Cloud DevOps Engineering for Enterprise Infrastructure

MetaSys works across AWS, Google Cloud Platform, and Azure, and builds multi-cloud and hybrid architectures. We also use Cloudflare for edge and CDN, and Vercel and Netlify for frontend hosting. The right platform depends on your workloads, so we design the target architecture around your systems rather than defaulting to one vendor.

Yes. MetaSys builds CI/CD pipelines with GitHub Actions, GitLab CI, and Jenkins, and automates infrastructure with Terraform and OpenTofu. We design Kubernetes clusters on EKS, GKE, and AKS, and set up canary releases, blue/green deployments, and automated rollback so your team can ship quickly and safely.

Yes. MetaSys runs a phased migration that starts with an infrastructure audit, then a target architecture you approve before any resource is created. We migrate in phases beginning with low-risk workloads, validate each phase before the next, and keep rollback possible at every stage. Production cutover includes live monitoring and a 30-day stabilization period.

MetaSys treats security as a baseline requirement, not an add-on. We implement IAM policies, network segmentation, and secrets management with Vault or AWS Secrets Manager, plus security scanning inside CI/CD. We build environments that meet compliance frameworks including SOC 2 and HIPAA, with encryption at rest and in transit throughout.

MetaSys optimizes cloud spend through right-sizing, reserved instance planning, spot instance strategies, and automated cost anomaly detection. On average, clients see a 60% reduction in cloud infrastructure cost after we rebuild their environments. We cut spend without cutting performance, so uptime and scalability hold as costs come down.

FinOps for AI workloads means treating cost as a first-class engineering metric, not a monthly surprise. MetaSys controls AI workload cloud spend through right-sizing, reserved instance planning, spot instance strategies for interruptible training jobs, and automated cost anomaly detection, which is how clients typically see a 60% reduction in cloud infrastructure cost without giving up performance.

MLOps covers the lifecycle of traditional trained models: data versioning, training pipelines, model registries, and monitoring for drift. LLMOps adds the concerns specific to large language models, such as prompt versioning, token cost tracking, and evaluating generated output rather than a single accuracy number. MetaSys builds both, since most production AI systems today run classic models and LLM-based components side by side.

MetaSys runs AI inference on Kubernetes clusters (EKS, GKE, or AKS) with auto-scaling tuned to inference traffic patterns, GPU-aware pod scheduling, and resource limits that keep one workload from starving another. This is the same infrastructure behind the 99.99% uptime SLA MetaSys delivers on managed clusters.

Ask whether they have run production Kubernetes clusters for AI inference specifically, not just standard web workloads, how they separate cost control for training versus inference, and what their incident response looks like outside business hours. MetaSys answers with named cluster metrics, a documented FinOps practice, and a 30-day stabilization period with dedicated support after every migration.