• Dynamic Routing: Route traffic based on various criteria such as request headers (e.g., user agent, version), paths, or weights. This is fundamental for canary releases, A/B testing, and blue/green deployments. For example, you can route 5% of traffic to a new version of a service.
• Load Balancing: Intelligent load balancing distributes requests across multiple instances of a service, with algorithms like round-robin, least connections, and consistent hashing, improving performance and availability.
• Retries and Timeouts: Automatically configure retries for failed requests and define service-specific timeouts, enhancing resilience against transient failures.
• Fault Injection: Deliberately inject failures (e.g., delays, HTTP errors) into requests to test the resilience of services and the overall system, a core practice of chaos engineering.
• Traffic Mirroring/Shadowing: Copy live traffic to a new version of a service or a debugging tool without impacting production users. This is useful for testing changes with real-world load.

The precise control over traffic flow is essential in modern distributed systems. The role of APIs in modern software often involves complex interactions that a service mesh can help manage effectively.

• Mutual TLS (mTLS): Provides strong identity and encrypts all traffic between services within the mesh by default. The mesh handles certificate issuance, distribution, and rotation, simplifying secure communication.
• Service Identity and Authentication: Assigns strong, verifiable identities to workloads (e.g., using SPIFFE/SPIRE standards), ensuring that services are who they claim to be.
• Authorization Policies: Define fine-grained access control rules specifying which services can communicate with each other, and what actions they are allowed to perform (e.g., allow GET requests from service A to service B on a specific path).
• Audit Logging: Securely log security-relevant events, such as policy violations or authentication failures, for auditing and compliance.

• Distributed Tracing: Generates and propagates trace information across service calls, allowing you to follow the path of a request as it traverses multiple services. This is invaluable for debugging latency issues and understanding service dependencies.
• Metrics Collection: Automatically collects a rich set of metrics (the "golden signals": latency, traffic, errors, saturation) for all inbound and outbound traffic for every service in the mesh. These metrics can be easily integrated with monitoring systems like Prometheus and Grafana.
• Logging: Standardizes and collects access logs for all service-to-service communication, providing a consistent format and making it easier to analyze traffic patterns.
• Service Topology Visualization: Many service meshes offer tools or integrations to visualize the relationships and dependencies between services, along with their health and performance metrics.

Achieving comprehensive visibility is a key goal; for more on this topic, explore Understanding Observability in Modern Systems.