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General Insurance / BFSI · Managed Cloud Operations · AWS

How a leading Indian insurer kept processing SLAs under five minutes while cutting container costs by up to 70%

A leading Indian general insurance company runs a documentation platform on Amazon ECS, where unpredictable traffic spikes made it difficult to balance processing SLAs against infrastructure cost. After four new microservices increased workload variability, fixed scaling patterns led to either under-provisioning or unnecessary container capacity. Flentas designed a custom, queue-based autoscaling framework that scales ECS services according to actual workload demand and now operates it as a managed service, sustaining sub-five-minute processing SLAs, zero downtime, and up to 70% lower container runtime costs.

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A leading Indian insurerGeneral insurance / BFSIDocumentation platform on Amazon ECS4 new microservices added
At a Glance
Up to 70%Reduction in container runtime costs
<5 minutesProcessing SLA sustained under variable load
ZeroDowntime during production rollout
The Challenge

The platform was scaling on the wrong signal

As new microservices were introduced, workload patterns became harder to predict. The existing scaling approach couldn't respond directly to the metric that mattered most: the number of messages waiting to be processed.

No native queue-depth autoscaling

ECS did not natively scale services based on Amazon SQS queue depth, creating a trade-off between SLA risk and over-provisioning.

Cost from excess capacity

Keeping additional tasks running to handle potential spikes meant paying for compute that wasn't always being used.

SLA risk during spikes

Scaling too conservatively could leave insufficient tasks available when queue volumes increased.

Ongoing tuning required

Scaling thresholds and operational parameters needed continuous adjustment as workload patterns evolved.

The Approach

Custom, event-driven autoscaling operated as a managed service

Flentas built a queue-depth-based autoscaling framework that connects workload demand to ECS scaling actions, then took on its ongoing monitoring and optimization.

  1. 01

    Ran the documentation platform on Amazon ECS with Fargate Spot, using Amazon SQS to buffer incoming workloads and support cost-efficient container processing.

  2. 02

    Monitored queue depth using Amazon CloudWatch Alarms, with Amazon SNS acting as the event layer between queue conditions and scaling actions.

  3. 03

    Used AWS Lambda to execute idempotent scaling operations on ECS services, dynamically adjusting task capacity based on messages waiting to be processed.

  4. 04

    Continues to manage and tune the framework, monitoring application health, adjusting scaling thresholds, and responding to incidents as workload patterns change.

Business Outcome

Lower container costs without compromising processing SLAs

Up to 70% reduction in container runtime costs

Enabled by Fargate Spot and workload-driven scaling.

Sub-five-minute processing SLAs sustained

Even as workload volumes fluctuated.

60% reduction in ECS over-provisioning

By scaling capacity according to actual queue demand.

40% fewer on-call alerts

With manual scaling interventions eliminated.

Zero-downtime production rollout

With the autoscaling framework deployed within six hours.

Continuous optimization

With scaling thresholds and infrastructure performance managed as part of ongoing AWS operations.

Technologies & Partners

Built on an event-driven AWS serverless stack

Amazon ECS (Fargate Spot)Amazon SQSAmazon CloudWatch AlarmsAmazon SNSAWS Lambda
Common Questions

What insurers and high-throughput platforms usually ask about managed autoscaling

Related

Where this fits

Insurance

For life, health, and general insurers, growth depends on launching products faster, settling claims sooner, and proving control to the regulator every quarter. Flentas helps you migrate, secure, and run the cloud behind policy administration, underwriting, and claims, so you can modernize the core without disrupting the book. From a legacy policy platform to an AI assisted claims workflow, we build the foundation that lets insurers move quickly while policyholder data stays protected and auditable. Regulated. Resilient. AI ready.

Explore Insurance
Get Started

Ready for autoscaling that responds to the workload that actually matters?

Talk to an AWS-certified architect about your own scaling framework.

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