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Testimonials
Nguyen is a champ. He's fast and has great communication. Well done!
We were impressed with their commitment to the project.
You guys are really a bunch of talented geniuses and it's a pleasure and a privilege to work with you.
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.
I was impressed at how quickly they were able to handle new tasks at a high quality and value.
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.
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.
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.
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How it works?
Argo Workflows is an open-source, Kubernetes-native workflow engine for defining and orchestrating multi-step pipelines as containerized tasks. Platform, data, and ML teams use it to automate batch processing, ETL, model training, and other job graphs that need reliable execution, retries, and clear visibility into run status. Workflows are declared as Kubernetes resources, making them a natural fit for teams standardizing on GitOps and cluster-based operations.
It typically runs inside a Kubernetes cluster and models pipelines as step-based sequences or DAGs, enabling parallel execution and dependency management across container jobs.
- Step-based and DAG-based workflow definitions for complex pipelines
- Parallelism controls and concurrency limits for predictable cluster usage
- Artifacts support to pass outputs between workflow steps
- Retries, conditionals, and loops for resilient, dynamic execution
- Built-in CLI and UI for submission, monitoring, and troubleshooting
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
Argo Workflows is a Kubernetes-native workflow engine for defining, scheduling, and orchestrating multi-step pipelines as containerized tasks. It is used to automate complex job graphs with strong Kubernetes alignment, reproducibility, and scalable parallel execution.
- Runs workflows as Kubernetes resources, enabling GitOps-friendly configuration and native RBAC, namespaces, and quotas.
- Executes each step in an isolated container, improving reproducibility and reducing dependency drift across environments.
- Supports DAG and step-based workflows, making it suitable for both linear pipelines and complex fan-out and fan-in patterns.
- Scales parallel tasks using Kubernetes scheduling, helping handle large batch workloads without a separate worker fleet.
- Provides artifact passing and integration with object storage, simplifying handoffs between tasks for data and ML pipelines.
- Includes retries, timeouts, and conditional execution, improving resilience and controllability for long-running jobs.
- Offers event-driven triggering via Argo Events, enabling workflows to start from webhooks, messages, or cluster events.
- Supports parameterization and templates, reducing duplication and making workflow definitions easier to standardize.
- Exposes a UI and APIs for observability, facilitating debugging, auditability, and operational visibility of workflow runs.
- Integrates well with CI/CD and platform tooling on Kubernetes, enabling consistent automation across build, deploy, and data operations.
Argo Workflows is a strong fit for Kubernetes-centric batch processing, data engineering pipelines, and MLOps orchestration where container isolation and cluster-native scaling matter. Trade-offs include the operational overhead of running and upgrading the Argo components and the need to model workflows declaratively, which can be less convenient than code-first orchestrators for some teams. For platform-aligned workflow orchestration concepts and ecosystem context, see https://argoproj.github.io/workflows/.
Common alternatives include Apache Airflow, Prefect, Dagster, and Tekton Pipelines, with selection typically driven by Kubernetes-first requirements versus code-first authoring, scheduling needs, and ecosystem integrations.
Our experience with Argo Workflows helped us build repeatable patterns, templates, and operational tooling for teams running complex, containerized pipelines on Kubernetes. Across delivery engagements, we implemented workflow orchestration that improved reliability, reduced manual steps, and made multi-stage automation easier to operate at scale.
Some of the things we did include:
- Designed and implemented Argo Workflows pipelines for batch processing, ETL, and ML job graphs, with clear task boundaries and reusable workflow templates.
- Integrated Argo Workflows with Kubernetes clusters across cloud and on-prem environments, including namespace isolation, RBAC, and service account scoping.
- Standardized CI/CD delivery of workflows using GitOps practices and policy checks, aligning releases with existing Argo CD deployment flows where applicable.
- Hardened workflow execution with secrets management, least-privilege permissions, network policies, and image provenance controls for regulated environments.
- Implemented observability for workflows (metrics, logs, and run-level traceability), and connected alerts to on-call workflows to reduce MTTR during failures.
- Optimized performance and cost by tuning parallelism, resource requests/limits, node selection, and retry/backoff strategies for high-volume workloads.
- Built artifact and output handling patterns for large datasets (object storage, caching, and retention), reducing rework and improving reproducibility.
- Implemented reliability patterns such as idempotent steps, checkpointing, and resumable workflows to handle intermittent infrastructure and dependency failures.
- Containerized legacy scripts and cron-based processes and migrated them into Argo Workflows, improving scheduling control, auditing, and change management.
- Enabled secure multi-team usage with standardized onboarding, shared libraries, and runbooks, and provided hands-on training for developers and platform teams.
This experience helped us accumulate significant knowledge across multiple Argo Workflows use-cases—from platform foundations to production operations—and enables us to deliver high-quality Argo Workflows setups that are maintainable, observable, and aligned with real delivery constraints.
Some of the things we can help you do with Argo Workflows include:
- Assess your current Kubernetes batch and pipeline workflows and deliver a review report with prioritized improvements.
- Create an adoption roadmap for standardizing workflow patterns, naming conventions, and reusable templates across teams.
- Design and implement Argo Workflows pipelines for data processing, ML jobs, and platform automation with reliable retries and parallelism.
- Deploy and operate Argo Workflows on Kubernetes with production-ready configuration, RBAC, namespaces, and multi-tenant guardrails.
- Integrate workflows with CI/CD and GitOps practices to promote versioned, auditable workflow definitions and safer releases.
- Harden security and compliance with least-privilege service accounts, secrets management practices, and policy controls.
- Improve reliability and observability by instrumenting logs, metrics, and alerts for workflow health, SLA tracking, and faster incident response.
- Optimize performance and cost by tuning resource requests/limits, concurrency, caching strategies, and Kubernetes autoscaling behavior.
- Troubleshoot failed workflows and cluster-level bottlenecks, then implement fixes and runbooks to reduce recurrence.
- Enable your teams with hands-on training, best-practice templates, and operational playbooks for day-2 support.
