory and LDAP. Loggly, on the other hand, provides basic security features like HTTPS encryption and IP whitelisting but does not offer the same level of fine-grained access control as Elasticsearch. It does not support RBAC or integration with external authentication providers.

In Summary, Elasticsearch and Loggly differ in terms of scalability and performance, full-text search and analytics capabilities, data retention and storage options, log collection methods, querying and filtering capabilities, and security and access control features.