Success Stories / Cloud & Devops Services for Real Estate Product

Cloud & Devops Services for Real Estate Product

A fast-growing real estate technology company faced challenges scaling its monolithic application, managing infrastructure manually, and delivering updates reliably across multiple environments. These limitations resulted in delayed deployments, inconsistent user experience, and mounting operational overhead.

DevOps AutomationPropTechSaaS

17 min read

2021

Executive Summary

Matoffo partnered with the client to modernize their cloud infrastructure on AWS, implementing a robust architecture centered around Amazon ECS, Aurora, CloudFront, and other managed services. The solution featured infrastructure as code, automated scaling, and centralized observability – alongside a secure perimeter using AWS WAF and ElastiCache for optimized performance.

By transitioning to a containerized architecture with automated deployments, the client reduced provisioning time by over 80%, improved platform availability to 99.9%, and decreased page load latency for end users. The new setup also cut cloud maintenance costs and empowered development teams to iterate faster – delivering a scalable, secure foundation for future growth.

Client Background

The client is an innovative proptech company focused on transforming how real estate transactions are conducted. Their flagship platform streamlines the complex and often fragmented process of property sales by bringing together all key participants – buyers, sellers, agents, and legal professionals – into a single, secure digital environment.

Designed to accelerate deal cycles and improve transparency, the platform enables users to access real-time transaction details, communicate seamlessly, and share all necessary closing documentation, all in one place. As the product gained traction and user adoption surged, the client required a more scalable, automated, and secure infrastructure to support growing demand and ensure a frictionless experience for its expanding user base.

Customer Challenge

As the client’s real estate platform rapidly expanded to support a growing user base and increasing transaction volume, the underlying infrastructure and deployment processes struggled to keep up. The team faced growing complexity in managing multi-environment deployments, infrastructure consistency, and application delivery. Manual workflows, fragmented toolsets, and the absence of scalable CI/CD automation introduced risk, slowed development velocity, and increased operational overhead.

Key Business Challenges:

High Deployment Overhead:

Updating services across multiple environments required significant manual effort, increasing release cycle time and demanding constant developer intervention.

Inconsistent Environments:

Infrastructure was manually replicated across staging, testing, and production, leading to configuration drift, unpredictable behavior, and delayed incident recovery.

Limited Delivery Automation:

Without a fully automated CI/CD pipeline, new releases were error-prone and difficult to validate, often resulting in regressions or downtime during feature rollouts.

Scalability Constraints:

As usage and data volume grew, the team struggled to scale infrastructure efficiently - risking performance bottlenecks and resource waste in peak traffic periods.

These challenges not only slowed the client’s ability to release and scale, but also placed an increasing operational burden on a lean engineering team – threatening both user experience and long-term product agility.

Goals and Requirements

In response to the infrastructure inconsistencies, manual deployment burdens, and lack of scalable automation, the client set out to modernize its delivery pipeline and cloud architecture. The primary objective was to implement a robust, multi-environment DevOps foundation that would enable faster, more reliable releases while reducing operational risk and cost. These goals were designed to improve deployment velocity, enforce environment consistency, and ensure long-term infrastructure scalability.

Performance Targets

Reduce Release Time:
Cut deployment duration from several hours to under 20 minutes by implementing fully automated CI/CD pipelines across all environments.
Increase Deployment Accuracy:
Eliminate environment drift and misconfigurations by adopting infrastructure-as-code and standardized environment provisioning.
Boost Developer Efficiency:
Allow engineers to focus on feature development by reducing their involvement in infrastructure maintenance and manual release steps.

Financial Targets

Lower Operational Costs:
Automate repetitive infrastructure tasks and reduce release-related downtime – targeting annual savings of approximately $15,000.
Optimize Cloud Usage:
Implement autoscaling and resource tagging strategies to minimize over-provisioning and manage compute costs efficiently.

Scalability & Reliability

Enable Growth at Scale:
Ensure the platform can support additional services and environments without increasing the DevOps workload or introducing complexity.
Ensure Configuration Consistency:
Manage all infrastructure components through version-controlled templates to guarantee repeatability across dev, staging, and production.
Improve Audit Readiness and Security:
Introduce logging, policy automation, and least-privilege access controls to maintain traceability and compliance with security standards.

By setting these goals, the client aimed not only to fix its immediate operational inefficiencies but to build a scalable, automated platform architecture capable of supporting accelerated growth and continuous innovation.

The Solution

To address the client’s infrastructure limitations and manual deployment bottlenecks, Matoffo designed and implemented a fully automated, multi-tier AWS architecture supported by infrastructure as code, container orchestration, and CI/CD automation. The solution was tailored to meet the client’s scalability, performance, and operational efficiency goals—using Amazon ECS, Aurora, and other managed services to power a highly available, secure, and observable platform.

