AWS infrastructure for products that matter.
Danubio designs, migrates, scales, and operates production workloads on AWS. New architectures for greenfield products, scaling plans for live systems under load, infrastructure-as-code migrations, security hardening, observability, and the cost discipline that keeps the bill from outpacing the business.
One team, across new builds, modernization, and the years after.
What AWS is good at.
AWS is the broadest production cloud, and that breadth is the point. These are the architectural shapes where the platform pays off and where Danubio reaches for it.
Scaling primitives that show up when you need them.
Auto-scaling groups, Lambda concurrency, RDS read replicas, CloudFront at the edge. The capacity to absorb traffic spikes without over-provisioning the steady state, and without rewriting the application to get there.
Managed services that move responsibility off the team.
Postgres without running Postgres, Kafka without running Kafka, ML inference without owning the GPUs. Each one is a deliberate tradeoff between cost and operational load that frees engineering time for product work.
Multi-region as a first-class concern.
Latency, residency, and disaster recovery are infrastructure questions AWS gives real answers to. Cross-region replication, edge caching, and Route 53 failover come as building blocks rather than projects to build from scratch.
Identity and security that scale with the team.
IAM, SSO, KMS, Secrets Manager, GuardDuty. Security primitives that grow with the org and the codebase, instead of becoming the thing that breaks at scale or fails the next compliance review.
The workloads Danubio operates on AWS.
The kinds of architectures AWS is most at home with, drawn from the production work we ship and operate over years.
Multi-tenant SaaS at production scale.
Brokerage platforms, enterprise CRMs, vertical marketplaces. Workloads where AWS provides the elastic capacity to absorb the customer growth that breaks single-server deployments, with the multi-AZ posture enterprise buyers require.
Real-time data and streaming pipelines.
Kafka on MSK, ingest into Postgres or S3, derived state surfaced through dashboards. Throughput patterns that stay predictable from launch volume through 10x and 100x growth, with the observability to know when something drifts.
AI and ML product infrastructure.
SageMaker for training and inference, OpenSearch for retrieval, Lambda glue for the integration layer. The infrastructure side of an AI product, kept separate from the model work so the data scientists can focus on the model.
Operational platforms with reliability requirements.
Internal tools and back-office systems where downtime has real business consequences. Multi-AZ deployment, automated backups, observability primed before launch, and deploys that roll back cleanly when something goes wrong.
What Danubio has shipped in AWS.
Multi-tenant SaaS scaling, real-time data pipelines, AI product infrastructure, and platform rebuilds. The work is concentrated in PropTech, where Danubio has delivered repeatedly for enterprise clients.
AI-powered property search, shipped to 400,000 agents in five months
Inside Real Estate committed publicly to launching an AI-powered home search across BoldTrail and CORE Home in five months. Promising models existed; the production search platform around them did not. Danubio became the engineering team behind the launch.
Real-time performance analytics dashboard handling 20k req/s
Inside Real Estate launched Vitals, a daily performance dashboard for every brokerage on BoldTrail. Danubio designed and built the real-time event tracking, scoring, and aggregation service behind it, end-to-end.
Rebuilding a live multi-tenant platform without downtime
CORE Home was live with pilot brokerages when Danubio took ownership of the stack. The rebuild migrated tenants one at a time onto a stronger foundation, with no cutover and no broken clients. The same platform now carries 5,000+ tenants across web and mobile.
Scaling an acquired SaaS to 350K enterprise users
Inside Real Estate acquired dashCMA in 2020 and kept Danubio on as the engineering team. Five years and 1M+ presentations later, the product is used across major national brokerages, including RE/MAX, eXp, and Berkshire Hathaway HomeServices.
From a founder’s vision to acquisition in 18 months
How a founder-led PropTech product moved from first build to acquisition, with Danubio acting as the engineering team behind the product.
What we use across AWS, end to end.
The services Danubio actually runs in production. Managed-service choices track current best practice, and older estates get rationalized as part of the work.
Compute and runtime
- EC2 with Auto Scaling
- Lambda and EventBridge
- ECS and EKS
- Fargate for containers
- Step Functions for workflows
Data and storage
- Aurora and RDS Postgres
- DynamoDB
- S3 with lifecycle policies
- ElastiCache (Redis)
- OpenSearch
- MSK for managed Kafka
Network, edge, security
- VPC with private subnets
- ALB and NLB
- CloudFront and Route 53
- API Gateway
- IAM, KMS, Secrets Manager
- WAF and Shield
Observability and IaC
- CloudWatch metrics and logs
- X-Ray tracing
- CloudTrail audit
- AWS CDK and Terraform
- Systems Manager and Config
The way Danubio approaches AWS work.
Principles that shape every AWS engagement, drawn from years of running production workloads where the bill, the blast radius, and the on-call rotation are real.
- 01
Senior-led, every engagement.
The engineers designing AWS architecture for a Danubio client are the engineers who have shipped production AWS workloads for years. No training-on-the-job at the client's expense, and no architectures cargo-culted from a conference talk.
- 02
Architecture before services.
Pick the AWS services to fit the workload, not the workload to fit the services. Cost, latency, blast radius, and operational load drive the design before any service name appears on a diagram.
- 03
Infrastructure as code, full stop.
Every AWS resource Danubio ships lives in CDK or Terraform, reviewed in pull requests, deployed through a pipeline. No console-only changes that drift from the spec and stay invisible until the next outage.
- 04
Cost discipline from day one.
Budgets, tagging conventions, anomaly alerts, and reserved capacity where it pays. The AWS bill stays a number the business can plan around, not a quarterly surprise that triggers a panicked optimization sprint.
- 05
Observability before launch.
Logs, metrics, traces, and alarms wired before traffic arrives. The first incident in production is not the first time someone reads CloudWatch or wonders where the trace IDs are coming from.
An AWS workload on the table?
New architecture, scaling work, IaC migration off click-driven setups, security hardening, regional rollout, or cost optimization. Whatever stage the workload is at, we can talk through it.