Once you’ve picked a partition key, the next decision is what actually runs inside a cell. For containerized workloads that’s an EKS-or-ECS question, and it’s worth more thought than “we already know Kubernetes.” The substrate decision determines how expensive isolation is per cell, and cost per cell is a number you’re about to multiply by every cell you’ll ever run.

EKS cells: namespace, or full cluster?

Inside EKS, a cell can live at two different depths. A namespace-per-cell model, with resource quotas and network policies enforcing the boundary, is cheap — one control plane serves every cell, and you’re mostly paying for the compute the pods consume. The catch is that the control plane itself becomes a shared blast radius domain again. A CRD conflict, an API server issue, or a cluster-wide add-on misbehaving can still touch every cell in that cluster, which undermines a chunk of the reason you wanted cells in the first place.

Cluster-per-cell removes that shared surface entirely, at a real price: an EKS control plane fee for every cluster, a separate node group or Karpenter deployment per cluster, and its own set of add-ons, IAM roles, and networking to keep patched. AWS’s own containerized cell-based reference design uses this model — pods are grouped into cell groups inside EKS, with topology-aware routing hints helping keep traffic within a cell’s Availability Zone rather than crossing between them. That Journey to Cloud-Native architecture post is worth reading end to end if you’re leaning EKS.

ECS cells: cluster-per-tenant is nearly free, until it isn’t

ECS clusters cost nothing to create — the meter starts when a task runs, which makes cluster-per-tenant a genuinely cheap way to get hard compute isolation without touching a Kubernetes control plane at all. It’s a well-worn SaaS isolation pattern for exactly that reason.

It’s also a pattern that can quietly become the problem. Amazon’s ad-serving platform ran an early cellular design that allocated a dedicated AWS account, Application Load Balancer, and ECS cluster to each tenant. Documented in AWS’s write-up on building hybrid multi-tenant architecture for stateful services, that setup hit real limits at a surprisingly small scale: just 18 clients spread across four AWS Regions already needed 181 separate targets to manage, onboarding a new client took roughly 52 days end to end, and average CPU utilization across the fleet sat around 3 percent. Strict isolation had bought accuracy, but the servers spent over 98 percent of their time waiting on requests that never came, because each tenant’s dedicated capacity had to be sized for its peak, not its average.

The fix wasn’t to abandon per-tenant isolation — it was to stop mapping tenants one-to-one onto cells. AWS Ads moved to a three-level hierarchy: a tier groups tenants with similar traffic profiles, a cell is the AWS account boundary within a tier, and an infra group — one VPC, one ALB, a set of per-tenant ECS clusters — is the reusable unit inside a cell. Onboarding became a configuration change against pre-wired infrastructure instead of a multi-week build, dropping to about 7 days. The lesson generalizes past ECS: your isolation boundary and your scaling unit don’t have to be the same thing.

The building blocks that show up in almost every cell design

Regardless of EKS or ECS, the same handful of AWS services tend to appear in the architecture diagram:

Purpose	Typical service
Container compute	Amazon EKS or Amazon ECS, often on Fargate or with Karpenter-managed EC2
Traffic routing to cells	Amazon Route 53 weighted or DNS-based routing, Application/Network Load Balancer
Cross-account, cross-compute service reachability	Amazon VPC Lattice, AWS PrivateLink
Cell-assignment state	Amazon DynamoDB — fast, highly available, a natural fit for “which cell does this customer belong to” lookups
Container images	Amazon ECR
Observability per cell	CloudWatch Container Insights, Amazon Managed Service for Prometheus, Amazon Managed Grafana
Cost visibility per cell/tenant	Kubecost against Amazon Managed Prometheus for multi-cluster cost monitoring
Deployment	Infrastructure as code (Terraform or CDK) plus ArgoCD for the app layer

The DynamoDB piece deserves a callout. AWS’s own hyperscale teams have mapped customers to cells with a hash function whose result is stored in DynamoDB, precisely because that table needs to answer “which cell?” reliably under load without becoming a bottleneck itself. It’s a small, boring piece of infrastructure carrying an outsized amount of trust.

Router patterns, briefly

Every design needs something that decides which cell a given request goes to, and there are three broad ways to build it, each with a different failure profile. A load-balancer-style router sits in the request path permanently, forwarding every packet — simple for clients, but now in the critical path for every transaction. A hand-off router authenticates the client once and returns the cell’s address, after which the client talks to the cell directly; this removes the router from the ongoing request path but pushes some logic into the client. A DNS-based router leans entirely on the DNS system’s own reliability, at the cost of DNS-level control over routing decisions. None of these is strictly better — which one fits depends on how much control you need over routing versus how simple you want the client side to be. We’ll dig into this properly, including where a control-plane/data-plane split helps, in the networking post.

Which one would I reach for

If I’m starting fresh on containers and the workload doesn’t need OS-level tuning, I lean ECS cluster-per-cell for the early cells. It’s cheaper to stand up and tear down, and the limits you bump into are AWS service quotas rather than Kubernetes control-plane behavior you have to reason about separately. I’ll reach for EKS cluster-per-cell once the team already runs Kubernetes elsewhere and wants one operational model across cell and non-cell workloads — the consistency is worth the extra control-plane bill at that point. Namespace-per-cell inside a single EKS cluster is the one I’d be most cautious about recommending for anything claiming strong isolation; it’s cheap, but you’re still betting the whole cluster’s control plane won’t be the thing that takes every cell down at once.

The substrate decision sets your cost floor. The networking layer sets your failure floor — which is where we’re headed next.