DigitalOcean to AWS — the service map, the cutover, and who runs it for you.

IWhy teams move off DigitalOcean — scale, services, and enterprise needs

Nobody migrates off DigitalOcean because it is hard to use — the opposite is true. They migrate because the simplicity that made DO perfect for a handful of Droplets becomes a ceiling on scale, on the services they can reach for, and on the buyers they can sell to.

The first reason is scale. DigitalOcean is excellent up to a point, then the walls appear: Droplet sizes top out well below AWS's largest families, GPU and specialized-compute options are limited, autoscaling is basic next to EC2 Auto Scaling and ECS/Fargate service scaling, and the footprint is roughly a dozen regions against AWS's 30-plus regions and 100-plus availability zones. A team that needs multi-region active-active, fine-grained autoscaling, or hardware DO simply does not offer (Graviton, Inferentia/Trainium, large-memory or local-NVMe instances) eventually hits a ceiling no Droplet resize clears.

The second reason is the managed-services catalog. DigitalOcean deliberately keeps a small, curated set: Droplets, App Platform, Managed Databases (PostgreSQL, MySQL, Redis/Valkey, MongoDB, Kafka, OpenSearch), Spaces object storage, DOKS (managed Kubernetes), Load Balancers, Functions, and a handful more. That is enough for a classic web app — and a hard stop the moment you want a managed message bus beyond what is offered, a data warehouse (Redshift/Athena), a managed streaming or ETL layer (Kinesis/Glue), fine-grained IAM, a service mesh, managed feature stores, or any of the long tail of AWS primitives. On AWS the answer to "is there a managed service for this?" is almost always yes; on DO, beyond the core, you are back to running it yourself on a Droplet.

The third reason is enterprise and compliance gravity. As teams move upmarket, procurement and security reviews start asking for things that favor AWS: a broad set of compliance attestations and audited controls (SOC 2, ISO 27001, PCI DSS, HIPAA eligibility, FedRAMP for public sector), data-residency choices across many regions, private connectivity (PrivateLink, Direct Connect, VPC peering), granular IAM and org-level guardrails (AWS Organizations, SCPs, Control Tower), and the simple fact that enterprise buyers and partners often standardize on AWS. DigitalOcean has been investing here, but AWS's breadth is still the reason a deal-blocking security questionnaire pushes a team to migrate.

The honest counterpoint: DigitalOcean is genuinely cheaper and simpler for steady, predictable workloads, and AWS asks you to own more — a landing zone, IAM, networking, and more configuration surface for the same app. Cost is rarely the reason to leave DO (often DO is cheaper at small scale); the reasons are scale headroom, service breadth, and enterprise readiness. That extra operational surface is precisely why most teams do not run this migration alone — and the CloudRoute angle is that a vetted AWS partner runs it for you, often funded by AWS.

IIThe target architecture: what each DigitalOcean piece becomes on AWS

DigitalOcean's building blocks map onto AWS with little ambiguity — DO intentionally mirrors the shape of the primitives. The only real decision is how much operational control you want at the compute layer; the data and storage services are close to one-to-one.

For compute, the target depends on what you run today. Plain Droplets (raw Linux VMs) become Amazon EC2 — the like-for-like VM, with far more instance families, autoscaling, and Savings Plan pricing — or Amazon Lightsail when you want the closest DO-like experience: a fixed-price, batteries-included VM with a simple console. If you run on App Platform (DO's PaaS), the natural targets are AWS App Runner (point it at a container or repo and it builds, deploys, serves HTTPS, and autoscales) or Amazon ECS on Fargate (serverless containers with full VPC networking, task sizing, service autoscaling, and an Application Load Balancer). Teams migrating because they outgrew DO's simplicity usually want EC2 or Fargate — the control App Runner and Lightsail abstract away is exactly what they are reaching for.

