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Testimonials
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.
Nguyen is a champ. He's fast and has great communication. Well done!
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.
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.
We were impressed with their commitment to the project.
I was impressed at how quickly they were able to handle new tasks at a high quality and value.
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.
They have been great at adjusting and improving as we have worked together.
I was impressed with the amount of professionalism, communication, and speed of delivery.
They are very knowledgeable in their area of expertise.
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.
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❌
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💥
🔍
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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 standardized orchestration, scaling, and security controls. It is typically used by platform engineering and DevOps teams to host microservices, APIs, and background jobs while reducing the effort required to operate Kubernetes control plane components.
GKE runs on Google Cloud infrastructure and integrates with core services such as IAM, VPC networking, and logging/monitoring, making it well-suited for consistent dev/test/prod deployments and shared internal platforms. For related platform engineering services, see Platform Engineering.
- Provision and manage Kubernetes clusters with a managed control plane
- Deploy workloads using standard Kubernetes manifests and CI/CD pipelines
- Support autoscaling, rolling updates, and self-healing for resilient delivery
- Apply access and governance policies through Google Cloud IAM and network controls
- Centralize observability for operations and incident response
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
GCP GKE (Google Kubernetes Engine) is Google Cloud’s managed Kubernetes service, used to run containerized workloads with standardized operations, security controls, and tight integration with GCP networking and IAM.
- Managed control plane and upgrades reduce operational burden while keeping clusters aligned with supported Kubernetes versions.
- Autopilot mode can offload node management and right-size resources, improving baseline reliability and cost efficiency for many workloads.
- Strong identity and access patterns with Workload Identity help avoid long-lived service account keys and simplify least-privilege access to GCP APIs.
- Private clusters and VPC-native networking enable tighter network isolation and predictable IP management for enterprise environments.
- Integrated ingress and load balancing supports global, highly available traffic management using Google Cloud’s load balancer stack.
- Built-in autoscaling options (HPA/VPA and cluster autoscaler) help handle variable demand while controlling resource waste.
- Policy and security tooling (such as Binary Authorization and policy enforcement) supports supply chain controls and consistent governance.
- Observability integration with Google Cloud Operations Suite provides centralized metrics, logs, and alerting for platform and workload health.
- Multi-cluster and hybrid options via Anthos support consistent fleet management across regions and, when needed, across environments.
- Terraform-friendly APIs and mature ecosystem make it practical to standardize cluster builds, add-ons, and day-2 operations through infrastructure as code.
GKE is a strong fit when Kubernetes is the standard runtime and teams want a secure, repeatable platform with minimal control plane management. Trade-offs include added complexity compared to simpler compute options, and the need for disciplined governance around networking, quotas, and cost allocation as clusters and namespaces scale.
Common alternatives include Amazon EKS, Azure AKS, and self-managed Kubernetes, with OpenShift also used in more opinionated enterprise platform setups.
Our experience with GCP GKE helped us develop repeatable delivery patterns, automation, and operational runbooks for building and running secure, scalable Kubernetes platforms on Google Cloud, with practical guardrails for reliability, governance, and cost control.
Some of the things we did include:
- Designed and implemented production-grade GKE clusters (regional and zonal) with private nodes, opinionated VPC networking, and clear separation across dev/test/prod environments.
- Provisioned GKE and supporting GCP services using infrastructure-as-code with Terraform, including reusable modules, policy-aligned defaults, and automated drift detection.
- Established GitOps delivery workflows for GKE with Argo CD, covering multi-team tenancy patterns, environment promotion, and safe rollback strategies.
- Standardized CI/CD pipelines for container build and deploy with GitHub Actions, adding image scanning, signed artifacts, and release traceability.
- Implemented security baselines using Workload Identity, least-privilege RBAC, network policies, and admission controls aligned to internal governance requirements.
- Hardened ingress and service-to-service traffic using Istio for mTLS, traffic shifting, and progressive delivery in multi-service platforms.
- Set up cluster and workload observability with Prometheus and Grafana, including SLO dashboards, alert routing, and actionable on-call runbooks.
- Migrated workloads from self-managed Kubernetes and legacy compute to GKE, refactoring manifests, validating runtime behavior under load, and tuning requests/limits for stability.
- Improved reliability with autoscaling (HPA/VPA where appropriate), 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 cluster autoscaler behavior, consolidating node pools, applying labeling standards for chargeback, and reducing waste through right-sizing.
This experience helped us accumulate significant knowledge across multiple GKE use-cases—from greenfield platform builds to migrations and ongoing 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:
- Review your current GKE and Google Cloud setup and deliver a findings report with prioritized remediation actions.
- Define an adoption roadmap covering cluster strategy (Standard vs Autopilot), environments, networking, identity, and operating model.
- Design and implement production-ready GKE clusters with opinionated defaults for reliability, scalability, and maintainability.
- Automate cluster provisioning and day-2 operations using Terraform, repeatable modules, and GitOps workflows.
- Build and harden CI/CD for containerized workloads, including artifact promotion, progressive delivery, and rollback patterns.
- Implement security and compliance guardrails such as least-privilege IAM, Workload Identity, policy enforcement, secrets management, and network controls.
- Establish observability and reliability practices with metrics/logs/traces, SLOs and alerting, runbooks, and incident response.
- Optimize cost and performance through right-sizing, autoscaling, node pool strategy, resource quotas, and governance.
- Migrate and modernize applications onto GKE with phased cutovers, minimal-downtime plans, and containerization guidance.
- Enable your teams with hands-on training and operational playbooks aligned to Google Kubernetes Engine best practices.
