Cloud SaaS Solutions Development
Build cloud-native SaaS on AWS/GCP — serverless architecture, microservices, auto-scaling infrastructure, CDN delivery, multi-region failover, and enterprise-grade observability. Infrastructure that scales with your ambition.
All Systems
Infrastructure Status
The Problem
Why Monolithic Apps Fail at Scale
Monolithic SaaS applications work at 100 users. They become a liability at 10,000. Here is what teams discover when their architecture cannot keep up.
A single component failure brings down the entire application — there are no blast-radius boundaries between services
Scaling the application means scaling the entire monolith even when only one endpoint is under load, wasting 80% of compute spend
Deployment requires a full application restart — even a typo fix causes 3–5 minutes of downtime
Database bottlenecks emerge when a single RDS instance handles OLTP queries and reporting analytics simultaneously
Teams step on each other's changes — a bug in the billing service breaks the user authentication module in the same codebase
Infrastructure is impossible to replicate consistently across environments, causing production-only bugs that cannot be reproduced locally
Solution
Cloud-Native Capabilities We Deliver
Serverless-First Architecture
AWS Lambda and API Gateway for compute that scales to zero when idle and to millions of requests under load — with no servers to provision, patch, or manage. Pay only for what you use, with sub-millisecond cold-start optimization built in.
Microservices with API Gateway
Domain-driven microservice decomposition with a managed API gateway handling authentication, rate limiting, request routing, and canary deployments. Service mesh for inter-service communication with circuit-breaking and retries.
Auto-Scaling (0 → 10,000 req/s)
Horizontal pod autoscaling on ECS or EKS with predictive scaling policies informed by historical traffic patterns. Load testing to 10× peak capacity validates that auto-scaling kicks in before users notice degradation.
CDN & Edge Caching
CloudFront distribution with cache behaviour rules per route type. Static assets cached at 200+ edge locations. API responses cached at the edge for read-heavy endpoints. Cache invalidation strategies aligned to your data freshness requirements.
Multi-Region Disaster Recovery
Active-passive or active-active multi-region architecture with automated failover. RDS Multi-AZ with read replicas in secondary regions. S3 cross-region replication for durable object storage. RTO under 15 minutes for all critical services.
CI/CD with Zero-Downtime Deploys
GitHub Actions pipelines with blue/green or canary deployment strategies eliminate deployment downtime. Automated rollback triggers on elevated error rate within the first 5 minutes post-deployment. Feature flags decouple deployment from release.
Observability (Datadog / New Relic)
Distributed tracing, custom metrics, and structured logging piped to Datadog or New Relic. SLO dashboards, anomaly detection alerts, and on-call runbooks ensure your team is notified before users report an issue.
Infrastructure as Code (Terraform)
Every cloud resource defined in Terraform — VPCs, security groups, Lambda functions, RDS clusters, and CloudFront distributions. Version-controlled infrastructure enables reproducible environments and safe change management.
Delivery Process
From Architecture to Production
Architecture Design & Cloud Strategy
We assess your current architecture, define the target-state cloud design, select the right AWS services for each workload, and produce an Architecture Decision Record before any provisioning begins.
Infrastructure Setup (Terraform)
VPC, networking, security groups, IAM roles, and base AWS services provisioned via Terraform. Development, staging, and production environments created with identical configurations from day one.
Service Development & Containerisation
Microservices developed in parallel sprints. Each service containerised with Docker, published to ECR, and deployed to ECS or EKS with health checks, resource limits, and autoscaling policies.
Load Testing & Resilience Validation
k6 or Gatling load tests simulate 10× projected peak traffic. Chaos engineering exercises validate auto-scaling, circuit-breaker, and failover behaviour before the first user sees the product.
Production Launch & SRE Handover
Go-live with Datadog dashboards active, PagerDuty alerts configured, and SLOs defined. SRE runbooks handed to your operations team with 30-day hypercare support included.
Tech Stack
Technologies We Deploy
Results
Infrastructure Performance Delivered
70%
Infrastructure Cost Reduction
Serverless architecture and right-sized auto-scaling eliminate over-provisioned fixed capacity that traditional deployments require.
Auto-scales
to 10× Traffic Spikes
Predictive and reactive scaling policies absorb sudden traffic spikes without pre-warming or manual intervention.
Sub-100ms
Global Latency
CloudFront edge caching and multi-region read replicas deliver fast response times to users worldwide.
Have a project in mind?
Get a free technical consultation from our senior engineering team.
Related Services
Build on Cloud-Native Foundations
FAQ
Cloud SaaS Architecture Questions
When should we choose serverless over containers for our SaaS?
Serverless (Lambda) is ideal for event-driven workloads, APIs with variable traffic, and batch jobs where cost efficiency at low utilisation matters. Containers (ECS/EKS) are better for long-running processes, stateful services, GPU workloads, or services with strict cold-start latency requirements. Most SaaS products benefit from a hybrid: serverless for APIs and event processors, containers for background workers and ML inference.
How do you achieve zero-downtime deployments?
We implement blue/green deployments on ECS where a complete copy of the new version is spun up before any traffic is shifted. Load balancer target group weights are shifted gradually — 10% → 50% → 100% — with automated rollback triggered if the error rate exceeds a configurable threshold within the first five minutes. Feature flags allow code to be deployed to 100% of infrastructure while features are released to 0% of users.
What does multi-region disaster recovery actually look like?
For most SaaS products, we implement active-passive multi-region: primary region handles all traffic, secondary region maintains a warm standby with an RDS read replica promoted to primary on failover. Route 53 health checks trigger automatic DNS failover when the primary region fails. RTO is typically under 15 minutes. For financial or healthcare SaaS requiring near-zero RTO, we architect active-active with DynamoDB Global Tables or Aurora Global Database.
How do you keep cloud costs predictable as we scale?
We implement a FinOps discipline from day one: AWS Cost Anomaly Detection alerts for unexpected spend increases, Reserved Instance and Savings Plan commitments for baseline compute, auto-scaling policies that scale down aggressively during off-peak hours, and monthly cloud cost reviews with recommendations. Our architecture reviews have reduced client cloud bills by 40–70% compared to their previous lift-and-shift deployments.
Can you migrate an existing monolithic application to cloud-native architecture?
Yes. We use the Strangler Fig pattern to incrementally extract capabilities from a monolith into independent services without a risky big-bang rewrite. Typically we start with the highest-traffic or most independently deployable capability, extract it to a Lambda or container service, route traffic through an API gateway, and iterate. The monolith shrinks gradually while production continues running on the existing system throughout the migration.
Ready to Start Your Project?
Schedule a free 30-minute strategy session with our senior engineers. No sales pitch — just honest technical advice on your specific challenge.