For the relational database, DO Managed Databases for PostgreSQL or MySQL become Amazon RDS (the like-for-like managed engine, simplest mental model) or Amazon Aurora (AWS's cloud-native PostgreSQL/MySQL-compatible engine — a little more per hour, but with faster failover, autoscaling storage, up to 15 read replicas, and Serverless v2 capacity scaling). Because both ends speak the same open-source protocol, your application code, queries, and ORM do not change; this is a homogeneous migration. DO Managed Redis/Valkey becomes Amazon ElastiCache (Redis OSS or Valkey) with a connection-string change, and DO Managed MongoDB maps to Amazon DocumentDB (or self-managed/Atlas if you need full MongoDB feature parity).

For storage and delivery, DigitalOcean Spaces is S3-compatible, so it becomes Amazon S3 almost directly — the same API and SDK calls, just a new endpoint and credentials — and the Spaces built-in CDN becomes Amazon CloudFront in front of the S3 bucket. DOKS (managed Kubernetes) becomes Amazon EKS: your manifests, Helm charts, and controllers largely carry over, with the work being the control-plane move, IAM-for-service-accounts (IRSA), the container registry (DO Container Registry → Amazon ECR), and load-balancer/ingress wiring. DO Load Balancers become an Application Load Balancer (HTTP/HTTPS) or Network Load Balancer (TCP/UDP), and DO Functions become AWS Lambda.

The supporting glue maps just as cleanly: DO's managed DNS and domains move to Amazon Route 53, free TLS comes from AWS Certificate Manager (ACM) instead of Let's Encrypt on a Droplet, DO Monitoring/alerts become Amazon CloudWatch (metrics, logs, and Container Insights), and DO's VPC becomes an Amazon VPC with subnets, security groups, and IAM roles. The one-time translations — secrets out of environment files into AWS Secrets Manager + SSM Parameter Store, and any cron running on a Droplet into Amazon EventBridge Scheduler triggering an ECS task or Lambda — are the hands-on work a migration partner standardizes. The full row-by-row mapping is in the comparison table below.

IIIDigitalOcean → AWS service map (the principles behind the table)

The full row-by-row mapping — every DigitalOcean building block, its AWS equivalent, what changes, and the engineering effort — lives in the comparison table further down. Two principles make reading that table safe.

First, anything built on a standard, open-source protocol moves with a connection-string or endpoint change and no application rewrite: PostgreSQL/MySQL (DO Managed Databases → RDS/Aurora), Redis/Valkey (DO Managed Redis → ElastiCache), Kubernetes (DOKS → EKS), and S3-compatible object storage (Spaces → S3). These are the "trivial-to-low" rows — the move is data transfer and credentials, not code.

Second, anything DigitalOcean-specific or simply absent on the DO side needs a one-time translation into an AWS-native primitive: App Platform build/deploy → App Runner or an ECS/CodePipeline flow, Droplet-resident cron → EventBridge Scheduler, environment-file secrets → Secrets Manager + Parameter Store, DO Container Registry → ECR, DO Monitoring → CloudWatch, and the Spaces CDN → CloudFront. Done once and documented, those translations are the work a migration partner does — the "medium/high effort" rows in the table.

IVThe cutover plan: DMS, Spaces→S3 sync, DNS, and a short downtime window

Everything else — compute, networking, the EKS cluster, the S3 buckets — can be built and tested without touching production. The database cutover and the object-storage copy are the two moments that touch live data, and a plan is what keeps user-visible downtime to zero-to-fifteen minutes.

For the database, the core technique is to keep the new RDS/Aurora instance continuously in sync with your DO Managed Database while you test, so that at cutover you switch to an already-current copy instead of copying a cold database under time pressure. AWS Database Migration Service (DMS) does exactly this: a full load into RDS, then a switch into change data capture (CDC) mode that streams every subsequent insert/update/delete in near-real time. Because both ends are the same engine (PostgreSQL→PostgreSQL or MySQL→MySQL), this is a homogeneous migration — no Schema Conversion Tool needed; the schema moves as-is. Very small databases can skip DMS for a single dump/restore inside the window; DMS earns its place once a cold dump/restore would blow past an acceptable downtime window.

