Proline Technologies Reinvents Customer Experience with a Cloud-Native Self-Service Platform on AWS
CLIENT
Proline Technologies
YEAR
2026
Overview
Proline Technologies, a leading Internet Service Provider, faced critical operational bottlenecks during monthly subscription renewal windows. By partnering with us to migrate from a legacy
server-based architecture to an AWS Serverless solution, Proline achieved 99.99% uptime during peak traffic, automated their provisioning workflows, and reduced infrastructure costs by
eliminating idle resources.
The Challenge
As an ISP, Proline experiences a Thundering Herd traffic pattern. On the 1st and 30th of every month, thousands of customers attempt to renew subscriptions simultaneously.
Operational Instability
Their previous SQL-based architecture struggled to handle concurrent writes, leading to database locks, timeouts, and failed transactions.
Poor User Experience
Their previous SQL-based architecture struggled to handle customers faced long loading times (>5 seconds) to check data balances or renew plans.
High Fixed Costs
To handle the peak usage (which only lasts 3 days), Proline had to pay for large servers that sat 90% idle during the rest of the month.
The Solution: Serverless & Event-Driven
We architected a cloud-native solution utilizing AWS Lambda, Amazon API Gateway, and
Amazon DynamoDB. The core design philosophy was “Queue-Based Load Leveling” to
decouple the user interface from the backend processing.
Amazon DynamoDB
Replaced the rigid SQL database to handle high-velocity
reads/writes with single-digit millisecond latency.
AWS Lambda
Serverless compute to handle logic without managing servers.
Amazon SQS (Simple Queue Service)
Acts as a buffer to absorb traffic spikes during renewal bursts.
DynamoDB Streams & Amazon SNS
Automates customer notifications (SMS) only after service is successfully restored.
Architecture Workflow
Ingestion
When a customer renews a plan via the mobile app, API Gateway triggers a lightweight Lambda function.
Buffering
Instead of writing to the database directly (which could cause bottlenecks), the Lambda pushes the request to an SQS Queue and immediately confirms receipt to the user.
Notification
Once the database is updated, DynamoDB Streams detects the change and triggers an SNS alert to notify the user: Your service is active.
Processing
A background Lambda worker pulls requests from the queue at a controlled rate and updates DynamoDB.
Key Results & Metrics
Legacy System vs AWS Serverless Transformation
Peak Uptime
BEFORE (LEGACY)
92% (Frequent Timeouts)
AFTER (AWS SERVERLESS)
99.99% (Zero throttling)
Cost Model
BEFORE (LEGACY)
Fixed (High Monthly Bill)
AFTER (AWS SERVERLESS)
Variable (Pay-per-request)
Support Tickets
BEFORE (LEGACY)
High volume regarding failed transactions
AFTER (AWS SERVERLESS)
Reduced by 85%
Operational Efficiency: The switch to AWS Graviton2 processors for the background
Lambda functions improved processing speed by ~20% while lowering costs. Additionally,
utilizing DynamoDB TTL (Time-to-Live) saved storage costs by automatically pruning raw debug logs after 90 days without writing custom scripts.
Lessons Learned
Asynchronous is Key: For user-facing transactions that involve heavy writing, decoupling the UI from the database using SQS ensures the app never “freezes,” even under massive load.
Provisioned Concurrency: For the “Check Balance” feature, we learned that standard Lambda functions had slight “cold start” delays. Implementing Provisioned Concurrency smoothed this out, ensuring instant responses.
Let Discuss,Your Needs
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