Anthos is Google Cloud’s platform for modernizing and running cloud-native applications across cloud and on-premises environments. It gives development and operations teams a common Kubernetes-based foundation instead of requiring a separate deployment and management approach for each location. A deployment can span Google Cloud, other public clouds, and on-premises infrastructure, or remain in a single environment. section 1.1 section 3.1.3 section 10.1
The platform separates the workload runtime from the services used to govern and observe it:
- Anthos GKE provides a consistent Kubernetes runtime for containerized workloads on-premises or in public clouds. section 16.2
- Anthos Config Management centrally manages configuration and policies as code, helping keep clusters consistent. section 1.2 section 16.4
- Anthos Service Mesh supports secure communication between services and centralized observability. Cloud Logging and Cloud Monitoring support monitoring, auditing, and troubleshooting. section 16.2
- The Anthos console provides a consolidated cluster view; command-line tools remain available for operating deployments. Binary Authorization can require trusted image signatures before containers are scheduled. section 3.1.3 section 5.7 section 13.4.2
Choose locations to meet application requirements, not simply to use every available cloud. Data locality, resource limits, intermittent connectivity, low latency, and regional or provider outages can all shape a hybrid design. A common deployment pattern reduces differences in scaling, security, and development across locations, but high availability still requires planning for the failures the application must survive. Operating multiple providers without common tooling can otherwise multiply version, identity, and support differences. section 7.0 section 7.1 section 3.1.3
An edge workload
In a smart-retail design, store cameras send checkout-line video over 5G to an Anthos bare-metal cluster at a telecom edge location. A containerized inference application counts people in line and sends an alert when a threshold is crossed. Latency-sensitive inference stays at the edge; metadata travels to Google Cloud for later analysis and model training. This placement also reflects a broader edge requirement: applications may need to run close to users without depending on a continuous cloud connection. section 8.3.3 section 7.5
Anthos also supports incremental modernization. Teams can migrate Windows or Linux virtual machines into containers and deploy them to a compatible cluster, rather than begin with a complete rewrite. They can then use consistent runtimes, automation, security, and observability while refactoring applications over time. The platform does not remove every physical maintenance task: hardware updates and core networking changes still need attention. section 2.4.6 section 16.6 section 16.4