For Spaces, the object data copies into S3 ahead of the window. Because Spaces is S3-compatible, the simplest approach is rclone (or the AWS CLI) running an initial bulk sync, then a final incremental sync just before cutover to catch anything new; for large datasets AWS DataSync gives a managed, parallelized transfer with verification. The application change is small — swap the endpoint, bucket name, and credentials in your S3 client config, and (if you used the Spaces CDN) point image/asset URLs at CloudFront. Object storage rarely needs strict point-in-time consistency, so the final incremental sync usually closes the gap without extending the maintenance window.

With CDC running, the buckets synced, and the new stack smoke-tested against the synced data, the cutover is short and scripted: maintenance mode → let DMS drain the last few seconds → run the final Spaces→S3 incremental sync → flip the database connection string and S3 endpoint → switch traffic to the new App Runner/Fargate/EKS service → update the Route 53 record (TTL lowered ahead of time) → verify writes land in RDS and uploads land in S3 → exit maintenance. Because the data was already synced, the window is dominated by DNS propagation and verification, not data transfer — commonly 0–15 minutes of write-downtime during a low-traffic period.

VThe step-by-step migration (assess → land → cut over → optimize)

Here is the end-to-end sequence a partner runs, mapped to the AWS MAP phases (Assess → Mobilize → Migrate). For a typical DigitalOcean workload this is a 3–8 week project — most of it parallelizable, none of it touching production until the final cutover.

• 1 — Assess (days 1–5) — Inventory every Droplet, App Platform app, Managed Database, Spaces bucket, DOKS cluster, Load Balancer, Function, cron job, and DNS zone. Decide EC2/Lightsail vs App Runner vs Fargate vs EKS, and RDS vs Aurora; produce the target architecture and a cost before/after model. Under MAP this Assess/TCO phase is typically free.
• 2 — Build the landing zone — Stand up the AWS account structure, VPC, subnets, security groups, IAM roles, ECR, and Secrets Manager / Parameter Store. (Reuses the AWS Landing Zone and AWS DevOps foundations in the DevOps cluster.)
• 3 — Recreate compute — Provision EC2/Lightsail, or build the App Runner service / ECS task definitions / EKS cluster. Containerize anything that was a bare Droplet process or App Platform app, and confirm images build and boot in CodeBuild or your existing CI.
• 4 — Provision data + storage — Create the RDS/Aurora instance and ElastiCache cluster in the VPC, restore the schema, create the S3 buckets (+ CloudFront), and wire EventBridge Scheduler for any Droplet/App Platform cron jobs.
• 5 — Start replication + bulk copy — Run a DMS full load from the DO Managed Database into RDS, then switch to CDC. In parallel, bulk-sync Spaces into S3 with rclone/DataSync. Validate row counts, known records, and object counts.
• 6 — Deploy + smoke-test — Deploy the new compute against the synced database and S3 (no public traffic yet) and run the full test suite + manual smoke tests, including background jobs, scheduled tasks, and asset/CDN delivery.
• 7 — Cut over — Maintenance mode → drain CDC → final Spaces→S3 incremental sync → flip connection string + S3 endpoint → switch traffic → update Route 53 (low TTL pre-set) → verify writes/uploads land on AWS → exit maintenance. Target window: 0–15 minutes.
• 8 — Optimize + decommission — Apply a Compute Savings Plan / Reserved Instances, right-size EC2/Fargate and RDS, set autoscaling, confirm CloudWatch alarms and backups. Keep DigitalOcean as rollback for a short window, then destroy the Droplets, databases, and Spaces — that teardown is when any savings become real.

VICost before and after — a representative mid-size app

Cost deserves an honest model, because for DigitalOcean it often cuts the other way at small scale. Here is a representative comparison using 2026 list-price ranges for a mid-size production app; yours vary with traffic, region, and how aggressively you right-size and commit.

Take a typical growth-stage SaaS on DigitalOcean: a handful of mid-size Droplets (or a DOKS cluster) running web + workers, a Managed PostgreSQL cluster with a standby, Managed Redis, a Spaces bucket with the CDN, and a Load Balancer. On DigitalOcean that commonly totals $600–$1,800/month with simple, predictable pricing — and that simplicity is a genuine feature.

