In the fast-paced world of early-stage company building, making the right choices in Google Cloud Run vs. GKE for application deployment is crucial. We’ve seen firsthand how selecting the wrong infrastructure from the start can cost startups valuable time and money. Many teams begin with Google Cloud Run due to its ease of use, only to later realize it lacks the customization needed for more complex applications. Below, we’ll explore a real-life example of a startup that transitioned from Cloud Run to GKE after facing these challenges.
Why Startups Choose Google Cloud Run
What is Cloud Run?
Google Cloud Run offers a streamlined way to deploy containers, combining the benefits of serverless computing with containerization. It enables startups to quickly deploy scalable, stateless HTTP containers without managing infrastructure. Cloud Run automatically scales based on traffic and can scale down to zero, ensuring you only pay for active usage.
Many startups choose Cloud Run vs. GKE initially for two key reasons:
- Cost-Effectiveness: With limited traffic or compute needs, you only pay for services when they are in use.
- Fully Managed NAtive Service: Cloud Run abstracts infrastructure management, similar to how Kapstan simplifies DevOps by automating deployment and scaling.
While Cloud Run offers advantages, it also has limitations that may require costly reconfiguration in the future.
GrowthFactor: A Real-World Example
One of our customers, GrowthFactor, initially chose Cloud Run due to its serverless nature and cost-effectiveness. However, as they built complex, data-rich applications, they encountered challenges that Cloud Run couldn't solve, particularly with task management.
Cloud Run’s Stateless Limitations
GrowthFactor required Celery to handle long-running, persistent tasks, but Cloud Run's stateless nature posed challenges:
- Stateful Workloads: Cloud Run automatically scales down to zero, making it unsuitable for persistent task queues like Celery that require constant connections to a message broker (e.g., Redis).
- Configuration Complexity: Workarounds were needed to simulate statefulness, increasing development time and reliability concerns.
- Fragmented Architecture: Many teams mix Cloud Run, Cloud Functions, and Compute Engine, leading to operational inefficiencies and slower development cycles.
Logs Management Challenges in Cloud Run
Effective logs management is essential for maintaining high service reliability, yet GrowthFactor encountered issues with Google Cloud Logging in Cloud Run:
- Log Delays: In high-traffic scenarios, logs didn't appear in real-time, making it difficult to diagnose issues promptly.
- Scalability Limitations: As their infrastructure grew, Cloud Run's logging inefficiencies led to delays and frustration.
The Cost of Transitioning from Cloud Run to GKE
We’ve seen many startups struggle with Cloud Run’s limitations before eventually migrating to GKE. These transitions often involve:
- Months of reconfiguration to migrate workloads.
- Increased engineering overhead to rebuild infrastructure.
- Operational slowdowns as teams shift focus from product development to fixing cloud architecture.
Why Kapstan First Chose to Support GKE
Google Kubernetes Engine (GKE) vs. Cloud Run: A Better Alternative
Spoiler alert: Deploying workloads to GKE via Kapstan is easier and faster than Cloud Run.
We chose to support GKE from the start to provide startups with:
- More control, customization, and flexibility.
- Seamless integration with third-party tools.
- None of the day-2 Kubernetes management overhead.
But What About GKE Costs?
A common concern when comparing Cloud Run vs. GKE is cost. Deploying two GKE clusters with Kapstan costs approximately $200 more per month than Cloud Run. However, for VC-backed startups with access to cloud credits, this added expense is minor compared to the ROI of using GKE.
ROI of GKE vs. Cloud Run
- Full Control & Customization: Tailor infrastructure configurations for specific workloads.
- Flexibility: Handle diverse workloads, multi-cloud architectures, and cloud migrations.
- Scalability: GKE scales seamlessly with increasing demand.
- Zero Tech Debt: Avoid fragmented architectures and the high migration costs of switching from Cloud Run later.
The True Cost: Time, Not Money
Managing infrastructure sprawl and day-2 Kubernetes operations can divert focus away from product development. For startups, time is the most valuable resource.
Enter Kapstan
Kapstan makes GKE as easy as Cloud Run—without the limitations:
- Instant provisioning: Deploy workloads faster than with Cloud Run.
- No DevOps expertise required: Engineers can spin up services, deploy, and monitor applications effortlessly.
- Built-in Kubernetes power: Scale seamlessly with no YAML files or Kubernetes headaches.
Conclusion: Google Cloud Run vs. GKE – Which Is Best for Your Startup?
If you’re an early-stage, VC-backed startup, and you:✅ Want a microservices and event-driven architecture,
✅ Need multi-cloud support to leverage cloud credits,
✅ Require advanced configuration/customization in the future,
✅ Want zero DevOps overhead so you can focus on your product,
Then Kapstan on GKE is the best choice.
Explore Kapstan today and see how it can simplify your AI/ML deployment strategy.
