Google Cloud Run

Score8.7 out of 10

31 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

Self-Healing and Recovery
Product can be configured to automatically restart, replace, reschedule, kill, and validate jobs, containers, nodes, or clusters.
Category average: 8.3
Resource Allocation and Optimization
Product’s ability to balance resource requirements, availability needs, and workload intensity to optimize resource usage.
Category average: 8
Update Rollouts and Rollbacks
Product provides tools or functionality to deliver updates to containerized applications in ways that minimize the impact of errors, and revert updates that cause problems.
Category average: 7.9

Discovery Tools
Product provides methods (such as URIs or sortable lists) to easily find and access jobs, nodes, containers, or clusters.
Category average: 8.2
Cluster Management
Product’s ability to centralize the management of multiple container or node clusters.
Category average: 8.2
Storage Management
Product’s ability to allocate storage resources and manage both temporary and persistent data.
Category average: 8.2

#1 most frequent

Professional, Scientific, and Technical Services

28.0%

885 installations of 3,161

#2 most frequent

Information

19.4%

613 installations of 3,161

#3 most frequent

Finance and Insurance

10.2%

321 installations of 3,161

Francisnei Bernardes Lima View profile

Tech Lead in Information Technology at Zapt Tech (11-50 employees employees)

Use Cases and Deployment Scope

The main use is to host an image processing service. This involves generating large quantities of thumbnails, dividing large map images into tiles and extract geometries in floorplan PDF files. Using Cloud Run, we were able to reduce costs compared to using serverless solutions. It works very well for long processing times, with a 24-hour time limit per job. It's very practical and easy to deploy, eliminating the need for advanced DevOps knowledge.

Pros

Performing time-consuming tasks in the background
Hosting for high-demand HTTPS services
Hosting services packaged in Docker images

Cons

Limited deployment strategies. Traffic splitting exists, but it's basic. Rollback depends on manual intervention.
Fragile local debugging and development. The local environment never 100% mirrors Cloud Run. Debug removal is nonexistent.
Lack of more flexible non-HTTP jobs and workloads. No support for: Advanced Cron,
Communication between jobs

Return on Investment

Cost reduction of approximately 60% when using serverless tools.
Ease of service implementation and maintenance.
Ease of rollback, greatly reducing frustration with faulty updates.

Alternatives Considered

Google Kubernetes Engine

Other Software Used

Atlassian Jira, Google App Engine

Verified User

Administrative Assistant in Research & Development (11-50 employees employees)

Use Cases and Deployment Scope

Let's a small team ship faster with less operational overhead.

Pros

If you’re building anything that receives callbacks (Twilio WhatsApp, Razorpay/Stripe webhooks, lead forms, CRM triggers), Cloud Run is perfect. You deploy a small HTTP service, it scales up instantly during spikes, and stays cheap because it can scale to zero when nothing is happening.
Need daily/weekly tasks like generating reports, scraping, syncing databases, sending automated emails? Scheduler → Cloud Run is extremely reliable. It behaves like “serverless cron” but with full container flexibility.
You can lock down services so only your org / service accounts can access them. This is a big win for internal admin tools, dashboards, analytics APIs, etc., without needing VPN setups.

Cons

Cold starts and latency can be unpredictable, especially for heavier containers or services that need quick response times.
It’s not ideal for long-running workloads or persistent connections, so some use cases feel forced or limited.
Networking and private connectivity (VPC, internal services, DB access) can be more complex than expected and harder to debug.

Return on Investment

Scale-to-zero means we don’t pay for idle servers, which reduced hosting costs significantly for bursty workloads like webhooks and scheduled jobs.
Deployment is much faster (minutes vs days), so the team ships more features/services without spending time managing infrastructure.
Better reliability and safer rollbacks through revisions, plus stronger access control via IAM, reduced outages and security exposure.

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

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

Self-Healing and Recovery

Resource Allocation and Optimization

Update Rollouts and Rollbacks

Areas for Improvement

Discovery Tools

Cluster Management

Storage Management

Most Frequent Users

Professional, Scientific, and Technical Services

Information

Finance and Insurance

Reviews

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Use Cases and Deployment Scope

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Other Software Used

Usability

Use Cases and Deployment Scope

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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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