Regulated fintech API
- Challenge
- Node 20 upgrade needed without maintenance window.
- Simplileap solution
- ALB weighted canary with connection draining.
- Outcome
- Full cutover; zero failed transactions.
// Scale
Auto-Scaling & Load Balancing
Traffic is unpredictable. Auto-scaling and load balancing ensure your application handles spikes automatically, eliminates single points of failure, and right-sizes resources for actual load, optimising both reliability and cost.
// Key benefits
What makes this service valuable
Horizontal and vertical scaling
Auto Scaling Groups (AWS), Managed Instance Groups (GCP), and Kubernetes HPA, configured with appropriate scaling policies based on your application's traffic patterns and latency requirements.
Load balancer architecture
ALB, NLB, or Nginx, configured for your application type with health checks, sticky sessions where required, TLS termination, and rate limiting.
Cost-efficiency by design
Scaling policies that scale in aggressively during low-traffic periods and scale out ahead of traffic growth, minimising compute costs without impacting availability.
// Details
Infrastructure that handles whatever traffic throws at it
Auto-scaling eliminates the manual ops work of capacity management and the risk of traffic spikes causing downtime. When configured correctly, it is invisible, your application just keeps working regardless of load.
Load balancing distributes traffic across multiple instances, eliminating single points of failure and enabling zero-downtime deployments through rolling updates.
// What this includes
- Auto Scaling Group or Kubernetes HPA configuration
- Scaling policy design (CPU, memory, custom metrics)
- Load balancer setup and health checks
- TLS termination and certificate management
- Target tracking and predictive scaling
- Multi-AZ deployment for high availability
- Load testing and scaling verification
// Deliverables
What you receive
Every engagement produces clear, documented deliverables. Here is exactly what is included in our auto-scaling & load balancing service.
- 01Auto-scaling configuration (as code)
- 02Load balancer setup and documentation
- 03Scaling policy documentation
- 04Load test results and scaling behaviour report
- 05High availability architecture diagram
// In practice
How auto-scaling & load balancing engagements run
AWS ALB/NLB or GCP load balancers sit behind autoscaling groups with target-tracking on request count or CPU — tuned so scale-up precedes saturation by 2–3 minutes. Health checks hit application /health, not just TCP open ports. Load tests validate scale-out at 2× expected peak; cost caps prevent runaway instance counts.
// Stack & frameworks
Stack we use for this
Cloud
- AWS / GCP
- Auto-scaling
- Reserved instances
- Cost dashboards
DevOps
- GitHub Actions
- Terraform IaC
- Docker
- Blue-green deploys
Observability
- Datadog / Grafana
- Error tracking
- Uptime monitors
- Executive reporting
// Delivery
Simplileap execution framework
01
Architecture mapping
Dependencies, API contracts, compliance constraints, and performance budgets documented before sprint one.
02
Secure sprints
Two-week increments with GitHub access, demo recordings, and QA checkpoints, client visibility at every stage.
03
QA & handover
Automated tests on critical paths, security review, runbooks, and knowledge transfer to your team.
// Proof
Real deployments from Bangalore
// Engagement models
How teams engage us
Currency
| Package | Ideal for | Investment | Includes |
|---|---|---|---|
| Managed services AMC | Post-launch platforms | ₹25K – ₹1.5L / month |
|
| DevOps transformation | Engineering teams | Project + retainer |
|
| SLO-backed operations | Revenue-critical apps | Scoped within AMC range |
|
// Company and service positioning
Company and Service positioning is reviewed for production delivery standards by Harsha Parthasarathy (Co-Founder, Strategy & Operations 24+ years IT veteran, IBM, Global Delivery, Program Management) and Keshav Sharma (Co-Founder, Engineering and Lead Architect, Full-stack engineering, product delivery and technical standards).
CIN
AAU-8582
Startup India
DIPP83124
Founded
November 2020
Office
Residency Rd, Bengaluru, India
// FAQ
Common questions about auto-scaling & load balancing
What triggers auto-scaling?+
CPU utilisation and memory are common triggers, but application-specific metrics are often more accurate, request queue depth, active connections, or custom CloudWatch metrics. We design scaling triggers based on your application's actual behaviour.
Ready to get started with auto-scaling & load balancing?
Share your requirements with our team. We respond within one business day with a clear plan from discovery to delivery.