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Testimonials
They have been great at adjusting and improving as we have worked together.
Nguyen is a champ. He's fast and has great communication. Well done!
Good consultants execute on task and deliver as planned. Better consultants overdeliver on their tasks. Great consultants become full technology partners and provide expertise beyond their scope.
I am happy to call MeteorOps my technology partners as they overdelivered, provide high-level expertise and I recommend their services as a very happy customer.
I was impressed at how quickly they were able to handle new tasks at a high quality and value.
We were impressed with their commitment to the project.
Thanks to MeteorOps, infrastructure changes have been completed without any errors. They provide excellent ideas, manage tasks efficiently, and deliver on time. They communicate through virtual meetings, email, and a messaging app. Overall, their experience in Kubernetes and AWS is impressive.
I was impressed with the amount of professionalism, communication, and speed of delivery.
Working with MeteorOps was exactly the solution we looked for. We met a professional, involved, problem solving DevOps team, that gave us an impact in a short term period.
You guys are really a bunch of talented geniuses and it's a pleasure and a privilege to work with you.
They are very knowledgeable in their area of expertise.
We got to meet Michael from MeteorOps through one of our employees. We needed DevOps help and guidance and Michael and the team provided all of it from the very beginning. They did everything from dev support to infrastructure design and configuration to helping during Production incidents like any one of our own employees. They actually became an integral part of our organization which says a lot about their personal attitude and dedication.
From my experience, working with MeteorOps brings high value to any company at almost any stage. They are uncompromising professionals, who achieve their goal no matter what.
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Free Project Planning: We dive into your goals and current state to prepare before a kickoff.
Pay-as-you-go: Use our capacity when you need it, none of that retainer nonsense.
Experts On-Demand: Get new experts from our team when you need specific knowledge or consultation.
We Don't Sleep: Just kidding we do sleep, but we can flexibly hop on calls when you need.
Ad-hoc Calls: When a video call works better than a chat, we hop on a call together.
How it works?
Google Cloud Run, a flagship offering from Google Cloud Platform (GCP), is a serverless solution designed to effortlessly scale and run containerized applications in the cloud. By abstracting infrastructure management, it empowers developers to focus purely on code, deploying it within Docker containers that respond to HTTP requests. This fully managed platform seamlessly integrates with GCP's vast suite of services and is built upon the open-source Knative project, ensuring flexibility, portability, and optimal performance. Leveraging Cloud Run guarantees a balance between efficient resource usage and robust application responsiveness, epitomizing the fusion of containerization and serverless paradigms.
Orchestration systems decide where and when workloads run on a cluster of machines (physical or virtual). On top of that, orchestration systems usually help manage the lifecycle of the workloads running on them. Nowadays, these systems are usually used to orchestrate containers, with the most popular one being Kubernetes.
There are many advantages to using Orchestration tools:
- Improve the utilization of CPU, memory, and storage usage by running many processes on a single machine
- Manage the entire lifecycle of the orchestrated workloads: pre & post initialization & termination
- Control the scale of workloads and the scale of their underlying infrastructure separately
- Centralized management of workloads and infrastructure
- Serverless Execution: Eliminates infrastructure management tasks, allowing developers to focus solely on writing and deploying code.
- Container-Based: Leverages Docker containers, providing flexibility to run any language, library, or binary.
- Automatic Scaling: Scales up or down with incoming requests, ensuring optimal resource usage without manual intervention.
- Pay-as-you-Go Pricing: Cost-effective as you only pay for the compute time you consume, with a generous free tier available.
- Integrated Developer Experience: Seamlessly integrates with Google Cloud's developer tools like Cloud Build and Source Repositories.
- Fast Deployments: Offers quick rollouts and continuous deployment capabilities, essential for agile development cycles.
- Event-Driven: Can be triggered by various events from Pub/Sub messages to Firestore changes, making it suitable for microservices and data-driven applications.
- Fully Managed Security: Automatic end-to-end encryption, ensuring data integrity and security throughout the application lifecycle.
- Hybrid & Multi-cloud Deployment: With Knative under the hood, it supports both on-premises and multi-cloud environments, granting deployment versatility.
- Integrated Monitoring & Logging: Native integration with Google Cloud Monitoring and Logging, providing deep insights into application performance and health.
Our experience with GCP CloudRun helped us build repeatable delivery patterns, runbooks, and automation that make it easier for teams to ship stateless container workloads on GCP with predictable scaling, security, and cost control.
Some of the things we did include:
- Containerized legacy services and migrated them to GCP CloudRun with minimal downtime, including traffic splitting and rollback strategies.
- Implemented CI/CD pipelines to build, scan, and deploy images to Cloud Run with environment promotion and policy checks.
- Designed secure service-to-service communication using IAM, service accounts, and least-privilege access to GCP APIs and data services.
- Set up observability for Cloud Run services with structured logging, metrics, and alerting, and integrated traces into existing incident workflows.
- Connected Cloud Run to private resources via VPC connectors, enabling access to internal endpoints and managed databases without exposing services publicly.
- Implemented asynchronous and event-driven patterns using Pub/Sub triggers and HTTP endpoints for background processing and bursty workloads.
- Standardized configuration and secrets handling across environments, including secret rotation and deployment-time validation.
- Defined autoscaling and concurrency settings based on load testing results, then tuned CPU/memory allocations to reduce latency and control spend.
- Established infrastructure-as-code for repeatable Cloud Run setups and environment provisioning, aligned with platform guardrails and audit requirements.
- Integrated Cloud Run services with GKE workloads for hybrid architectures, including shared identity and consistent deployment conventions.
This hands-on delivery work helped us accumulate significant knowledge across multiple Cloud Run use-cases—from simple APIs to event-driven services—and enables us to deliver high-quality GCP CloudRun setups that are secure, observable, and easy to operate at scale. For deeper platform context, we also align implementations with official guidance from Google Cloud Run documentation.
Some of the things we can help you do with GCP CloudRun include:
- Assess your current container workloads and produce a Cloud Run readiness review with risks, recommendations, and a prioritized remediation report.
- Define an adoption roadmap for Cloud Run, including workload selection, environment strategy, and operating model for teams and platforms.
- Implement production-grade Cloud Run services with reliable deployments, traffic splitting, and rollbacks integrated into CI/CD pipelines.
- Automate provisioning and configuration using infrastructure as code (Terraform) and policy guardrails for consistent, repeatable environments.
- Harden security with least-privilege IAM, service-to-service authentication, secrets management, and network controls aligned to compliance needs.
- Improve reliability and operability with structured logging, metrics, tracing, and actionable alerting using Google Cloud Observability best practices.
- Optimize cost and performance by tuning concurrency, CPU/memory sizing, min/max instances, and request timeouts based on real traffic patterns.
- Design resilient integrations with Google Cloud services (Pub/Sub, Cloud SQL, Storage) and implement safe connectivity patterns for private resources.
- Troubleshoot production issues quickly (cold starts, latency spikes, 5xx errors) and implement runbooks and SLOs for ongoing operations.
- Enable your teams with hands-on training, reference architectures, and deployment templates to ship stateless container workloads faster.
