The problem this solves
Many cloud programs begin with speed and end with fragmentation. Teams add accounts, clusters, services, and automation before the operating model is clear. Costs rise, observability is inconsistent, security exceptions multiply, and latency-sensitive workloads still sit too far from the users, machines, or sensors that depend on them.
Edge computing adds another layer of complexity. Industrial sites, logistics networks, healthcare devices, and AI inference endpoints need local processing, resilience during connectivity gaps, and a clear way to synchronize with central cloud services. Without a disciplined architecture, edge becomes a collection of fragile exceptions instead of a managed extension of the platform.
How we build it
We start by mapping each workload to its business constraints: latency, compliance, data gravity, availability, operational ownership, and expected growth. From there, we define the landing zone, network model, identity boundaries, deployment topology, data movement strategy, and observability baseline.
We then turn the architecture into a production path. That includes infrastructure-as-code, CI/CD integration, policy guardrails, cost tagging, resilience testing, edge update strategy, and runbooks for the teams who will own the system after launch. The result is a platform that developers can use without bypassing security or operations.
Where it fits
Hybrid cloud landing zones for regulated or multi-region businesses.
Edge inference for manufacturing, logistics, retail, and field operations.
Cloud cost optimization for AI, data, and container workloads.
Migration from manually managed infrastructure to repeatable platform patterns.
How the work is delivered
We confirm the business case, test the riskiest technical assumptions, and build the smallest version that is useful in production. Then we prepare your team to own and run it.
What you get
A placement plan that states which workloads run in a cloud region, which run at the edge, and why.
A standard landing zone (the base setup of accounts, networks, and access rules) that new workloads deploy into.
Cost tags and budgets, so spending can be traced to the team and workload behind it.
Tested failover and recovery plans, including how edge sites keep working when the connection drops.
Infrastructure-as-code templates, deployment pipelines, and runbooks your team can reuse after launch.
Frequently asked questions
When should a workload move to the edge instead of staying in the cloud?
Edge is most useful when latency, connectivity, data volume, privacy, or local autonomy materially changes the business outcome. Vertex evaluates those constraints before recommending edge infrastructure.
Can Vertex work with our existing cloud provider?
Yes. The service is provider-aware rather than provider-prescriptive. We work across AWS, Azure, and GCP and can design hybrid patterns around your existing commitments.
Does this include cloud cost optimization?
Yes. Cost ownership, workload placement, rightsizing, tagging, and usage visibility are part of the architecture, not a separate cleanup activity.
How do you keep edge deployments manageable?
We standardize deployment, configuration, observability, update workflows, and fallback behavior so edge nodes behave like managed platform extensions.