After working on multiple production deployments across Cloud Run and App Engine, I kept seeing the same issues surface during releases — not because Cloud Build was misconfigured, but because the runtime model didn’t align with how the application was built. This article captures those practical differences from an engineering perspective, not a feature comparison.
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Cloud Build + Cloud Run vs Cloud Build + App Engine: Choosing the Right Deployment Model on Google Cloud
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Modern applications don’t just need to run correctly — they need to deploy reliably, repeatedly, and predictably.
On Google Cloud, Cloud Build often forms the backbone of CI/CD pipelines. However, the real difference in outcomes usually comes not from Cloud Build itself, but from where the application is deployed.
Two common and entirely valid deployment patterns are:
Cloud Build + Cloud Run
Cloud Build + App Engine
Teams rarely struggle because one of these choices is “wrong.”
More often, problems arise when the deployment model doesn’t match how the application is built or expected to evolve.
This article isn’t about declaring a winner.
It’s about understanding how these two approaches differ in practice — and when each one makes sense.
How Cloud Build + Cloud Run Usually Works
Cloud Run is a fully managed service for running containerized applications, with built-in autoscaling, including the ability to scale to zero when idle.
A typical CI/CD flow looks like this:
Code is committed to a Git repository
Cloud Build is triggered automatically
A container image is built
The image is pushed to Artifact Registry
Cloud Run deploys a new revision
From a CI/CD perspective, everything revolves around the container image.
What teams typically appreciate about this approach:
Deployment cycles are fast and predictable once image builds are optimized
Any language or framework works, as long as it runs in a container
Applications can scale to zero when idle, reducing cost for spiky or low-traffic workloads
Each deployment produces a clear, versioned revision with built-in traffic splitting
This model fits naturally when systems are already designed around services, containers, and environments.
How Cloud Build + App Engine Usually Works
App Engine follows a different model. Instead of building containers, source code is deployed directly, as long as the runtime is supported (for example, specific versions of Node.js, Python, Java, or Go).
A common flow looks like this:
Code is committed
Cloud Build triggers
Source code is packaged
Cloud Build deploys directly to App Engine
App Engine manages runtime, scaling, and traffic routing
This approach often feels simpler, especially for teams that want to avoid managing Dockerfiles or container images.
What tends to work well here:
Minimal deployment configuration compared to container pipelines
No container lifecycle management — the runtime, OS, and scaling are abstracted away
Built-in autoscaling and traffic splitting for typical web workloads
A familiar model for monolithic or long-running applications
For teams already invested in App Engine, this simplicity can be a real advantage.
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Where the Two Approaches Really Differ
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The most meaningful differences are not about Cloud Build — they are about the runtime and deployment model.
Container vs Source
Cloud Run deploys container images with full control over the runtime
App Engine deploys source code for supported runtimes, with more behavior managed by the platform
Flexibility
Cloud Run can run anything that fits in a container, including APIs, background workers, and AI workloads
App Engine is more opinionated, supporting a defined set of languages and environments
Scaling Behavior
Cloud Run scales to zero by default when there is no traffic (with optional minimum instances)
App Engine also scales automatically, but often keeps at least one instance warm depending on configuration
Cost Profile
Cloud Run follows a pay-per-use model based on CPU, memory, and request time
App Engine costs are more closely tied to instance uptime and instance class
Release Control
Both platforms support traffic splitting and gradual rollouts
Cloud Run’s image-based revisions make deployments feel like explicit, versioned artifacts
When Cloud Run Is Usually the Better Fit
Cloud Run is often a strong choice when:
Applications are built as microservices or multiple small services
Containers are already part of the workflow
Runtime flexibility is important (custom binaries, specific dependencies, mixed stacks)
Traffic patterns are unpredictable or spiky
Portability across environments matters
For many greenfield projects, Cloud Run naturally becomes the default.
When App Engine Still Makes Sense
App Engine remains a solid option when:
A straightforward deployment model is preferred for a supported runtime
Managing Dockerfiles and container images is not desirable
The application is more monolithic but fits App Engine’s scaling model
Existing App Engine workloads are being maintained without a strong reason to migrate
App Engine isn’t outdated — it’s opinionated.
That opinionated model can reduce complexity when the application and team fit within its boundaries.
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So Which One Should You Choose?
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Neither option is universally better.
A useful way to think about the decision is:
If flexibility, portability, and container-based workflows matter most, Cloud Run is usually the right fit
If simplicity, faster onboarding, and a managed runtime are the priority, App Engine can be the better choice
Cloud Build integrates cleanly with both, so CI/CD automation isn’t the real differentiator.
The real decision is architectural — shaped by the application’s structure, the team’s experience, and how the system is expected to evolve over time.
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