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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.
They are very knowledgeable in their area of expertise.
You guys are really a bunch of talented geniuses and it's a pleasure and a privilege to work with you.
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.
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.
We were impressed with their commitment to the project.
They have been great at adjusting and improving as we have worked together.
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 with the amount of professionalism, communication, and speed of delivery.
Nguyen is a champ. He's fast and has great communication. Well done!
I was impressed at how quickly they were able to handle new tasks at a high quality and value.
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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?
AWS ECS (Amazon Elastic Container Service) is a managed container orchestration service for running and scaling Docker-based applications on AWS. It is commonly used by platform and DevOps teams to standardize how services are deployed, updated, and operated across environments, especially for microservices and API backends. ECS reduces the need to manage an orchestration control plane while integrating with core AWS services for identity, networking, and observability.
ECS workloads run on Amazon EC2 or on AWS Fargate for serverless container compute, and are often paired with Amazon ECR for image storage and CI/CD pipelines for automated releases. For related platform patterns, see DevOps Engineering.
- Run containers on EC2 or Fargate using task definitions and services
- Rolling deployments and service autoscaling for long-running workloads
- Integration with VPC networking, IAM, and Elastic Load Balancing
- Secrets and configuration injection via AWS-managed integrations
- Operational visibility through CloudWatch logs, metrics, and events
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
AWS ECS (Amazon Elastic Container Service) is a managed container orchestration service for running and scaling Docker-based workloads on AWS. It is commonly used when teams want straightforward operations, strong AWS-native integration, and flexible compute choices.
- Managed control plane reduces the overhead of operating a container scheduler, including upgrades and core cluster components.
- Supports both EC2 and AWS Fargate launch types, enabling either instance-level control or serverless per-task compute.
- Integrates tightly with IAM, VPC, and security groups to implement least-privilege access and network isolation for services.
- Built-in load balancing integrations with Application Load Balancer and Network Load Balancer simplify service exposure and traffic routing.
- Service autoscaling and capacity features help match task counts to demand using CloudWatch metrics and scaling policies.
- Task definitions provide a versioned deployment unit for container images, resource limits, environment configuration, and runtime policies.
- Works naturally with Amazon ECR for image storage and scanning workflows, reducing friction in the container image lifecycle.
- Operational visibility via CloudWatch Logs, Container Insights, and OpenTelemetry-compatible instrumentation supports consistent observability patterns.
- Deployment controls such as rolling updates, health checks, and deployment circuit breakers reduce release risk and support automated rollback.
- Cost optimization options including Fargate Spot and EC2 Spot can reduce runtime spend for interruption-tolerant workloads.
ECS is often a strong fit for microservices, API backends, background workers, and scheduled tasks on AWS. Trade-offs can include less portability than Kubernetes-based platforms and fewer ecosystem extensions, which may matter for complex multi-cluster or multi-cloud requirements.
Common alternatives include Amazon EKS, HashiCorp Nomad, and managed Kubernetes offerings such as Google Kubernetes Engine (GKE) and Azure Kubernetes Service (AKS). For details on ECS concepts and components, see Amazon ECS documentation.
Our experience with AWS ECS helped us build repeatable delivery patterns, infrastructure modules, and operational runbooks that we use to help clients run containerized workloads reliably on AWS with clear security and cost controls.
Some of the things we did include:
- Designed and implemented ECS clusters on both EC2 and Fargate, selecting capacity strategies, task placement, and scaling policies based on workload characteristics.
- Built end-to-end CI/CD pipelines to build, scan, and deploy container images to ECS with safe rollout strategies (blue/green and canary) and automated rollback.
- Standardized service networking with ALB/NLB, private subnets, and service discovery, including cross-environment routing patterns for dev/stage/prod.
- Implemented secure IAM task roles, least-privilege policies, and secret handling via AWS Secrets Manager and Parameter Store, including rotation and audit-friendly access patterns.
- Set up observability for ECS services using CloudWatch metrics/logs, distributed tracing, and actionable alerting tied to SLOs and on-call workflows.
- Delivered infrastructure-as-code for ECS environments using Terraform modules, enabling consistent provisioning, drift detection, and controlled changes across accounts.
- Migrated container workloads from self-managed Docker hosts and AWS EKS to ECS where simpler operations and managed control planes were a better fit.
- Integrated ECS services with event-driven components (SQS/SNS/EventBridge) and data services, tuning task concurrency and retries for predictable throughput.
- Hardened deployments with image vulnerability scanning, runtime guardrails, and policy checks in pipelines, aligned to compliance requirements and internal standards.
- Optimized performance and cost by right-sizing CPU/memory, tuning autoscaling, using Spot where appropriate, and validating changes with load tests and real traffic patterns.
This experience helped us accumulate significant knowledge across multiple ECS use-cases—from greenfield platforms to migrations and cost optimization—and enables us to deliver high-quality AWS ECS setups that teams can operate confidently at scale.
Some of the things we can help you do with AWS ECS include:
- Review your current AWS networking, IAM, container build/release process, and produce an ECS readiness report with prioritized remediations.
- Create an adoption roadmap covering ECS on EC2 vs Fargate, cluster and capacity strategy, deployment patterns, and a pragmatic day-2 operating model.
- Design and implement production ECS services and task definitions with load balancers, service discovery, autoscaling, and safe rollout strategies.
- Standardize repeatable environments with Infrastructure as Code (Terraform/CloudFormation) and versioned, auditable changes across accounts and stages.
- Build secure CI/CD pipelines for container workloads (build, test, scan, deploy) with policy checks and automated promotion between environments.
- Implement security and compliance guardrails including least-privilege IAM, secrets management, image scanning, and network segmentation.
- Establish observability for ECS workloads—logs, metrics, tracing, dashboards, and actionable alerting—to reduce MTTR and improve reliability.
- Optimize cost and performance through right-sizing, autoscaling policy tuning, capacity provider configuration, and scheduling strategies.
- Harden operations with runbooks, incident response workflows, and hands-on troubleshooting for stability at scale.
- Enable your team with pairing, documentation, and practical training so engineers can deploy and operate ECS confidently.
Learn more on the AWS ECS product page.
