What Your Startup Runs, and What You Are Paying For

Start With What AWS Is Better At

There is no argument to be had here, so it is worth conceding it properly before anything else.

AWS has more managed services than any competitor, and the gap is not close. It has autoscaling at every layer, from instances to containers to databases to the load balancers in front of them. It has the deepest permission model in the industry, the widest compliance coverage, the most regions, and an ecosystem where every third-party tool has an AWS integration written first. If you need a queue, a data warehouse, a machine learning endpoint and a hardware security module, they all exist, they all speak to each other, and one team can own them.

That is worth money. AWS charges for it and the charge is not unreasonable.

The question this article is about is a different one, and it is a question about timing rather than quality.

The AWS Features Your Startup Will Not Touch for Two Years

Sit down and list what an early product actually runs. For most of them it comes to a handful of things: some containers, a Postgres, a Redis, object storage, a load balancer, and somewhere to put logs and metrics.

Now list what the AWS bill is priced around. Multi-account organisations with service control policies. Cross-region replication. Fine-grained IAM with permission boundaries and session policies. Autoscaling groups tuned against custom CloudWatch metrics. Transit gateways. Reserved capacity planning. Compliance evidence collection.

Every one of those is genuinely good, and each solves a problem that shows up later: a second environment that matters, a second team touching production, an auditor with questions. AWS is built so that when those arrive, the answer already exists, is documented and is supported. That is a deliberate design goal and it is most of why larger companies stay.

It also means the platform is built for a stage. A product can reach for it before it has reached that stage, which is a question of sequencing rather than a mistake about quality.

The Differences That Outlive the Price List

The differences that matter are structural rather than a matter of who is cheaper this quarter. Prices change; how a platform meters you does not.

The control plane is a line item on one and not on the other. Amazon EKS bills the managed Kubernetes control plane per cluster, per hour, as its own charge separate from the nodes underneath it. DigitalOcean Kubernetes includes the control plane at no charge and sells high availability for it as an add-on. Whatever the numbers are when you read this, one platform has a cluster charge before you deploy anything and the other does not.

Outbound traffic is metered differently, and the difference compounds. AWS gives a small free allowance across the whole account, then meters egress by the gigabyte for everything leaving for the internet. DigitalOcean attaches a transfer allowance to each node and pools it across the cluster, so a three-node cluster carries three allowances that any node can draw on. The practical consequence is that on one platform your bill tracks your traffic from the first month, and on the other it does not move at all until you leave the pool.

For a product serving pages, images or an API to real users, this is usually the largest single difference between the two, and it is the one least visible when you are estimating costs from an instance-size table.

Version lifecycle is handled differently, and it is worth setting deliberately. Upstream Kubernetes supports a release for about a year. AWS supports each version on EKS for fourteen months of standard support and then offers a further period of extended support, during which the cluster keeps receiving security patches long after upstream has moved on. That is a real service, and running old versions safely is expensive to provide, so it carries a higher per-cluster rate.

The part to set consciously is the default. An EKS cluster's upgrade policy defaults to EXTENDED, so a cluster left alone moves into extended support and its higher rate rather than being upgraded. Setting the policy to STANDARD inverts that: AWS upgrades the cluster when standard support ends, on its schedule.

Both behaviours are documented and both are defensible, and which one you want depends on whether an unplanned upgrade or a higher rate is the bigger problem for your team. It is worth choosing rather than inheriting, because the consequence arrives more than a year after the cluster was created.

DigitalOcean handles the same problem by patching within a version and requiring an upgrade to a supported one, with no equivalent paid tier.

The Uptime Numbers Almost Nobody Compares

The usual reason given for choosing AWS anyway is reliability. It is worth checking that against what both companies actually commit to in writing, because the published commitments do not say what most people assume.

	DigitalOcean	AWS
Managed Kubernetes control plane	99.95%, with HA enabled	99.95%, standard
A single machine	99.99% per Droplet	99.5% per EC2 instance
Spread across zones	not offered	99.99% at region level
Credit when missed	100%	10, 30 or 100% by severity

The control-plane commitments are identical, down to the credit tiers.

The single-machine line surprises people, and it is worth reading as a difference in design philosophy rather than a scoreboard. DigitalOcean commits to 99.99% for an individual Droplet, roughly four minutes a month. AWS commits to 99.5% for an individual EC2 instance, roughly three and a half hours, and 99.99% once you are spread across availability zones.

Those two numbers describe two different assumptions about how you will build. DigitalOcean's is a promise about the machine, because many of its customers run one. The AWS pair is a promise about an architecture, because AWS is designed on the premise that no single instance should matter, and the whole platform, from availability zones to autoscaling groups to load balancers, exists to make that true.

If you build the way AWS assumes, the higher number is the one you get, and it is a stronger guarantee than anything on the other side of the table. The question for an early team is whether it is building that way yet.

Three caveats belong with that table, because leaving them out would be cherry-picking.

DigitalOcean's control-plane commitment applies only when high availability is enabled, which is a paid option. The AWS commitment is on the standard control plane with nothing added.

The AWS region-level number is not marketing. If you have built across zones, you have earned it.

And an SLA is a refund policy, not a measurement. Neither figure tells you what either platform's actual uptime was last year. They tell you what each company is willing to owe you when it misses.

Before You Pick AWS, Check What You Will Actually Use

The decision comes down to one question, and it is answerable in an afternoon.

Write down the services your product needs in the next twelve months. Not the ones you might want at Series B. The ones that appear in your architecture diagram now.

If that list is containers, a database, a cache, object storage and a load balancer, both platforms do all of it, and the differences above are the only ones that will show up on your invoice or in your week.

If the list has things on it like cross-account isolation, a compliance regime with an auditor attached, a data pipeline feeding a warehouse, or traffic patterns that genuinely need autoscaling tuned against custom metrics, then AWS is the right answer and the price is the price. Go there deliberately, knowing why.

Both answers are right for someone, and plenty of good companies are on each. The thing worth avoiding is picking without writing the list, because then the platform decision gets made by an invoice a year later instead of by you.

Moving Later Is Not the Disaster It Sounds Like

The other objection is lock-in, and it deserves a straight answer rather than reassurance.

If you build on plain Kubernetes, your workloads are manifests and container images, and both of those move. What does not move cleanly is everything you take from the platform rather than from Kubernetes: managed database features, IAM-based authentication between services, provider-specific load balancer annotations, and any queue or function you wired in directly.

So the practical advice is not "avoid managed services". It is to know which of them you are marrying. A managed Postgres you talk to over a connection string is portable. A workflow built from six AWS-specific services is the thing that will hold you, and by the time you have it, you are also the company that should be on AWS.

We do this migration in both directions and have written about the mechanics of moving Kubernetes workloads without downtime in zero-downtime Kubernetes deployments.

What We Tell People Who Ask

Start where the platform matches the size of the problem. For most early products that is the cheaper, smaller platform, and the honest reason is not that AWS is bad. It is that you cannot yet use the parts of AWS that justify it.

Revisit the decision when something specific pushes you: a compliance requirement, a traffic pattern that needs real autoscaling, a second team, a second region. Those are concrete triggers and you will recognise them when they arrive. Moving at that point is a normal step in a company's life rather than an admission that the first choice was wrong.

If you want a second opinion on which side of that line you are on, we manage Kubernetes on both, and the conversation usually takes one call and a look at your architecture diagram.