1
Infrastructure Design & VPC Setup
A multi-AZ VPC architecture was created with public and private subnets, NAT gateways, bastion hosts, and isolated layers for compute, data, and security. This formed the foundation for hosting production and non-production environments with high availability and network segmentation.
2
CI/CD and Infrastructure Automation with Terraform
Using Terraform, Matoffo codified all infrastructure components including ECS clusters, subnets, database instances, and load balancers. GitLab CI was configured to trigger Terraform plans and apply workflows automatically, storing state securely in Amazon S3 for consistent environment management.
3
Application Containerization and Deployment on ECS
The client's real estate platform, including services such as Nginx, PHP, and other custom microservices, was dockerized and deployed to Amazon ECS. Container instances were distributed across zones and managed by Auto Scaling Groups to ensure both cost efficiency and resilience under load.
4
Search and Performance Optimization
Amazon Elasticsearch Service was integrated to support fast, scalable catalog search functionality. ElastiCache for Redis was deployed to reduce latency for frequently accessed data and minimize read pressure on Aurora.
5
Front-End Acceleration and Global Access
Static and dynamic content was accelerated through Amazon CloudFront, with domain management handled by Amazon Route 53. AWS WAF was configured to provide an additional layer of security against common web-based threats.
6
Monitoring, Security, and Logging
Comprehensive observability was achieved using built-in CloudWatch metrics, ECS service logging, and audit-ready Terraform output.

Results and Impact

The implementation of a containerized, fully automated AWS infrastructure significantly improved the client’s operational efficiency, deployment speed, and scalability. By standardizing infrastructure with Terraform, containerizing the real estate platform on ECS, and introducing CI/CD pipelines with GitLab, the client transitioned from manual, error-prone workflows to a resilient, cloud-native architecture that supports long-term growth.

Quantitative Outcomes

90% reduction in deployment time, cutting release cycles from over two hours to under 20 minutes through GitLab CI automation
~$20,000 annual savings, driven by reduced manual provisioning, lower incident recovery effort, and improved engineer focus
30–35% decrease in infrastructure cost, enabled by right-sized ECS instances, use of Auto Scaling Groups, and Redis caching
99.9% application uptime, achieved through multi-AZ ECS deployment and Aurora high availability

Qualitative Outcomes

Improved developer velocity, as teams now deploy independently and focus on application logic instead of infrastructure concerns
High environment consistency, with staging, QA, and production environments managed from a single Terraform/IaC source
Stronger security and compliance, through network isolation, AWS WAF, IAM scoping, and fully auditable infrastructure changes
Faster scaling and new feature rollouts, allowing the platform to handle usage spikes and onboarding of new clients without delays

This transformation empowered the client with a secure, efficient, and highly adaptable platform – unlocking new opportunities for product delivery, operational savings, and market expansion.

Key Learnings

The most impactful elements that led to the success of this engagement – and can be replicated in similar projects—were:

Infrastructure as Code with Terraform
By managing all environments through Terraform and GitLab CI, the client achieved full consistency across development, staging, and production. This eliminated manual configuration errors, accelerated provisioning, and enabled fast, repeatable deployments.
Containerization with Amazon ECS
Moving the application to ECS allowed the team to decouple services from underlying infrastructure, improve scalability, and reduce management complexity. Combined with autoscaling and multi-AZ support, it created a resilient, cost-efficient platform ready for rapid growth.
GitOps-Driven CI/CD with GitLab
Implementing GitLab CI as the core automation engine brought structure and transparency to the release process. Developers now push code with confidence, triggering automatic infrastructure provisioning, deployments, and environment updates – reducing cycle time and operational risk.

Next Steps

With a stable and scalable AWS-based infrastructure in place, the client is now positioned to build on this foundation and further optimize operations, expand platform capabilities, and explore new strategic opportunities. Key next steps include:

1
Implement Blue–Green or Canary Deployments
To reduce release risk and improve user experience during updates, the client plans to introduce advanced deployment strategies such as blue–green or canary rollouts using ECS and Application Load Balancer routing rules.
2
Platform-as-a-Service Potential
With infrastructure now modular and replicable via Terraform, the client is evaluating the opportunity to productize the solution—offering white-labeled platform instances to other real estate firms or SaaS providers through a managed service model.

These initiatives will help the client drive continuous optimization, ensure proactive infrastructure governance, and unlock new revenue streams by turning internal capabilities into externally offered services.

Conclusion

This transformation marked a critical turning point in the client’s technology journey – shifting from manual, fragmented deployments to a fully automated, cloud-native DevOps ecosystem built on AWS. By embracing containerization, Infrastructure as Code, and continuous delivery, the company achieved faster releases, stronger security, and greater resilience across its real estate platform.

Beyond immediate technical gains, the engagement unlocked long-term business value: lower operational costs, improved time to market, and the agility to scale seamlessly as user demand grows. With a modern DevOps foundation now in place, the client is empowered to deliver innovation faster, onboard new customers with confidence, and explore new service models that extend the impact of this transformation far beyond its original scope.

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