The same workload on AWS at list price — EC2/Fargate (or EKS) for web + workers, RDS/Aurora with a Multi-AZ standby, an ElastiCache node, S3 + CloudFront, an ALB, plus data transfer and CloudWatch — often lands in a similar-to-somewhat-higher band at first: roughly $800–$2,200/month before optimization, because AWS prices more components individually and Multi-AZ/CloudFront/data-transfer add line items DO bundles. Once you apply a one- or three-year Compute Savings Plan, Reserved Instances or committed database capacity, and right-size with autoscaling, steady-state typically settles back to $700–$1,600/month — at parity or modestly above DO for the same workload, but now on infrastructure that scales far past where DO would have capped you.

So the honest framing on cost: do not migrate from DigitalOcean to AWS expecting an immediate bill cut — at small, steady scale you may pay the same or a little more, and you trade DO's simplicity for AWS's configuration surface. You migrate for the scale headroom, the service catalog, and the enterprise readiness covered earlier; AWS cost competitiveness arrives at scale once committed pricing kicks in. The cost the table cannot show is the migration itself — engineering time, dual-running DO + AWS for a few weeks, and the learning curve of owning the infrastructure — and that is exactly where the CloudRoute model changes the arithmetic. When the workload qualifies for the AWS Migration Acceleration Program, AWS funds the assessment and credits a large share of the migration/modernization cost (the partner is paid through MAP), so you reach AWS without paying the usual migration bill. When MAP does not apply, it is a vetted-partner engagement that de-risks the cutover: you pay for the work, but you are not improvising the riskiest 20 minutes of your year.

VIIGotchas: the things that turn a clean migration messy

Most DigitalOcean→AWS migrations that go badly go badly for a small, repeatable set of reasons. Naming them up front is the cheapest insurance there is.

• You are taking on AWS's configuration surface — A Droplet is a VM you SSH into; an equivalent EC2 setup means a VPC, subnets, security groups, IAM roles, and an ALB before "hello world" serves traffic. This is the single biggest adjustment — budget for the landing zone, do not bolt networking and IAM on after the fact, and lean on the AWS Landing Zone foundation rather than hand-rolling it.
• Spaces → S3 is API-compatible, not behavior-identical — The SDK calls match, but bucket policies, ACLs, CORS rules, signed-URL parameters, and default-encryption settings do not carry over — they must be recreated on S3. Public-by-default Spaces buckets becoming locked-down S3 buckets (S3 Block Public Access is on by default) is a classic "images 403 after cutover" surprise. Re-derive every policy and CORS rule deliberately.
• Managed Database connection + SSL differences — DO Managed Databases enforce SSL and hand you a specific connection string and CA; RDS/Aurora want their own SSL mode and CA bundle, and connection limits scale with instance class differently. Many "won't connect after cutover" issues are just sslmode and certificate config — verify the string and pooling (RDS Proxy / PgBouncer) against RDS before the window.
• DOKS → EKS is not a lift-and-shift of the cluster — Manifests and Helm charts carry over, but the control-plane move, IRSA (IAM roles for service accounts), the registry switch (DO Container Registry → ECR), the ingress/load-balancer controller, the CSI driver for persistent volumes, and cluster-autoscaler/Karpenter all need re-wiring. Treat EKS as a rebuild-and-redeploy, not a cluster export.
• Cron and scheduled jobs must be recreated explicitly — Cron living on a Droplet or in an App Platform job does not migrate itself — every schedule has to be rebuilt as an EventBridge Scheduler rule triggering an ECS task or Lambda. Jobs silently not firing after cutover is the classic "we forgot one" failure; inventory them in the Assess phase.
• Forgetting to decommission DigitalOcean — If a cost reduction is part of the goal, it is not real until the Droplets, Managed Databases, and Spaces are destroyed. Keep DigitalOcean as rollback for a defined short window — then actually tear it down. Paying both bills "just in case" for months erases any year-one benefit and is pure waste.