Google Cloud Run

Score9 out of 10

17 Reviews and Ratings

What is Google Cloud Run?

Google Cloud Run enables users to build and deploy scalable containerized apps written in any language (including Go, Python, Java, Node.js, .NET, and Ruby) on a fully managed platform. Cloud Run can be paired with other container ecosystem tools, including Google's Cloud Build, Cloud Code, Artifact Registry, and Docker. And it features out-of-the-box integration with Cloud Monitoring, Cloud Logging, Cloud Trace, and Error Reporting to ensure the health of an application.

Categories & Use Cases

Security and Isolation
Product provides effective tools to isolate containers, nodes, and clusters and protect them from threats.
Category average: 8.5
Container Orchestration
The product’s ability to automate networking, deployment, scaling, and other container management functions.
Category average: 8.2
Resource Allocation and Optimization
Product’s ability to balance resource requirements, availability needs, and workload intensity to optimize resource usage.
Category average: 7.8

Analytics, Monitoring, and Logging
Product provides tools to manage performance, health, and other data related to containers or clusters. This could include analytics tools, logging tools, live dashboards, etc.
Category average: 7.9
Cluster Management
Product’s ability to centralize the management of multiple container or node clusters.
Category average: 8.1
Storage Management
Product’s ability to allocate storage resources and manage both temporary and persistent data.
Category average: 8.2

Shivanand Labbi

Senior software engineer in Information Technology at Manhattan (501-1000 employees employees)

Use Cases and Deployment Scope

We use Google Cloud Run in our organization to deploy the majority of containerized applications into it without owning any infrastructure from our end, which is one of the biggest relief with regards to Google Cloud Run because it takes care of auto scaling, manages latency issues, along with good redundancy with very solid backend support. post Cloud Run major infra management has been reduced to core for our team also it gives lot of savings.

Pros

Auto scaling is the best one
provide direct VPC connectivity and rigid network
Cloud SQL and Pub/Sub services
Handling latency issues

Cons

More detailed documentaion we can expect, current looks bit complex
I would say its bit expensive so to run small application also we need to pay more
Migration part is bit complex need some impriovement over there
starting trouble it has I feel , feel slowness in start slowly it picks up.

Return on Investment

Lots of cost savings and less infra resources
Esay to use and manage and deployments also so good choice
The only negative part is that we will have zero idea of where the app is running, so we will have to be completely dependent on Google when it comes to the security part.

Alternatives Considered

Splunk AppDynamics, Docker, Google Kubernetes Engine, IBM WebSphere Hybrid Edition and IBM DataPower Gateway

Other Software Used

IBM DataPower Gateway, Docker, Splunk AppDynamics, PagerDuty, Google Cloud Platform

Usability

Vinaya Kumar Bendi View profile

Engineering Lead in Engineering at Kloudle (1-10 employees employees)

Use Cases and Deployment Scope

Most of our platform services are deployed as Google Cloud Run services.

Recently, we have also turned towards Google Cloud Run jobs for performing flow run tasks managed by a job server.

Being an early startup, using Google Cloud Run helps us to keep a tab on our cloud costs based on usage.

Pros

Auto scaling
Request concurrency
Customise memory and CPU resources

Cons

Increase request timeout beyond the current 1 hour limit on Google Cloud Run services
Support for troubleshooting unexpected Google Cloud Run container exits after running for several hours
Support configuration for calling of HTTP endpoints for pull based subscriptions

Return on Investment

Built in support for auto scaling helps reduce operational overhead
Any application performance issues can be addressed quickly by allocating more resources while a proper fix can be planned and rolled out later
Using Google Cloud Run enables development of microservices which provides granular control for scaling critical services in the platform

Alternatives Considered

AWS Lambda and DigitalOcean Droplets

Other Software Used

AWS Lambda, DigitalOcean Droplets, Google Compute Engine

Francois Salvert View profile

Web Analytics and Dashboarding senior consultant in Marketing at Dataligent Services Analytiques Inc. (1-10 employees employees)

Use Cases and Deployment Scope

We use Google Cloud Run to host Google Tag Manager server side scripts.

Pros

Easy to setup
Very cost efficient
Quick delivery and scalable options to maintain speedy deliveries if site ever needs it.

Cons

The documentation is not always clear and sometimes trial and error is needed
The menu system is not user friendly and inconsistent: sometimes it's tabs, sometimes menus and other configuration pages are long to scroll. Could (and will) be improved.

Return on Investment

It runs well for GTM server side which enables great marketing campaigns and re-targeting
The cost is negligible vs other solutions
Easy to maintain and upgrade

Alternatives Considered

Looker, Google BigQuery, Google Analytics and Google Tag Manager

Other Software Used

Google Tag Manager, Meta Pixel, Google Analytics

Verified User

Team Lead in Engineering (501-1000 employees employees)

Use Cases and Deployment Scope

We have been setting up some new micro services. As the load on them is not consistent throughout the day, we think that going serverless is the right decision. Also, we didn't want to deal with Kubernetes, at least at the moment. That makes Google Cloud Run our obvious choice.

Pros

Serverless micro service that only runs when there's a real demand
Serverless micro service that is very elastic, can scale up & down very quickly
Serverless micro service that integrates with other GCP services naturally

Cons

Like any other serverless FaaS, cold start is an issue
Unlike Lambda, simple code zip package is not supported, making the cold start issue worse
More complex pricing system than AWS Lambda

Return on Investment

As our workload is very light, & intermittent throughout the day, Google Cloud Run is much more cost effective than running a full always-on container or VM
Using Docker image as package delivery medium simplifies build process, & allows "build once run everywhere" pattern
Setting up micro services using Google Cloud Run with strictly private networking is not overly complicated

Alternatives Considered

AWS Lambda

Other Software Used

AWS Lambda, HashiCorp Terraform, Docker

Verified User

Director in Engineering (51-200 employees employees)

Use Cases and Deployment Scope

We're testing the execution of a container version for our Vistadash product. This allow us to estimate costs in a controlled environment, which is also production ready.

Pros

Observability Tools
DNS mapping
Revision management

Cons

Docker compose support
Tighter integration with Artifact Registry
Simplify steps to release a revision

Return on Investment

Accelerate testing times for Proof of Concepts
Enable cost estimations before release

Alternatives Considered

Amazon Elastic Container Service (Amazon ECS)

Other Software Used

Postman, Windsurf, 1Password

Red Hat OpenShift

Amazon Elastic Compute Cloud (EC2)

Kubernetes

Google Cloud Run

What is Google Cloud Run?

Categories & Use Cases

Key Features

Top Performing Features

Security and Isolation

Container Orchestration

Resource Allocation and Optimization

Areas for Improvement

Analytics, Monitoring, and Logging

Cluster Management

Storage Management

Reviews

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Usability

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Other Software Used

Use Cases and Deployment Scope

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Alternatives Considered

Other Software Used

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