Episode 44 — Deploying Containers with GKE and Cloud Run
Welcome to Episode 44, Deploying Containers with G K E and Cloud Run. In this episode, we explore two of Google Cloud’s most powerful platforms for running containerized applications. Both are designed to simplify deployment, scaling, and management, but they take different paths toward that goal. Cloud Run emphasizes simplicity through a fully managed experience, while Google Kubernetes Engine, or G K E, provides deep control and customization. Choosing between them is about balancing flexibility against operational effort. Containers have become the standard unit of software delivery, so understanding these two environments will help you make informed choices about how to deploy them effectively and efficiently in your organization.
Cloud Run represents the easiest entry point into container-based computing on Google Cloud. It abstracts away all infrastructure, letting you deploy a container image and have it run automatically with scaling, logging, and security built in. Developers do not need to manage clusters or nodes; the platform handles everything from request routing to load balancing. It scales from zero to many instances based on demand, charging only for compute time used. For example, a startup might deploy an A P I that experiences unpredictable traffic without worrying about idle costs. Cloud Run’s simplicity encourages experimentation, rapid iteration, and minimal maintenance, making it ideal for teams that prioritize agility over granular configuration control.
G K E, or Google Kubernetes Engine, offers a more hands-on approach by providing a managed Kubernetes control plane. It gives developers full access to Kubernetes features, including namespaces, pods, services, and advanced scheduling. This flexibility is valuable for complex or large-scale systems that need custom networking, specific resource limits, or integration with specialized workloads. For example, a financial institution running multiple microservices might prefer G K E to fine-tune performance and compliance settings. However, with this control comes more responsibility—teams must manage upgrades, node health, and resource optimization. G K E is powerful, but it requires operational maturity and a good understanding of Kubernetes concepts to use effectively.
The choice between Cloud Run and G K E often comes down to how much control you need. Cloud Run is best for teams that want a simple, serverless-like experience where infrastructure fades into the background. G K E suits teams that require advanced configurations, custom networking, or long-running workloads. It’s not uncommon for organizations to use both: Cloud Run for lightweight services that scale independently and G K E for core systems needing persistent coordination. Evaluating your team’s skills, automation capabilities, and governance requirements helps determine the right balance. The goal is to reduce operational friction while maintaining the control level that your business demands.
Building and deploying containers efficiently depends on a reliable pipeline. Google Cloud provides Artifact Registry for storing container images securely, integrated with Cloud Build for automated builds and testing. This pipeline ensures that images are versioned, scanned, and ready for deployment. For instance, after a developer pushes code to a repository, Cloud Build can automatically compile the application, create a container image, and store it in the registry. From there, it can be deployed directly to Cloud Run or G K E. This automation eliminates manual steps and ensures consistency across environments. A well-designed pipeline is the foundation of continuous delivery and operational reliability.
Container best practices play a critical role in performance and security. Images should be kept small by including only necessary dependencies. This speeds up deployment and reduces attack surfaces. Security begins with using trusted base images and regularly updating them. Containers should also be immutable, meaning that once deployed, their content does not change. Instead, new versions are built and redeployed. This immutability ensures repeatable environments and simplifies troubleshooting. For example, if an issue arises, rolling back simply means redeploying the previous image. Following these principles keeps your container ecosystem clean, efficient, and predictable.
Deployment patterns such as rolling, blue-green, and canary releases define how new versions are introduced without disrupting users. Rolling updates replace instances gradually, minimizing downtime. Blue-green deployments maintain two environments, switching traffic once the new version is verified. Canary releases send a small percentage of traffic to the new version before full rollout, allowing safe validation. For example, a retailer might test new checkout logic with only five percent of users to detect issues early. Both Cloud Run and G K E support these strategies, though G K E provides more control for complex rollouts. Understanding these deployment patterns ensures smoother updates and better user experiences.
Autoscaling is one of the main advantages of cloud-native platforms. Cloud Run uses the Knative Pod Autoscaler, or K P A, to adjust container instances based on concurrent requests. It can scale down to zero during inactivity, saving costs. G K E, by contrast, uses the Horizontal Pod Autoscaler, or H P A, which responds to CPU or memory metrics. For example, a data analytics service might scale pods up during heavy computations and back down afterward. Combining autoscaling with proper resource requests and limits prevents overconsumption and ensures responsiveness. Mastering these scaling behaviors helps applications remain both performant and cost-effective under variable load conditions.
Networking in container environments requires careful planning to maintain reliability and security. Both Cloud Run and G K E integrate with load balancers to distribute traffic efficiently. In G K E, services and ingresses define routing rules, while Cloud Run simplifies this by automatically managing endpoints. For advanced environments, service mesh tools like Anthos Service Mesh or Istio provide observability, traffic shaping, and policy enforcement. For instance, they can control which services communicate and track latency between them. Networking choices affect performance and visibility, so understanding ingress, egress, and internal routing is essential for designing scalable container systems.
Managing secrets and configuration is a core operational concern. Google Cloud offers Secret Manager for sensitive information like A P I keys, and ConfigMaps for non-secret configurations in G K E. Both ensure that applications remain flexible and secure without embedding credentials in code or images. A containerized application can retrieve its configuration at runtime, allowing updates without rebuilding the image. For example, an application might fetch database credentials from Secret Manager when it starts, ensuring they remain protected. Separating secrets from images supports compliance and simplifies rotation, aligning with best practices in secure software delivery.
Observability ensures that developers and operators can understand system behavior in real time. Google Cloud integrates logging, metrics, and tracing across both Cloud Run and G K E. Cloud Monitoring collects performance data, Cloud Logging stores application logs, and Cloud Trace visualizes request paths. These tools help diagnose latency issues, detect errors, and measure user experience. For example, a sudden increase in response time can be traced to a specific service or database call. By combining metrics and traces, teams can maintain confidence in large, distributed systems. Observability transforms troubleshooting from guesswork into actionable insight.
Security in container deployments requires consistent enforcement across the stack. Policies can restrict image sources, enforce runtime protections, and apply identity-based access controls. Image scanning detects known vulnerabilities before deployment, while service accounts ensure least-privilege access to other Google Cloud services. In G K E, Binary Authorization can require image verification signatures. Cloud Run automatically isolates workloads and integrates with Identity and Access Management for secure access. Implementing these controls prevents accidental exposures and strengthens compliance. The goal is layered defense, where every part of the pipeline contributes to reducing risk.
Cost management differs between Cloud Run and G K E due to their architectures. Cloud Run charges based on request volume, CPU, and memory used per second, scaling costs directly with demand. G K E, however, incurs costs for node pools, even when workloads are idle, unless using autopilot mode. Reservations and committed use discounts can optimize long-term expenses for both. For example, a predictable enterprise workload might use commitments for stability, while an unpredictable startup service benefits from Cloud Run’s per-request model. Understanding these differences ensures spending aligns with workload patterns and operational goals.
Minimizing operational burden is the guiding principle when choosing between Cloud Run and G K E. Cloud Run offers effortless deployment and scaling, while G K E provides the flexibility needed for complex architectures. Many teams find success using both, starting with Cloud Run to prototype and moving to G K E as systems mature. The goal is not to pick one permanently but to use each where it fits best. By understanding the trade-offs in control, cost, and complexity, organizations can build resilient, efficient, and manageable containerized applications that evolve gracefully over time.
