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Google Cloud Run

Score8.7 out of 10

31 Reviews and Ratings

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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

Key Features

Learn about the best (and worst!) features Google Cloud Run has to offer, as determined by TrustRadius' reviewers.
Based on 31 ratings of Google Cloud Run's features
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Top Performing Features

  • 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

Areas for Improvement

  • Discovery Tools

    Product provides methods (such as URIs or sortable lists) to easily find and access jobs, nodes, containers, or clusters.

    Category average: 8.1

  • 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

Reviews

What users are saying about Google Cloud Run.
View all reviews

The Best Serverless app hosting platform.

Incentivized
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

Google Cloud Run is very easy to use.

Incentivized
Francisnei Bernardes LimaView 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

The easiest way to run containers in production without managing servers

Incentivized
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.

A solid production-ready container application manager

Incentivized
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

Google Cloud Run is optimal for running container at scale.

Incentivized
Verified User
Engineer in Information Technology (1-10 employees employees)

Use Cases and Deployment Scope

We use a Docker container that converts images to smaller sizes and makes map tiles with large images for indoor maps for navigation. Using just serverless functions was not enough because of the time limit. Making conversions with images takes running time that was only supported using Google Run, which increases the time limit to 24 hours.

Pros

  • Serverless service.
  • Run python scripts.
  • Manage Docker images.

Cons

  • Increase up time.

Return on Investment

  • Make the tasks to standardize the images of the application easier.
  • It is possible to create tile maps that are key to the application.

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