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Testimonials
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.
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.
You guys are really a bunch of talented geniuses and it's a pleasure and a privilege to work with you.
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.
I was impressed at how quickly they were able to handle new tasks at a high quality and value.
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.
They are very knowledgeable in their area of expertise.
I was impressed with the amount of professionalism, communication, and speed of delivery.
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.
Nguyen is a champ. He's fast and has great communication. Well done!
We were impressed with their commitment to the project.
They have been great at adjusting and improving as we have worked together.
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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?
GCP GKE (Google Kubernetes Engine) is Google Cloud’s managed Kubernetes service for running containerized applications with automated cluster operations. It is commonly used by platform teams, DevOps engineers, and application teams to standardize deployments, improve reliability, and scale services without managing Kubernetes control plane components directly. GKE is typically adopted for microservices, API backends, and batch workloads where consistent rollout and rollback workflows are needed across environments.
GKE runs on Google Cloud infrastructure and integrates with surrounding GCP services for networking, identity, and observability, making it a practical foundation for cloud-native platforms and internal developer platforms. Learn more about related delivery patterns on DevOps consulting.
- Managed Kubernetes with automated upgrades, patching, and node lifecycle management
- Cluster security controls such as workload identity, policy enforcement, and private networking options
- Autoscaling for nodes and workloads to handle variable demand
- Support for multi-environment deployment workflows (dev/test/prod) and GitOps-style automation
- Built-in integration with Google Cloud logging, monitoring, and load balancing
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
- Managed Kubernetes - GKE offloads the burden of managing the Kubernetes control plane, automating updates, patching, and node management.
- Advanced Cluster Security - It boasts built-in security features, including private clusters, encrypted data at rest and in transit, and workload identity for resource access control.
- Continuous Integration and Deployment - Seamless integration with popular CI/CD tools and Google Cloud services ensures a streamlined deployment pipeline.
- Autoscaling - Both vertical and horizontal pod autoscaling is available, making it easy to adapt to varying loads and optimize costs.
- Multi-Cluster Management - Anthos integration allows for the unified management of clusters across multiple clouds and on-premises environments.
- Network Policies - Enables defining how pods communicate with each other, enhancing security and segmentation within applications.
- Monitoring and Logging - Native integration with Google Cloud's Operations Suite (formerly Stackdriver) provides real-time insights into cluster performance and health.
- Hybrid and Multi-cloud Deployments - With Anthos, GKE can be the central point for managing workloads across diverse environments.
- Google Cloud's Global Load Balancing - Deploy applications globally, distributing traffic to ensure low latency and high availability.
- Istio Integration - Facilitates service mesh deployment for enhanced security, telemetry, and network management.
Our experience with GCP GKE helped us build practical patterns, automation, and runbooks for delivering secure, scalable Kubernetes platforms on Google Cloud, with an emphasis on reliability, governance, and cost control.
Some of the things we did include:
- Designed and implemented production GKE clusters (regional and zonal) with opinionated networking, IAM, and environment separation for dev/test/prod.
- Built GitOps delivery workflows for GKE using Argo CD, including multi-tenant repo structures, promotion strategies, and drift detection.
- Standardized CI/CD pipelines for container builds and deployments with GitHub Actions, adding policy checks, image signing, and automated rollbacks.
- Implemented cluster security baselines: Workload Identity, least-privilege RBAC, private clusters, network policies, and admission controls aligned to internal governance requirements.
- Set up observability for GKE workloads with Prometheus and Grafana, including SLO dashboards, alert routing, and actionable runbooks.
- Hardened ingress and traffic management using Istio for mTLS, progressive delivery, and service-level routing in multi-service platforms.
- Migrated workloads from self-managed Kubernetes to GKE, refactoring manifests, right-sizing resources, and validating performance under realistic load.
- Improved reliability with HPA/VPA tuning, PodDisruptionBudgets, node pool strategies, and documented incident response procedures for common failure modes.
- Implemented backup and recovery approaches for stateful workloads, including restore testing and clear RPO/RTO expectations across environments.
- Optimized GKE costs by tuning autoscaling, consolidating node pools, setting resource requests/limits, and applying chargeback/labeling standards for finance visibility.
This experience helped us accumulate significant knowledge across multiple GKE use-cases—from greenfield platform builds to migrations and optimization—and enables us to deliver high-quality GCP GKE setups that teams can operate confidently over time.
Some of the things we can help you do with GCP GKE include:
- Assess your current Kubernetes and GKE setup and deliver a prioritized review report covering reliability, security, and operability gaps.
- Create a pragmatic GKE adoption roadmap, including cluster strategy, networking approach, and operating model for your teams.
- Design and implement production-ready GKE clusters (standard or Autopilot), including VPC networking, node pools, and workload isolation.
- Automate cluster and platform provisioning with Infrastructure as Code and GitOps workflows using Terraform.
- Implement secure-by-default guardrails: RBAC, Workload Identity, secrets handling, policy controls, and hardening aligned to compliance needs.
- Build CI/CD and deployment patterns for Kubernetes (progressive delivery, rollbacks, and environment promotion) to ship faster with less risk.
- Establish observability for clusters and workloads—metrics, logs, traces, SLOs, and alerting—so incidents are detected and resolved quickly.
- Optimize cost and performance with autoscaling, right-sizing, resource requests/limits, and workload scheduling strategies.
- Troubleshoot cluster and application issues (networking, DNS, ingress, upgrades, and performance) and provide actionable remediation runbooks.
- Enable your teams with hands-on training, operational playbooks, and support for ongoing upgrades, patching, and day-2 operations.
